matplotlib datetime rounding bug

Matplotlib_datetime_rounding_bug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5WZLXd/d/VNWKJwUAAOPcYBEAAObPXL01r6qHJLlmkvuMFpliDQDA+QirAQBg\nZ5ibt+tVdb0MN228fXf/eGnx6B8AALuUsBoAAHau/b6Fr6oLJHlokjd191e2vqRV3TbJZZOcsKxz\nyMFJ7lBVD0tyse4+e/k37N2795yvFxYWsrCwsC2FAgCwNYTVAAAw/xYXF7O4uLiubdd1E8eqOiPJ\nDbr7i5srbeNGN2+88vJFSV6V5H+SPLO7Txzb3k0cAQDmmBssAgDA7jCJmzi+P8nNk0wtwO7u7yX5\n3vJlo2D9O+PhNQAA88XMagAAYCXrffv/siR/VlVXT/KhJD9YvrK7Pzzpwtap40aOAABzRVgNAACs\n13pbiOxbY3V398GTK2kytBABAJg+bUAAAID9WauFyHoD7EPWWt/dJ22ksK0kwAYA2F7CagAAYCM2\nHWDPIwE2AMDWEVYDAACTMpEAu6ruluQRSa6ZZE93f7mqHpLk8939bxOrdkIE2AAAkyGsBgAAttJa\nAfa6fsWoqvsmeWmSVyT52SQXHK06OMkTksxcgA0AwIFzg0UAAGCWrLcH9seTPKu7/66qTk9yk+7+\nfFXdNMnbu/tyW13ogTIDGwBgbWZWAwAAs2DTM7CTXDvJe1dY/v0kl9xoYQAAbA8zqwEAgHm03l9P\nvprkekm+OLb8Dkk+N9GKAADYFGE1AACwU6z3V5aXJXlBVT04SSW5WlXdMclzkuzdotoAANgPYTUA\nALCTrasHdpJU1TOSPDrJRUaLfpTkud391C2qbVP0wAYAdho9qwEAgJ1orR7Y6w6wRzu6WJJDkxyU\n5MTuPn0yJU6eABsAmGfCagAAYLeYZID9E0muNXr4ue7+4QTq2xICbABgXgirAQCA3WzTAXZVXSTJ\nnyR5WJILjRaflaE39hO6+8wJ1ToxAmwAYBYJqwEAAM5rEgH20Un2JHlikvePFt8mybOTvLO7j5hQ\nrRMjwAYApk1YDQAAsH+TCLBPT3Kv7n772PK7JHljd19iIpVOkAAbANhOwmoAAICNWSvAXu+vTz9I\ncvIKy7+S5IyNFgYAMI/2F1bv2ZMceaSwGgAAYLPWOwP7qCQ3SnJEd58xWnbRJEcnOaG7/2hLq9wA\nM7ABgEkwsxoAAGBrbaiFSFX9U5KllZXkZ5L8b5KPjx7fKMMM7sXu/qVJF71ZAmwA4EAJqwEAALbf\nRgPsYzIE2LXsvytpN3EEAOaNsBoAAGA2bPomjvNIgA0ALBFWAwAAzC4BNgCwawirAQAA5sumA+yq\nunSSpyVeFz+2AAAgAElEQVT52SSXS3LQstXd3ZebRKGTJMAGgJ1PWA0AADD/JhFgvznJTyV5dZJv\n5NybOyZDgP3SSRQ6SQJsANhZhNUAAAA70yQC7NOTLHT38ZMubqsIsAFgfgmrAQAAdo+1Auz1/sr3\nhZy3bQgAwETsL6zesyc58khhNQAAwG603hnYd05yVJLHJPlEd/94qwtbpY5HJHlokkNGi05I8sfd\n/dYVtjUDGwBmjJnVAAAAjJtEC5ErJzk2yW1XWN3dffDmSlyfqvqlJD9K8pkMM8IfmOQJSW7Z3R8b\n21aADQBTJKwGAABgPSYRYL8ryaWTvCTnv4ljuvvvJ1DnhlTVt5I8qbtfPrZcgA0A20RYDQAAwEZN\nIsA+I8mtu/sTky5uo6rq4CS/luSVSQ7r7k+PrRdgA8AWEFYDAAAwSZO4ieOnklxyciVtXFXdKMn7\nklw4yQ+T/Pp4eA0ATIYbLAIAADBN652BfXiSvUmemuTjSc5evr67v70Vxa1SywWTXDXJpTLMwP69\nJHfq7g+NbWcGNgAcADOrAQAAmIZJtBDZt8bqbbuJ40qq6h1JTu7uI8aW99Oe9rRzHi8sLGRhYWGb\nqwOA2SSsBgAAYFoWFxezuLh4zuM//MM/3HSAvbDW+u5eXGv9Vqqq45J8ubsfMLbcDGwAiLAaAACA\n2bbpGdizoqqeneQtSU5Ocokk90nyhCSHd/c7xrYVYAOw6wirAQAAmDeTaCFy2Frru/vDG6ztgFTV\nq5LcKckVknwvyceSPGc8vB5tK8AGYEcTVgMAALAT7Oge2KsRYAOwkwirAQAA2KkmEWAfMrbogklu\nmuSoJEd291s3WePECbABmFfCagAAAHaTLeuBXVV7kjytu396wzvZIgJsAOaBsBoAAIDdbisD7Osk\n+Vh3X3TDO9kiAmwAZo2wGgAAAM5vEi1ELjO+KMmVkuxNcs3uvtlmi5w0ATYA0ySsBgAAgPXZyps4\nfjnJvbv7fZuob0sIsAHYLsJqAAAA2LhJBNgLY4v2JTk1yWe7++xNV7gFBNgAbAVhNQAAAEzWlvXA\nnmUCbAA2S1gNAAAAW2/DAfYKva9X1N3f3mBtW0aADcCBEFYDAADAdGwmwF6t9/Vy3d0Hb7S4rSLA\nBmA1wmoAAACYHZsJsBdWWdVJDk/yqCRnd/clN1vkpAmwAUiE1QAAADDrJtoDu6oOS/KnSe6Q5GVJ\nnt7dp266ygkTYAPsPsJqAAAAmD8TCbCr6ppJnpHk15K8IcmTu/tzE6tywgTYADubsBoAAAB2hk0F\n2FV12SRPTfLwJO9J8sTu/uDEq5wwATbAziGsBgAAgJ1rMz2wj0ry+CQnJXlSd79tSyrcAgJsgPkk\nrAYAAIDdZTMB9r4kZyb59yT7Mty8cXxH3d2/NKFaJ0aADTD7hNUAAADAZgLsYzKE1sn5g+sl3d1H\nbKrCLSDABpgtwmoAAABgJRO5ieO8EWADTI+wGgAAAFgvATYAW0ZYDQAAAGyGABuAiRBWAwAAAJMm\nwAbggAmrAQAAgO0gwAZgTcJqAAAAYFoE2ACcQ1gNAAAAzBIBNsAuJawGAAAAZp0AG2AXEFYDAAAA\n82jHBNhVdWSSeya5bpIfJXl/kiO7+4QVthVgAzuWsBoAAADYKXZSgP0vSf4uyQeTHJTk6Ulum+TQ\n7v7O2LYCbGBHEFYDAAAAO9mOCbDHVdXFknwvyS939z+PrRNgA3NHWA0AAADsNjs5wL5ikq8kuX13\nv3dsnQAbmGnCagAAAICdHWAfm+RaSW4xnlYLsIFZIqwGAAAAWNmODLCr6nlJfj3D7OuTVlgvwAam\nQlgNAAAAsH5rBdhzGZ1U1fMzhNd3Wim8XrJ3795zvl5YWMjCwsJWlwbsMiuF1V/5SnLjGw8h9Z49\nyZFHCqsBAAAAliwuLmZxcXFd287dDOyqekGSX8sQXn96je3MwAYman9htZnVAAAAAAdux7QQqaoX\nJblfkl9J8sllq07v7h+MbSvABjZMWA0AAACwPXZSgL0vSScZP5m93f30sW0F2MC6CKsBAAAApmfH\nBNgHQoANrERYDQAAADBbBNjAriSsBgAAAJh9AmxgxxNWAwAAAMwnATawowirAQAAAHYOATYwt4TV\nAAAAADubABuYC8JqAAAAgN1HgA3MHGE1AAAAAIkAG5gyYTUAAAAAqxFgA9tGWA0AAADAgRBgA1tC\nWA0AAADAZgmwgU0TVgMAAACwFQTYwAERVgMAAACwXQTYwKqE1QAAAABMkwAbSCKsBgAAAGD2CLBh\nFxJWAwAAADAPBNiwwwmrAQAAAJhXAmzYQYTVAAAAAOwkAmyYU8JqAAAAAHY6ATbMAWE1AAAAALuR\nABtmjLAaAAAAAAYCbJgiYTUAAAAArE6ADdtEWA0AAAAAB0aADVtAWA0AAAAAmyfAhk0SVgMAAADA\n1hBgwwEQVgMAAADA9tlRAXZV3THJ45IcluRKSY7o7levsJ0Am/0SVgMAAADAdK0VYM9jJHexJB9P\n8uokf51ESs267C+s3rMnOfJIYTUAAAAAzIq5m4G9XFWdnuQR3f3XK6wzA3sXM7MaAAAAAObDTpuB\nDedhZjUAAAAA7EziPOaKsBoAAAAAdo8dHfHt3bv3nK8XFhaysLAwtVqYjMc8JjnxRGE1AAAAAMyr\nxcXFLC4urmtbPbABAAAAAJiatXpgH7TdxQAAAAAAwHrMXeOFqrpYkuuMHh6U5OpVddMk3+ruL0+v\nMgAAAAAAJmnuWohU1UKS40YPO8nS1PJjuvtBy7bTQgQAAAAAYMat1UJk7gLs9RJgAwAAAADMPj2w\nAQAAAACYOwJsAAAAAABmkgAbAAAAAICZJMAGAAAAAGAmCbABAAAAAJhJAmwAAAAAAGaSABsAAAAA\ngJkkwAYAAAAAYCYJsAEAAAAAmEkCbAAAAAAAZpIAGwAAAACAmSTABgAAAABgJgmwAQAAAACYSQJs\nAAAAAABmkgAbAAAAAICZJMAGAAAAAGAmCbABAAAAAJhJAmwAAAAAAGaSABsAAAAAgJkkwAYAAAAA\nYCYJsAEAAAAAmEkCbAAAAAAAZpIAGwAAAACAmTSXAXZV/W5VfaGqflhVH6qq20+7JgAAAAAAJmvu\nAuyq+o0kf57kj5PcNMl7k7ytqq461cIAAAAAAJiouQuwkzwmyau6+5Xd/enu/v0kpyT5nSnXxRZa\nXFycdglwwIxb5pWxyzwybplXxi7zyLhlHhm3zCtjd84C7Kq6UJLDkrx9bNXbk9xu+ytiu3iyMo+M\nW+aVscs8Mm6ZV8Yu88i4ZR4Zt8wrY3fOAuwkl01ycJKvjy3/RpIrbH85AAAAAABslXkLsAEAAAAA\n2CWqu6ddw7qNWoj8IMm9u/sNy5a/KMmh3X2nZcvm58QAAAAAAHax7q6Vll9guwvZjO4+q6qOT7In\nyRuWrbpLktePbbviCQMAAAAAMB/mKsAeeV6Sv6mq/0ry3iQPz9D/+iVTrQoAAAAAgImauwC7u4+t\nqp9MclSSKyb5RJK7dfeXp1sZAAAAAACTNFc9sAEAAAAA2D0OmnYBsFxV3auqLjrtOmAjquoyy76u\nqrpdVd2+qn5imnXBaqrq4Ko6qqreXFUPHy17UFV9pqo+W1XPGd1AGWaO11zmlbHLvKmqa1bVPavq\n8qPHV6qqp4zeQ9x02vXBSqrqflX1F1V1t9Hju1bVO6rqnVX1iGnXB6vxmrsyATaz5vVJTqmql1TV\nYdMuBtajqq5TVZ9J8s2qen9V/d8kxyV5d5J3JflkVV1/qkXCyvYmeXSS7yR5clU9PclzkhyT5JVJ\nHpDkD6ZVHKzEay7zythlHlXVXZOcmOTYDGP01kk+mOE9wv2TfKCqfn6KJcL5VNWjkrwiyY2TvLaq\nHprk75OclORzSZ5TVY+ZXoWwMq+5qxNgM4v+Msmdk3yoqo6vqodX1SWmXRSs4TkZ3gj9bJJPJ3nb\naPlVk1w5yaeSPHs6pcGa7pvkgd39gCSHZ7i/xCO7+xnd/awkD0vym9MsEFbgNZd5Zewyj/YmeWGS\ni2R4n/DmJP/Y3dft7usl+Yv4sJvZ8/AkD+nuhSR3y5AxPKm7H9LdD0vyu0kePMX6YDV74zV3RXpg\nM1Oqal+SK3T3N6pqIclDk9wjyf9mmJ398u5+3xRLhPOpqlOT/Fx3f2z0Yct3kyx093+O1h+W5G3d\nfflp1gnjquqHSa7X3V8aPT4ryc26+4TR40OSnNjdWjsxM7zmMq+MXeZRVZ2W4b3B56rq4CRnJrll\nd390tP66ST7Y3ZeaZp2wXFWdkeT6Y+9xD+vu/x49vkaSE7zHZdZ4zV2dGdjMrO5e7O77JLlKhk+Y\nbp3kPVX139OtDM7nwklOH319RpJ9SU5btv70JN4cMYtOSXKjJKmq6yW5QJIbLlt/aJKvT6EuWIvX\nXOaVscs8+lHOHZcXyZAhLO/XfpEkZ213UbAf30pyjSSpqqtkeI979WXrrzbaBmaN19xVCLCZed39\nre5+fnffMMkdk3xo2jXBmBOTPLSqKslvZ+gnfJ9l6++d4U+FYda8LslfV9Wrkrw9w5+uv6Cqfm90\nc5uXJnnDNAuEFXjNZV4Zu8yj9yX5k6r6mQx/uv6xJEdV1aWq6uIZJhr5/YxZ809Jjq6qpyV5U5K/\nTfL8qrp7Vf1ChpYi/zrNAmEVXnNXoYUIM2V5C5Fp1wLrNbqz9T9m+FDw+xn6rP1jki9nmF11WJLf\n6O6/n1qRsIKqukCSJye5ZYY/W39xVf1+kqckqQzj+NHd/YMplgnn4TWXeWXsMo9GNxZ9a5JDMtxI\n7O4ZerLecrTJN5PcdenP22EWVNUlk/x5Ru9xkzwxyXOTPDLDe9x/S3Lf7j51akXCCrzmrk6AzUwZ\n9b1+T3efPe1a4EBU1bWT3DTD+D2lqq6Z4QZ4leTN3f3uqRYIsIN4zWVeGbvMq6q6bHd/c/T1BZLc\nJcOHMe/p7u9OtThYp6q6WIYc7PvTrgXW4jX3/ATYAAAAAADMpAtMuwAYV1UHJfm5JLdLsnQX9q8l\neW+Sd7ZPXZhRo55UN09yhdGiryU53if8zLKqulWSR2V4zV0+dt+T5M+7+4PTqg3W4jWXeWXsMm/8\nfsY8qqqrJvmdnPc97ikZxu1LuvvL06oN1uI1d2VmYDNTqurKSd6S5MZJPpnk66NVV0hy/QwN7O/e\n3V+ZToVwflV1wSR/luQhSS6c5MejVQdnuIvwy5I8TmscZk1V/UqS1ydZzHATx6XX3Mtn+DO1hSS/\n3t3/OI36YCVec5lXxi7zyO9nzKOqun2G3tenZHiPu3SPraX3uFdIcjdtm5g1XnNXJ8BmplTVm5Jc\nMsn9u/vksXVXSfI3SU7r7l+eRn2wkqp6QZJfTfKEDG+QvjladdkMb5D+NMnfd/ejplMhrKyqTkjy\nt939zFXWH5nkft19w+2tDFbnNZd5Zewyj/x+xjyqqg8leW93//4q61+Q5HbdfcuV1sO0eM1dnQCb\nmVJV309yh+7+yCrrb5bk3d19se2tDFZXVacm+c3ufucq638uyeu6+7LbWxmsrarOTHKT7v70Kuuv\nn+Rj3X3h7a0MVuc1l3ll7DKP/H7GPKqqHya56RrvcW+Q5CPdfZHtrQzW5jV3dQdNuwAY88Mkl1lj\n/WVG28As+YmcO4tqJd8cbQOz5vNJ7rnG+l8ebQOzxGsu88rYZR75/Yx59LUkt19j/e0ytBeBWeM1\ndxVu4siseV2Sv66qxyV5e3d/K0mq6ieT7Mnwp5WvnWJ9sJJ/T/K8qrr/eC+qUQ+rP0ty3FQqg7U9\nNcnrqmoh5+2BfYUMf85+5yT3nk5psCqvucwrY5d55Pcz5tFzkvzV6Gbl4/d52ZPkgRluYg6zxmvu\nKrQQYaZU1YWT/HmSByW5YM57c5uzk7wyyaO6+6zpVAjnV1VXS/LPSQ5NcmKGN0iV4Q3SDZKckOQX\n3OmaWVRVt83wBv42OfcO7V9L8r4kL+ju902rNliJ11zmlbHLPPL7GfOqqn4jyWOSHJZhvCbD+D0+\nyfO6+9hp1Qar8Zq7OgE2M6mqLpXk5jlvmHJ8d39velXB6qrq4AyfiN425x23783wyem+adUGq6mq\nam8EmENec5lXxi7zyu9nzKuqulCGm+UmyTd3Y/DH/PGae34CbGZKVT3//7V359GWleWdx78/JhFQ\nDGOJUQZBEQcQccABQZyiCGiSVlsFNRJaXEoU1GCLpUFDK+XUtgqoaEziFFtFQBERaBA0ihBRwIGh\noJSxHJnHp//Y+1qnTt1zB6w6ex/q+1mrVt2z33Pv+tVa731r7+fs/bzAV2ma0nsCr4mQZG+ai85b\nu84izUe7odgJwPE0c3i17KemyeKaq0nl3NUk8vpMkyjJ+TTz9viq+nHXeaS5cs0dzU0c1Tf3BT4P\nXJfkM0n2TeJmNuq79wG/SfLVJK9s+1NJk+AFNI+wHwksTfK1JK9Kssks3yd1yTVXk8q5q0nk9Zkm\n0XtpWjOdkWRxkg8l2SOJNTD1nWvuCN6Brd5JEuDxwL7APsDWwKnA14ATqur6DuNJ00rycJr5ui+w\nC03/4OOBr1XVZV1mk+YiyXYsW3efAHyfZg4fX1WXdJlNGuaaq0nl3NUk8vpMkyrJ2sAeNPP2BcAG\nwIk06+7JVXVTh/GkabnmTs8CtnovybYs+8V9IvADml/czw/v4C71QZLNaU6Q9gWeAVzKsovTc7vM\nJs1Fks1o5vA+wJ7A5cBbq+qkToNJ03DN1aRy7mpSeX2mSZVkF5p5uw+wHfAd4MiqOrvTYNIMXHMb\nFrA1UZJsSnOivzdwdlUd1XEkaUZJ1geeQ/MfzvNodrz+525TSXOXZD2aOXxjVX276zzSTFxzNamc\nu5pUXp9pUiXZhqYg+Kuq+o+u80hzsTqvuRawJWlMkqwJbLS6PvIjSePkmqtJ5dyVpJVroCXDg4Er\nfMpFmjw2sNdESbJtktO6ziHNR5LNk7yjqu7yYlR9ksZhSc5NckqSFw6NL0hyV1f5pFHadfVZSTZq\nX2+ZZGGSdyV5JIBrriZFkivbfQgA5676Jck6Q6+3TfLhJN9I8sm2JYPUK0mOTPLc9utNafYb+D7w\nBeAHSc5pj0u9kuSnSQ5P8qCus/SNd2BroiTZCTivqvzwRRPDeau+SvJm4HDg48CGwP7AB6vqbe34\nAuAq5676JMmuwLdoNmL6DfB84ATgD8CawBbA07y7Sn2T5BBg+OIrwHuBDwDXAFTVB8YcTRqp/SD7\ngVV1XXtOezZwCXAesCPwSGC3qvrPDmNKy0lyFfBXVfXjJJ8BtgdeQTN3twGOA66sqld0l1JaUZK7\ngRuA9YBvAp8ATqqquzsN1gMWsNUrSRay4on9oAcCB1pMUZ8keTozz9vtgE84b9U3SX4GLKyqL7av\ndwZOAj5XVYdYwFYfJTmVZmPRQ4C/Bw4FTqyq17TjxwF/UVUvHP1TpPFrL0qvAu4YGtpy8HhVbT3m\naNJI7bxd0BawTwBuBV5cVXe3bRk+RVPg/qtOg0oDktwKbF9Vi5NcDryiqr47MP5Y4OSq2ryzkNI0\n2jV3K+BpwAHAbjTnCJ8GPlVVizsL1zEL2OqV9pf1cuDmEW+5L7B1Va05vlTSzNp5O5ty3qpvktwM\n7DB4IpTk4cAZwBeBI4GrLWCrT5L8FnhqVV3UPtp+C7BrVf2gHX8c8PWq8tFL9UqSY2h6sL60qn4+\ncPwOYKequrCzcNIIQwXsJTTzd7AQuBPwLQuB6pP2Jo03V9UJSS4D9ptm3p5ZVffvLKQ0jcE1t339\nMJpC9v7AxsCpNDfHfbm7lN3wglR9cznw9qp69HR/gL+hedRS6pOlwH7AZiP+PAPnrfppKfCQwQNt\nUWUP4CXAh7oIJc1iHdoPuqvq9vbrpQPj19Oc4Eu9UlUHAv8MfCfJgcPDHUSS5mpqft4F/HFo7I80\nbcikPjkaWNTemPER4Kgk2wIk2YbmHPfkDvNJc1JVv6iqNwN/CbyUpl3eF7tN1Q0L2Oqb84HHdh1C\nmqfzaZ4MWDrdH+D3XQeURjgbeNHwwar6Gc0HL88YeyJpdkuAwRYLL6XtHdxaQNMbW+qd9o6ppwD7\nJ/lakk26ziTNweVJbqApoOw4NLYty6/BUueq6kM0bfF+AhwI7AT8IsntNH2wNwDe0F1CaX6q6vaq\n+lJVPZOmRelqZ62uA0hDFtK0CRnlQppNF6Q+OYZmk4VRrgBePaYs0nz8L2Dn6Qba9gzPoHnyReqT\nL9Ns1AhAVZ04NL434GZi6q2quiLJbsC7gP/Cm4rUb8PnsL8cer0r8JUxZZHmrKre1LZu2oemhrAG\ncDXwXeDUsp+u+ulMVtwrYzlVddmYsvSKPbAlSZJ0r5HkfsCdVXVL11mk2STZHdgd+GhVXd9tGkmS\npH6ygC1J0mqu3RxkV5rWC9A8CnxOVQ3fZSVJklZTSdYEptreLK2qu7rMI80myQbA41j+HPdHVXVj\nd6mkuXHNXZ4FbPVOktcBTwa+XVWfSXIQ8GaaR37+FTjcx33UN0keDLyWZu5OnSBdDZwDHF1VS7rK\nJo2SZEOadXUv4CbgunZoM2B94ATgFVU1vGGT1CnXXE0q564mUZIXAYcCu7CsDemdwA+BRVX11a6y\nSdNJsjbwfuAA4D40G5BCswHebcCxwKFVNWOrBqkLrrnTs4CtXklyCE0/wFNo7gY8BjgYWETzn80h\nwFur6ujOQkpDkjwV+CbNBegpLCsCbg48i+YC9XlV9d1uEkrTS/JZmh7YB9LccV3t8bBsDT6/qvbr\nLqW0PNdcTSrnriZRkgOBjwD/QjNvr22Hpubt/sDrq+rYbhJKK0ryYZp9XN5CM2+XtkOb0Mzb9wFf\nrqp/6CahND3X3NEsYKtXklwMvLuq/j3JY4FzgQOr6pPt+N8Br62qXbrMKQ1Kci5N8W/anazbE6gn\nV9Xjx5tMmlmS3wPPrarvjxjfFTi5qjYcbzJpNNdcTSrnriZRkkuBI6eux6YZfw1wWFU9dLzJpNGS\nXA+8tKpOHTH+TOALVbXJdONSV1xzR3PHa/XNljS7AlNV59Psvvq9gfEzgW07yCXN5JHAR2cYPxp4\n9JiySPM10yfZfsqtPnLN1aRy7moSbQGcNcP42cCDxpRFmqv7suyu6+ksbd8j9Y1r7ggWsNU3NwPr\nDbxeCgxvsLAWUr9cAzx1hvEn0zwuLPXNCcAn2zutl9MeOxb4+thTSTNzzdWkcu5qEl0EHDTD+N8D\nF44pizRXpwMfSLJCoa899n7gtLGnkmbnmjuChUD1zS9o7jy5GKCq/nJo/GHA4jFnkmZzFPDxJE9g\nxT5VzwZeCdhfTX30BuBzwNlJbgSub49vCmwAnAy8vqNs0iiuuZpUzl1NojcB30jyXJaftwto+rE+\nCHheR9mkUV4HnARcmeQimnkbmvX2ETQFwOd3F08ayTV3BHtgq1eSPB24oarOGzH+emCNqvrweJNJ\nM0vyYpr/bHam2XAUmt2ufwR8oKq+1FU2aTZJHkGzaeOC9tA1wPeq6uLuUkmjueZqUjl3NYmSbA38\nD5pzhc3bw9fQtHo8uqoWdxRNGinJmjQfDg6f454DnFJVd3eVTZqJa+70LGBL0kqUZB2a3a0BllbV\n7V3mkaR7M9dcTSrnriRJ0txZwJYkSZIkSZIk9ZKbOEqSJEmSJEmSeskCtiRJkiRJkiSplyxgS5Ik\nSZIkSZJ6yQK2JEkijccneXGSDdpjGyRZu+tskiRJ0nwlOS7J/aY5vn6S47rIJOmecRNH9VaSJwF7\nApuy7MOWAFVVb+gsmDSDJOsDOwKbMfQhYVV9pZNQ0iySbA4cDzwBKGC7qrosyTHArVV1cKcBpVaS\n3eb63qo6c1Vmke6pJDvPNF5V540rizSTJG8CPl5VtyQ5hOYcYVpV9YHxJZPmJsndwIKqum7o+KbA\nNVW1ZjfJpNlZE1veWl0HkKaT5FDgfcAlwFUsO1kKM5w4SV1K8kzgC8BGI97iUy/qqw8C1wEbA1cO\nHP8P4P90kkia3hlzfF8BXpSqr86dYcy5qz55PfAvwC3t1zNdh1nAVm8k2YimdgCwUZI7B4bXBPYC\nrh17MGmOrImtyAK2+upg4A1VZeFEk+TDwInA24Cry0dcNDn2BPasqt8lGTx+GfCQbiJJ09ps4Osn\nAouAdwPfb489CfifwFvGnEuaj22GXq8N7AS8HThs/HGk6VXV1gNfb9VhFGm+lg58fdE04wUsHFMW\n6Z6wJjbEArb66v7AN7oOIc3TVsDeVXVV10GkebovcMc0xzcBbh1zFmmkqvrTBWmSI4CDq+qUgbdc\nmuQ6mjtWThx3PmkuqmrxNId/meQPNAUVz4HVK0nWAc4C9quqn3edR5qDZ7R/nwb8NfC7gbHbgSuq\n6tdjTyXNnTWxIRaw1VdfAJ4LfKzrINI8nANsD1zadRBpns4CXsnAnX9J1gLeCnyno0zSbB4B/Gqa\n479ux6RJcznw2K5DSMOq6vYkW7OaPrauyVNVZwAk2Qa4sqru7jaRNG/WxIZYwFZfXQn8U5KnABcw\ndGegm4Sopz4OLEqyBdPPWzdlUl+9GTgzyeOB+9C0ZXgUsCHwlC6DSTO4CFiY5FVVdTNAkvWAdwAX\ndppMmkHbm3W5Q8AWwDsB725VX30WOIDmnEGaCFNPvLTXZw8B1hkad8Nn9ZU1sSGxRav6KMnigZcr\nTNLBfmxSX7S7XI9S7nKtPkvyQOC1wONoiinnAR+tqqs7DSaN0H7gchJN/+Af08zbRwN3AntV1Q86\njCeNNMP5whLgJVX1vXHmkeYiyceAl9Psj/Ej4KapIZrz3Dd0lU0apS1cfx542jTDXp+pt6yJrcgC\ntiStJEm2mml8RM9LSdI9lGQD4L+zrGXIRcDnquqm0d8ldSvJ7kOH7gauBy6pqun2I5A6l+SMgZeD\nRYSpAvYe400kzS7Jl2j2dDkI+CFNS4bNgSOANw7toyGpxyxgS5K0mkuy80zjtr+RJGn1lOQxwIVV\ndTpBSsAAAAr+SURBVFfXWaT5SnItzVNZP0zyR2CXqvpFkucDh1fVkzqOKGmO7IGtXkryEWbYJMRH\n1NRXSXYEDgV2oJnDFwKLquonnQaTZnbuDGMF+HileinJ84DXAdsAz66qJUkOAC6rKjcgVW8lWR/Y\nEdgMWGNwrKq+0kkoaXr/BSwArgNIchLwGluMaULcl+YJF4Df0qy5vwAuplmDpV6yJrYiC9jqq0ez\n/C/rOsD2NEWU8ztJJM0iyd7AV4CzgG/QPFL5VOD8JC+qqq93mU+awTZDr9cGdgLeDhw2/jjS7JK8\nDDgG+CSwJ828heZc4S2ABWz1UpJnAl8AhjdznLLGiONSH+xGUxSUJsHPaeoIi2n2y3htkiU0LUV+\n3WEuaTbWxIbYQkQTI8m6wHHAmVV1dNd5pGFJLgC+WlULh47/E7BPVfkpvyZKkmcDC6vqKV1nkYa1\na+6RVfX5JDcAO1bVZUl2Ak6pqs06jihNK8mFNL1Y3wZcXV6QqcfaTUcXVNXUHdh/Wm+7TSbNLsnL\ngbWr6tNty7xvARsDtwH7V9WXOg0ozcPqXhOzgK2JkuSRwMlV9eCus0jDktwKPKqqLhk6/jDgJ1V1\nn26SSfdMku2AH1fVel1nkYYluRl4RFVdMVTA3hb4aVWt23FEaVpJbgIeU1WXdp1Fmo0FbN2btO2b\ntgeurKrrZ3u/1Derc03MFiKaNJsA9+s6hDTC9cAuwCVDx3cGrh1/HGlukgw/xh5gC+CdNI9eSn10\nFfBw4Iqh408DLAyqz86hKaA4TzUp/jXJbTTnB+sCxya5ZWC8qmrvbqJJ83Ib8POqurHrINI9tNrW\nxCxgq5eSHMLy/X6miikvo+ktLPXRscAx7d1/Z7fHnkqzqeNRnaWSRkhyHPAPwNIRb1kCvGR8iaR5\nORb4cJLX0JwnPCTJbjTr7Tu7DCYNax9dn/JxYFGSLYALgDsG31tV540zmzSLz9Jcl6V9/e/TvMfH\nutUr7V4DGw22CElyGM35wVpJTgVeXFW/7yiiNCNrYiuyhYh6Kclilv9lvZvm7tbTaPpd3tBFLmkm\nSUJTDDwUeGB7+CqaYsr/tsel+mbqsWBgh6GhqTX3l1V159iDSXPQrrnvBt5Ic0cgNHdWLaqqwzsL\nJk2jXW/noqpqzVUaRpLu5doC9Ter6v3t6ycA3wc+BVxMs9nzv1XVod2llEazJrYiC9iStAokuT9A\nVf2x6yzSKMN9LaVJkGQP4Oyqur19vT7NhzBrABetjif06r8kW7H8XawjVdXiVRxHku7VklwD7FVV\n57avjwKePLUxeZK/Bd5TVQ/rMKakebCFiCZCkrWAde1VpUlh4VqSVpnvALcm+R5wOs2dKP9ZVXd1\nG0saraoWt22bDvZDFkla5R7A8nsQPQX45sDrc4EHjTWR9GewJuYd2OqZ2XpVAfaqUu8kOYHZ76py\ncxv1zhwfafdxdvVKu8/AHu2f3Wna4NwMnEVTzD4d+JFtm9Q3PvUiSeOR5HLg1VV1epL7AL8HXlBV\np7bjjwHOqKrhjcylTlkTG807sNU3/8jAJ6Ntr6r3sHyvqrfT9BiW+uL5wJXAGYwuZFtIUV8dAPyh\n6xDSXFXVJcAlwCcAkmzPsmL2IcB7aeb0X3QUUZIkdeubwHuT/COwD8s+6J7yaJpzCalvrImNYAFb\nffMoml/YKX8LfK+qDgBIsoTml3e1+2VVrx0F7AfsBhwHfKaqftVtJGnOTvBuQE2yqvpZkt8Bv6Up\nXL8EWL/bVJIkqUMLgf9Lc7fqjcArq+q2gfG/A77dRTBpFtbERrCFiHolya3AdlW1pH19Ds3uwUe0\nr7cGflpVXpiqV9qeVM8HXg08h+YR9uOAr1XVHV1mk0bxcXZNqiSb0NxxPdVK5KHAj2iehPl/wHer\n6qau8knTsW2TJI1XkgcAN1bVnUPHNwZumNoQWuoLa2KjeQe2+uZqYFtgSdur6rHAOwbG7wfcNt03\nSl1qT4qOB45PsgDYHzgC+FiSrVfnzRYkaWVK8hOac4WpgvXBwDkWrDUhbNskSWMyqk9wVf1m3Fmk\nObImNoIFbPWNvap0b7A+sCHNfy43dJxFGqmq1ug6g3QPPBT4HXA5cBlwqcVrTRDbNkmSpFGsiY3g\nhav6ZiFwK02vqlcBB9irSpMgyXpJXpnkTOAnwJbAflW1jXdfS9JK9QDgxcAvgZcDFya5Mslnk7w6\nyTbdxpMkSZLuEWtiI9gDW71krypNkiSfBP4bTTHlU8DnRj2uJklauZKsC+wKPJ2mH/YTgWurastO\ng0lD3HdAkiTNhTWxFVnAlqQ/U3tBugS4oD00uLBm6lhV7T3WYJK0GkiyNvBklm3quCuwti1yJEmS\npHsHe2BL0p/vs0xftB7kp4WStBIkWYvmLus9WFawXhe4Ajid5kmY0zsLKEmSJGml8g5sSZIkTYwk\nNwLrAVfRFKrPAE6rqsu7zCVJkiRp1bCALUmSpImR5ECagvUvu84iSZIkadWzgC1JkiRJkiRJ6iU3\nt5EkSZIkSZIk9ZIFbEmSJEmSJElSL1nAliRJkiRJkiT1kgVsSZIkSZIkSVIvWcCWJEmSJEmSJPWS\nBWxJkiRJkiRJUi9ZwJYkSZIkSZIk9ZIFbEmSJEmSJElSL1nAliRJkiRJkiT1kgVsSZIkSZIkSVIv\nWcCWJEmSJEmSJPWSBWxJkiRJkiRJUi9ZwJYkSZIkSZIk9ZIFbEmSJEmSJElSL1nAliRJkiRJkiT1\nkgVsSZIkSZIkSVIvWcCWJEmSJEmSJPWSBWxJkiRJkiRJUi9ZwJYkSZIkSZIk9ZIFbEmSJEmSJElS\nL1nAliRJksYoyWeS3N3+uT3JtUlOS3JQkrXm8XN2b3/GRqsyryRJktQlC9iSJEnSeBXwbWABsCXw\nLOAE4F3AWUnWm+fPy8qNJ0mSJPWHBWxJkiRpvALcXlXXVdXVVXVBVX0Q2B3YGXgLQJKXJ/lhkj+2\nd2l/KckW7dhWwGntz7u+vRP7uHYsSd6S5JIkNye5IMnLxvxvlCRJklYKC9iSJElSD1TVhcDJwF+3\nh9YGDgceA+wFbAJ8vh27cuB9O9DczX1w+/rdwKuAg4BHAEcCxyR53ir+J0iSJEkr3Zx77EmSJEla\n5S4GnglQVZ8eOL44yUHARUm2qKqrkvyuHbuuqn4LkGR94I3As6rq7Hb8iiRPBF4HfGMs/wpJkiRp\nJbGALUmSJPVHgLsBkuwMLAR2BDZiWa/rhwBXjfj+HYB1gW8lqYHjawOXr4rAkiRJ0qpkAVuSJEnq\njx2Ay9qNHL8FnAK8HLgO2BQ4C1hnhu+fahG4F02bkUF3rNyokiRJ0qpnAVuSJEkavxo+kORRwHOA\nI2h6V28MvK2qrhgYH3R7+/eaA8cuAm4DtqqqM1ZyZkmSJGnsLGBLkiRJ47duks1pis+bAnsChwHn\nAouADWgK0a9P8jGagvYRQz/jCppC+F5JTgRurqobkiwCFiUJzR3bGwBPAu6qqk+s+n+aJEmStPKs\nMftbJEmSJK1ERbNR49U0RehTaVp+LAR2q6pbqup6YH9gX+BC4HCazRn/dOd2Vf26/Z73ANcAH2mP\nHw68EzgU+ClNG5IXApet+n+aJEmStHKlaoWnFyVJkiRJkiRJ6px3YEuSJEmSJEmSeskCtiRJkiRJ\nkiSplyxgS5IkSZIkSZJ6yQK2JEmSJEmSJKmXLGBLkiRJkiRJknrJArYkSZIkSZIkqZcsYEuSJEmS\nJEmSeskCtiRJkiRJkiSplyxgS5IkSZIkSZJ66f8DlFPEFGdh9NIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4386e309d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[736176.6666666666, 736177.6666666666, 736178.6666666666, 736179.6666666666, 736180.6666666666, 736181.6666666666, 736182.6666666666, 736183.6666666666]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import dates as mdates\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "hkt = pytz.timezone('Asia/Hong_Kong')\n",
    "\n",
    "# Labels\n",
    "plt_title = 'Number of matplotlib bugs found per day'\n",
    "x_label = 'Date'\n",
    "y_label = 'Number of bugs'\n",
    "\n",
    "# Data preparation\n",
    "x_series = [\n",
    "    datetime(2016, 8, x, tzinfo=hkt)\n",
    "    for x in xrange(1, 8)\n",
    "]\n",
    "\n",
    "y_series = range(1, 8)\n",
    "\n",
    "x_min = mdates.datestr2num(\"2016-07-31\")\n",
    "x_max = mdates.datestr2num(\"2016-08-08\")\n",
    "y_min = 0\n",
    "y_max = 8\n",
    "\n",
    "# Plot Preparation\n",
    "fig, plot = plt.subplots(figsize=(25, 5))\n",
    "plt.plot(x_series, y_series)\n",
    "locator = mdates.HourLocator(0, tz=hkt)\n",
    "plot.xaxis.set_major_locator(locator)\n",
    "plot.xaxis.set_major_formatter(mdates.DateFormatter('%a %d/%m'))\n",
    "plt.xlim([x_min, x_max])\n",
    "plt.ylim([y_min, y_max])\n",
    "\n",
    "# General Cosmetics\n",
    "plt.setp(plot.get_xticklabels(), rotation=90, fontsize=14)\n",
    "plt.setp(plot.get_yticklabels(), fontsize=14)\n",
    "plt.title(plt_title + '\\n', fontsize=14)\n",
    "plt.xlabel('\\n' + x_label, fontsize=14)\n",
    "plt.ylabel(y_label + '\\n', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "print plot.get_xticks().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yXHd/ZaMDH6qqelySJyd5Xnf/2hrrhdsAAHAQgmwAABZtruH2qkGul+RGk4cf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\nDLclSZIkSZIkSb3z/wEG5lj5TXLuLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f437c401490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[736176.0, 736177.0, 736178.0, 736179.0, 736180.0, 736181.0, 736182.0, 736183.0, 736184.0]\n"
     ]
    }
   ],
   "source": [
    "# Drop the timezone and make them naive\n",
    "x_series_naive = [\n",
    "    x.replace(tzinfo=None)\n",
    "    for x in x_series\n",
    "]\n",
    "\n",
    "x_min = mdates.datestr2num(\"2016-07-31\")\n",
    "x_max = mdates.datestr2num(\"2016-08-08\")\n",
    "y_min = 0\n",
    "y_max = 8\n",
    "\n",
    "# Plot Preparation\n",
    "fig, plot = plt.subplots(figsize=(25, 5))\n",
    "plt.plot(x_series_naive, y_series)\n",
    "\n",
    "# DON'T specify a timezone here either\n",
    "locator = mdates.HourLocator(0)\n",
    "plot.xaxis.set_major_locator(locator)\n",
    "plot.xaxis.set_major_formatter(mdates.DateFormatter('%a %d/%m'))\n",
    "plt.xlim([x_min, x_max])\n",
    "plt.ylim([y_min, y_max])\n",
    "\n",
    "# General Cosmetics\n",
    "plt.setp(plot.get_xticklabels(), rotation=90, fontsize=14)\n",
    "plt.setp(plot.get_yticklabels(), fontsize=14)\n",
    "plt.title(plt_title + '\\n', fontsize=14)\n",
    "plt.xlabel('\\n' + x_label, fontsize=14)\n",
    "plt.ylabel(y_label + '\\n', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "print plot.get_xticks().tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}