Week11_21_Matplotlib_Decoration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add some decoration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two options below of template plots to build upon: a tree and a snowman.\n",
    "\n",
    "As a group, decorate an image (or multiple images) and when completed you can save this figure to file (uncomment the appropriate cell to do so). This can then be downloaded from Noteable and uploaded to the Padlet with your group name:\n",
    "\n",
    "https://uob.padlet.org/racheltunnicliffe1/agfyg4uqs6tay423\n",
    "\n",
    "*Password: brisc*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decorate a tree\n",
    "\n",
    "The code below create the outline of a tree which you can add your decoration to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.5)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O4XtGRo+vfVxt8TbbUYBAooQAeeh3a3+nk10nbccIhJauFmZDAEsoIUCe2ffpPk3cMpHl2XPEyGjcunH6tPNT21GAwKGEAHnmvsr7bEcInPZEu55Y/4TtGEDgUEKAPLL64Got+WgJ61fkWMqk9PyW59XY1mg7ChAolBAgTxhjdNe7d3FKriWJVEJjl4+1HQMIFEoIkCcWf7hY249u55RcS1zj6vfv/157mvbYjgIEBiUEyAPJVFL3V96vEL+SVoWdsH698te2YwCBwSsekAd+X/d7HWg+oJQ4I8ampEnq1V2vquZIje0oQCBQQgDLOhOdemjlQ7Zj4C/CTlgPrnzQdgwgECghgGWTt07WsfZjtmPgL1zjatm+ZdpYv9F2FMD3KCGARe3xdo1bP05GxnYUfEbYCevnS3/OgnFAllFCAIumV09XS1eL7Rg4i2tcbWvYpnnvz7MdBfA1SghgSVu8Tb9b9ztmQfKUI0cPLH9A7fF221EA36KEAJZM2TpFzV3NtmOgF0ZGxzuOa/zG8bajAL5FCQEsaIu36ckNTzILkudSJqUnNjyho21HbUcBfIkSAlgweetkZkE8IuEm9Nt1v7UdA/AlSgiQYx2JDmZBPMQ1rqZXT9fhlsO2owC+QwkBcuyF2hc4I8ZjjDF6asNTtmMAvkMJAXIomUoyC+JBrnE1a9ssNbY12o4C+AolBMihxbsW65PoJ7ZjoB/clKtnNz1rOwbgK5QQIEeMMapYX6GQw6+dF7nG1eSqyXyUBmQQr4ZAjqw5tEbvH32fpcA9rDPRqVnbZtmOAfgGJQTIkfEbxyvshG3HwAAYGU3YNEHJVNJ2FMAXKCFADuxt2qt39r4j17i2o2CAGtoatOTDJbZjAL5ACQFyYOKWiRwL4hMhJ6QJmybYjgH4Aq+KQJad7DypF2pfYBbEJ1Impc1/3qzqI9W2owCeRwkBsmxu3VzFkjHbMZBBkVBE06qn2Y4BeB4lBMgiN+Vq4uaJLE7mM8lUUgt2LFA0FrUdBfA0SgiQRW/vfVv10XrbMZAFsWRM87fPtx0D8DRKCJBFk7dO5rRcH5u5babtCICnUUKALNnbtFeV+ys5INWnjIzqGuv0fuP7tqMAnkUJAbJkRs0MZkF8LhKKaHLVZNsxAM+ihABZEEvGNKd2DrMgPpdMJfXy9pfVGmu1HQXwpLRKyLRp03TDDTeotLRUpaWlGjlypJYuXZqtbIBnvbbrNTV3NduOgRzoSnbpjQ/fsB0D8KS0SsiVV16pJ554QtXV1aqurtZ3v/td/ehHP9LOnTuzlQ/wpImbWSE1KEJOSFOqptiOAXiSY4wZ0AIGw4YN09NPP62f/vSnfdo+Go2qrKxMLS0tKi0tHchTA3mp5kiNbpp1k+0YyLGDdx3UVRddZTsGkDXZeP/u959qrutq0aJFam9v18iRI3vdLhaLKRqNnnED/GxK1RRFQhHbMZBDISekhR8stB0D8Jy0S8iOHTs0dOhQFRUV6Y477tCSJUv05S9/udftKyoqVFZW1n0rLy8fUGAgn7V0tWjhjoVc6j1gUialaVXTNMCJZSBw0i4h1157rerq6rR582b97Gc/06hRo7Rr165etx87dqxaWlq6b/X1rB4J/5q3fZ5iLteJCaLD0cNaf3i97RiApwz4mJDvfe97+uIXv6gZM2b0aXuOCYFfGWP0pSlf0u6m3bajwIJIKKJRN47S7H+dbTsKkBV5dUzIacYYxWL85QfUNdZRQAIsmUrqtV2vKeEmbEcBPCOto+cefPBB3XrrrSovL1dra6sWLVqk1atX6913381WPsAzXnr/JUWciJKG40GCKhqL6u29b+u2626zHQXwhLRKyNGjR/WTn/xEDQ0NKisr0w033KB3331X//zP/5ytfIAndCY69ULdCxSQgAs7YU3eOpkSAvRRWiVkzpw52coBeNorO19RNMbp50HnGlcrDqzQ3qa9umb4NbbjAHmPJR2BDJi0ZRIrpELSqdmQ6dXTbccAPIFXTWCAth/drtrGWqVMynYU5AHXuJpbN5cDVIE+oIQAAzSzZqYiDiuk4v872XVS7+x9x3YMIO9RQoAB6Ep26aX3X+KAVJwh7IQ1aesk2zGAvEcJAQZg8a7Fao232o6BPOMaVysPrNRHJz6yHQXIa5QQYAAmbpnIAanoUdgJa1bNLNsxgLzGqyfQT7UNtao+Us0BqeiRa1zNrp2tzkSn7ShA3qKEAP00t26uIiEOSEXvorGoFuxYYDsGkLcoIUA/xJIxvbz9ZSVTHJCK3jlyNHHLRA3wOqGAb1FCgH5Y8tESnew6aTsG8pyR0QfHPtDWP2+1HQXIS5QQoB+mVk3lgFT0ScSJaGrVVNsxgLzEqyiQpkPNh7Tu8DoOSEWfJE1SCz5YoGPtx2xHAfIOJQRI07zt8xR2wrZjwENSJsX1ZIAeUEKANKRMSrO3zZZrXNtR4CEpk9LzW59X3I3bjgLkFUoIkIZ3P35Xh1oO2Y4BDzrRcUKv7XzNdgwgr1BCgDRM3DyRj2LQLyEnpKc3Ps3pusBnUEKAPtrbtFeV+yv5KAb9kjIpvX/0fa0+uNp2FCBvUEKAPpq0dRKzIBiQsBNWxfoK2zGAvEEJAfqguatZc7bNYRYEA+IaV5X7K/V+4/u2owB5gRIC9MGsmlmKuTHbMeADESfCbAjwF5QQ4ALiblwTNk9gcTJkRNIk9erOV/Xh8Q9tRwGso4QAF7Bwx0I1tjXajgEfCTthPbL6EdsxAOsoIcB5pExK49aPkyPHdhT4SNIk9dqu17T96HbbUQCrKCHAeby1+y3tadojI9Z2QGZFQhH9ctkvbccArKKEAL0wxug3a3/D1XKRFclUUpX7K7XqwCrbUQBreHUFerHq4Cpta9jGAanImrAT1gPLH2AVVQQWJQToxSOrHmFxMmSVa1xVHanSn/b8yXYUwApKCNCDDYc3aH39ehYnQ9aFnJDGrBgjN8W/NQQPJQTowcOrHmYWBDmRMintOr5L87bPsx0FyDlKCHCWDYc3aOXBlcyCIGccObqv8j61xlptRwFyihICnOXh1cyCILeMjD7t/FRPbXjKdhQgpyghwGesOrBKKw8wC4LcS5mUntzwpA42H7QdBcgZSgjwF8YYjV0xllkQWJMyKf165a9txwByhhIC/MUfd/9RW/68hVkQWOMaV/N3zNeOoztsRwFyghIC6NTqlQ8sf4DVUWFdyAnp3mX32o4B5ASvuICkl95/SXua9rA6KqxzjavK/ZVasX+F7ShA1lFCEHhxN66HVz1sOwbQLeyE9T/v/I8SbsJ2FCCrKCEIvMlbJ+tI6xHbMYBurnG1t2mvJm+dbDsKkFWUEARaU0eTHln9iIy4gBjyi5HRI6sf0cnOk7ajAFlDCUGg/W7d79SZ6LQdA+hRe6Jdz2x6xnYMIGsoIQis+pZ6Td46mVNykbdSJqUJmyaoqaPJdhQgKyghCKz7Ku/jbBjkvbgb15MbnrQdA8gKSggCacfRHXp156vMgiDvucbVpC2TdLTtqO0oQMZRQhBID618SOEQy7PDG5KppB5d86jtGEDGUUIQOOsOrdNbe95SMpW0HQXoE9e4mlkzU3ua9tiOAmQUJQSBYozRvcvu5SJ18JyQQnps9WO2YwAZRQlBoLy15y1VHaniWBB4TtIktfCDhfrg2Ae2owAZQwlBYLgpV/dX3s9F6uBZYSesx9c8bjsGkDG8GiMwXt/1unY37ea0XHhW0iT12q7XtPvEbttRgIyghCAQ3JSrh1c/zCwIPC8cCuu3a39rOwaQEbwiIxAWfrBQe5r2MAsCz0umklrwwQIdOHnAdhRgwCgh8L24G9cDyx+QI8d2FCAjHDl6euPTtmMAA0YJge/NqpmlhtYGrpQL33CNq9nbZquxrdF2FGBAKCHwtdZYqx5e/TAFBL6TMik9v+V52zGAAaGEwNee3/q8mruabccAMs41rp7f+rza4m22owD9RgmBb53oOKFx68ZxMCp8qy3ephfrXrQdA+i3tEpIRUWFvvrVr6qkpESXXHKJbrvtNu3ezfnqyE/j1o1TV7LLdgwgq8ZvHC83xQrA8Ka0SsiaNWs0evRobd68WZWVlUomk7rlllvU3t6erXxAvxxvP66pVVNZnh2+ZmR0qOWQ3trzlu0oQL9E0tn43XffPePruXPn6pJLLlFNTY2+9a1vZTQYMBATNk3gKrkIhLAT1sTNE3XbdbfZjgKkbUDHhLS0tEiShg0b1us2sVhM0Wj0jBuQTSc7T2rS1knMgiAQXONqzaE12nlsp+0oQNr6XUKMMbrnnnv0jW98Q9dff32v21VUVKisrKz7Vl5e3t+nBPrk+a3PcywIAiXshDVp6yTbMYC0OcaYfi2gMHr0aL399ttav369rrzyyl63i8ViisVi3V9Ho1GVl5erpaVFpaWl/XlqoFfRWFRXTrhSrfFW21GAnCoKF+nP9/xZwwcPtx0FPhWNRlVWVpbR9+9+zYT8/Oc/15tvvqlVq1adt4BIUlFRkUpLS8+4AdkyvXo66yYgkBKpBKfrwnPSKiHGGN1555164403tHLlSl199dXZygWkrSvZpac2PMXqqAiklElpStUU9XNyG7AirRIyevRovfzyy1qwYIFKSkrU2NioxsZGdXZ2Zisf0Ge/r/u9mjqbbMcArDnQfEDrDq+zHQPos7RKyLRp09TS0qLvfOc7uvzyy7tvr7zySrbyAX3iplw9sf4J2zEAqyKhiGbVzLIdA+iztNYJYZoP+eoPH/1BB1sO2o4BWJVMJbVo5yI98/1ndMmQS2zHAS6Ia8fAF57Y8IRCDv+cgZRJ6YXaF2zHAPqEV2143uZPNqv6SDUXqgN0qoTM2TaHmWt4AiUEnjdx80RFQml9sgj42scnP1ZNQ43tGMAFUULgaQ2tDXp91+tcJwb4jEgoolnbOEAV+Y8SAk+bvW0264IAZ0mmkpr3/jy1xlg5GPmNEgLPSqaSmlo9lWNBgB50Jbu0YMcC2zGA86KEwLMW71qsxrZG2zGAvDWndo7tCMB5UULgWc9seobTcoFeGBlVHanS7hO7bUcBesUrODyp+ki1qo5U8VEMcB5hJ6wZNTNsxwB6RQmBJ03aMonTcoELcI2redvnyU25tqMAPaKEwHOisahe2/kap+UCfXCi44RWHFhhOwbQI0oIPOfVna8q5sZsxwA8IeJENKVqiu0YQI8oIfAcPuMG+i5pknpr91s61HzIdhTgHJQQeMr2o9tVfaSaBcqANIScEKfrIi9RQuAp06unc0AqkCbXuJpePV0JN2E7CnAGSgg8I5aMaf6O+RyQCvTD8Y7jenvv27ZjAGeghMAzln68VNFY1HYMwJPCTlhz6+bajgGcgRICz1i4Y6EiDh/FAP3hGldv7X5L9S31tqMA3Sgh8ISmjiYt+WiJkoaPYoD+Cjkhzd4223YMoBslBJ4wb/s8jgUBBsg1rmbXzuZyB8gblBDkPWOMplVNsx0D8IUjrUdUua/SdgxAEiUEHlDXWKc9n+5hbRAgA8JOWJO2TLIdA5BECYEHzNs+j7VBgAxxjaulHy9lBVXkBUoI8pqbclkbBMiwkBPiejLIC5QQ5LVl+5bpWPsx2zEAXzm9gmp7vN12FAQcJQR5bWrVVIWdsO0YgO+0xlu18IOFtmMg4CghyFsNrQ165+N35BrXdhTAdxw5enbzszKGA75hDyUEeYtFlYDsMTLadXyXVh9cbTsKAowSgrzkplxNrZ7KokpAFoWdsMZvHG87BgKMEoK89O7H76qxrdF2DMDXXOPqnY/f0a7ju2xHQUBRQpCXJm+dzAGpQA5EQhE9teEp2zEQUJQQ5J0DJw/ovX3vcUAqkAPJVFLzd8zXJ9FPbEdBAFFCkHemV09XyOGfJpArxhiODYEVvNIjr8TduGZum8ksCJBDrnE1o3qGTnScsB0FAUMJQV55fdfrau5qth0DCJx4Kq6JmyfajoGAoYQgbxhj9PTGp/koBrAgZVKauHmiTnaetB0FAcKrPfLG5k82q66xjrVBAEs6k50cG4KcooQgb4zfNF6RUMR2DCCwUialCZsm6GjbUdtREBCUEOSF/Sf3a8mHS5RMJW1HAQItkUrot+t+azsGAoISgrwwflcHCvgAABScSURBVON4jgUB8oBrXE2vnq69TXttR0EA8KoP6462HdWc2jmclgvkCyM9uPJB2ykQAJQQWPfMpmfkpiggQL5ImqRe3/W66hrrbEeBz1FCYNWJjhN6fuvzzIIAeSbiRPTI6kdsx4DPUUJg1TMbn1HcjduOAeAsSZPUm7vfVG1Dre0o8DFKCKw52nZUEzdPZF0QIE+FnbDuq7zPdgz4GCUE1jy98WklUgnbMQD0wjWuVhxYoaV7l9qOAp+ihMCK4+3HNaVqCseCAHku5IR077J7mbFEVlBCYMXTG59WwmUWBMh3KZPShyc+1Pzt821HgQ9RQpBzjW2NmrRlErMggEc4cvTA8gfUleyyHQU+QwlBzj225jGWZwc8xMiosa1Rk7dOth0FPkMJQU59dOIjzaiewSwI4DFGRo+uflQNrQ22o8BHKCHIqQdXPKhwKGw7BoB+6Ep26ZeVv7QdAz5CCUHObP5ks5Z8xJVyAa9yjauFOxZq57GdtqPAJyghyAljjO5bdp/CDrMggJeFQ2E9tPIh2zHgE5QQ5MSqg6u0vn49x4IAHpdMJfXH3X9U9ZFq21HgA5QQZJ0xRmOWj2EWBPCJSCiih1c9bDsGfCDtErJ27Vr98Ic/1BVXXCHHcfSHP/whG7ngI2/uflNVR6qYBQF8IplKaunHS7WpfpPtKPC4tEtIe3u7brzxRk2ezPniuLCEm9D9y+9XyGHSDfCTsBPWnUvvlDHGdhR4WCTdO9x666269dZbs5EFPmOM0VdmfEV7mvbYjgIgw1zjalvDNr3x4Rv6P1/+P7bjwKPSLiHpisViisVi3V9Ho9FsPyXyRGNbo3YeP3Uq34jLR8jIKGVSiifjSqQSirtxJdyE2hPt6kp2KebGen0sR45CTkghJ9T9OH66oFbYCXfPFvV1/0JOSIXhQg0pGKKicJEKI4UqChepIFygSCjS/fNy5HTfxzVu92OnTEqxZEzJVLL759+V7FJXsqvH53bkKBwKy5EjIyM35crIf38FF4QK+rR/ISekonCRBhcMVmG4UAXhAhWECrr/Gw6FzxjX0z9zY4xc4yrmxtSR6FDcjasj3qEut+uCp6+HnJDCTlgpk8qLjzcdOfrVyl/ptutuY/0f9EvWS0hFRYUee+yxbD8N8tAlQy7Rpp9u0qVDLtXVF199we3jblzt8XZFY1Gd6Dihtnibmjqb1JHoUGusVdFYVE2dTWrpalFbok3t8Xa1xdvUFm9Tc1ezmrua1ZHoUHuivdc38M+WGanvb/i9ceTIcZwz3uz78phDC4aqtLhUJYUluuqiq3TJkEt0UdFFKi0q1ZDCIRpSMEQlRSUaXDBYxZFilRaVamjhUF1UfJFKCkt0UfFFKo4Uy3GcXp+jv4wxaou36XjHcZ3sPKmj7Uf1aeen3ePSGmvV8Y7jisaiqm+pV0Nbg9oTp8bifG+iZ5ciIyNjTPfPqz9OP57jOOc87oXepAtCBSopKun+uQ4tHKrhg4arvLRclw29TMMGDdOQwiG6uPhilRaVqrSoVMMHD1dJYYmKI8UaXDA44z//zkSn2hPtao21qiPRoeauZrXF29QaP/X1p52fqqG1QUfajqg11qqWWIsOtxzWsfZjaou3nffnlO6/0bOFnXD3z1o6VaqKIkX62Vd/NrCdRqA5ZgAf6DmOoyVLlui2227rdZueZkLKy8vV0tKi0tLS/j410CtjjLqSXWqNt6oz0amWWIsa2xp1rP2YWrpaut8wW7pa1JHoUFuiTbHkqVmAWDKmRCoh17hKusnuv4Q/WzYioYjCobAGRQZpUMEgFYYLNTgyWEMKh6iksKS7RJQVl3UXhouKL9Lnh3xeQwuHnpq5iBTl9GfixuPqaGiQHEdOKCQnFJIcR6GCAoULChQqKlK4sPDU9/vp9M+9Jdai9ni7mrua1RpvPaNAtsXb1BprVcyNqS3e1j0D0x5vV8w9NSvjplzF3Xh3QXHkKBQ69QYaCUVUEC44NfMTLlRxpFhFkSIVhgpVFCnSoMggXVR8kQYXDNbggsEaVDBIgyKDNKRwiIYNGqayojJdNvQylRSVKBLK+t9gORV342qLt+nTzk/VFm/rLo0nOk6c+nceb1NHouPU70asVR3JDnUmOtUWb1NnovOMWbLTs15FkSIVR4pVGCrU0KKhKg6f+nkPLRyqsqIy/d+v/F9dUXKF7V1HjkSjUZWVlWX0/Tvrv4VFRUUqKsrtCy6CzXGcU28+BYO6v3fDpTdYTGRfy759evfHP77gdqHCQkUGDVJhWZmKL75YhaWlKh4+XIVlZSooKVFRWZkiQ4ao+OKLVVBSokGf/7yKhw1TZPDgHn/uyJ3CcKGGDRqmYYOG2Y4C9Jm//hQA0KNQuG+f16ficcXjccVbWtR2+HCfH98JhRQZMkSFJSUqKClRZNAgFQwdeuo2ZIgKhg5VYVmZIoMHKzJokMJFRYoMHqzwX2ZgIoMGyQmHT91CIYUike5ZGWOMZIxMKiWTSmloebkixcX9+jkAyC9pl5C2tjZ9/PHH3V8fOHBAdXV1GjZsmL7whS9kNByAzHAi2f17w6RSSrS2KtHamtXnkaTvv/KKhl9/fdafB0D2pf3KVF1drX/6p3/q/vqee+6RJI0aNUovvvhixoIByJywjz4SNSn/nBUFBF3aJeQ73/kOi9MAHuOnjy+Ma//UVACZwTKWQABEBvnnYNFUImE7AoAMoYQAARAqLLQdIWMoIYB/UEKAAHD6eHaMF7ix3lfWBeAtlBAgABzHyfoZMrnCTAjgH5QQICBCPikhbjxuOwKADKGEAAER9sNxIY6jOBfBBHyDEgIERIEPrtXkhMOKnTxpOwaADKGEAAFRVFZmO0JGdH36qe0IADKEEgIERNHFF9uOMGAmlVKirfdL1gPwFkoIEBAFJSVSyOO/8qmUku3ttlMAyBCPvyIB6KtIcbEcx7EdY8DiObhIHoDcoIQAAVF00UW+KCGdx47ZjgAgQyghQEAUXXSRLy4+meDjGMA3KCFAQAy69FJfXIE2xWJlgG9QQoCA8MPZMRLXjgH8hBICBERhSYntCBmRSiRYuh3wCUoIEBAFQ4bYjpAxCc6QAXyBEgIERMHQobYjZAwLlgH+QAkBAqJ4+HDJB6foSpQQwC8oIUBAhAoKfPORTLKz03YEABlACQECpHj4cNsRMoISAvgDJQQIkEKfXEmXEgL4AyUECBC/HJwaj0ZtRwCQAZQQIEAKy8o8fyVdJxLh+jGAT3j71QhAWgoGD/bFRey4fgzgD5QQIEDCgwb54jRdjgkB/IESAgRIoR+OCUmllOzosJ0CQAZQQoAACRcXS8bYjjEgJpVisTLAJyghQIBEBg2SSaVsxxgwPo4B/IESAgSIH2ZCJMmlhAC+QAkBAiQyaJDtCBmR7OqyHQFABlBCgADh2jEA8gklBAiQcFGR7QgZwdkxgD9QQoAAccJh2xEygpkQwB8oIUCAhAoKbEfIiFQiYTsCgAyghAAB4peZkFQ87otTjYGgo4QAARLySQmRJDcetx0BwABRQgB4Eh/JAN5HCQECJOW6tiNkjBuL2Y4AYIAoIUCA+Ok4CmZCAO+jhAAB4qc3bpNM2o4AYIAoIUCA+OkjDD99tAQEFSUECJCUj84oYSYE8D5KCBAgCR8td+6n41uAoKKEAAHiq2NCKCGA51FCgADx1ccxxtiOAGCAKCFAgLixmBTyya89JQTwPJ+8GgHoC7erS47j2I6RGX7ZDyDAKCFAgCTa2njzBpA3KCFAgMSam33zMYbjl4+VgADjtxgIkHg06puzSsJFRbYjABggSggQIPHWVt/MhESKi21HADBAlBAgQGInT9qOkDGhggLbEQAMECUECJBEW5vtCBkTZiYE8DxKCBAgbleX7QgZExk0yHYEAANECQECJNnZaTtCZoRCnB0D+EC/founTp2qq6++WsXFxRoxYoTWrVuX6VwAMswY45sSEopEbEcAkAFpl5BXXnlFd999tx566CHV1tbqm9/8pm699VYdPnw4G/kAZIjb2emfM2MGD7YdAUAGpF1CJkyYoJ/+9Kf6r//6L33pS1/SxIkTVV5ermnTpmUjH4AM6fLRmTEFlBDAF9Ka04zH46qpqdGYMWPO+P4tt9yijRs39nifWCymWCzW/XVLS4skKRqNppsVwAA0HTqkDtft+X86zqljLBxHjiQjScacWtgsQ7MnTjjcvWS8cd0BPW4kHOY1BMix079zmbyCdVol5MSJE3JdV5deeukZ37/00kvV2NjY430qKir02GOPnfP98vLydJ4aAP6/3bulsjLbKYBAampqUlmGfv/6dXTX2VfhNMb0emXOsWPH6p577un+urm5WVdddZUOHz6csZ3wgmg0qvLyctXX16u0tNR2nJxhv9nvIGC/2e8gaGlp0Re+8AUNGzYsY4+ZVgn53Oc+p3A4fM6sx7Fjx86ZHTmtqKhIRT1c46GsrCxQg3daaWkp+x0g7HewsN/BEtT9DmXw9Pi0HqmwsFAjRoxQZWXlGd+vrKzU17/+9YyFAgAA/pf2xzH33HOPfvKTn+imm27SyJEjNXPmTB0+fFh33HFHNvIBAACfCj/66KOPpnOH66+/XsOHD9e4ceM0fvx4dXZ2at68ebrxxhv7/qThsL7zne8oErAFh9hv9jsI2G/2OwjY78zst2Myea4NAABAH3HxBQAAYAUlBAAAWEEJAQAAVlBCAACAFRkvIVOnTtXVV1+t4uJijRgxQuvWrTvv9mvWrNGIESNUXFysv/7rv9b06dMzHSkn0tnv1atXy3Gcc24fffRRDhMP3Nq1a/XDH/5QV1xxhRzH0R/+8IcL3scP453ufvtlvCsqKvTVr35VJSUluuSSS3Tbbbdp9+7dF7yf18e8P/vthzGfNm2abrjhhu4FuUaOHKmlS5ee9z5eH2sp/f32w1ifraKiQo7j6O677z7vdpkY74yWkFdeeUV33323HnroIdXW1uqb3/ymbr31Vh0+fLjH7Q8cOKB/+Zd/0Te/+U3V1tbqwQcf1C9+8QstXrw4k7GyLt39Pm337t1qaGjovl1zzTU5SpwZ7e3tuvHGGzV58uQ+be+X8U53v0/z+nivWbNGo0eP1ubNm1VZWalkMqlbbrlF7e3tvd7HD2Pen/0+zctjfuWVV+qJJ55QdXW1qqur9d3vflc/+tGPtHPnzh6398NYS+nv92leHuvPqqqq0syZM3XDDTecd7uMjbfJoH/4h38wd9xxxxnfu+6668yYMWN63P7+++8311133Rnf++///m/zta99LZOxsi7d/V61apWRZE6ePJmLeDkhySxZsuS82/hlvD+rL/vtx/E2xphjx44ZSWbNmjW9buPHMe/Lfvt1zC+++GIze/bsHv+fH8f6tPPtt5/GurW11VxzzTWmsrLSfPvb3zZ33XVXr9tmarwzNhMSj8dVU1OjW2655Yzv33LLLdq4cWOP99m0adM523//+99XdXW1EolEpqJlVX/2+7SvfOUruvzyy3XzzTdr1apV2YyZF/ww3gPht/FuaWmRpPNezMqPY96X/T7NL2Puuq4WLVqk9vZ2jRw5ssdt/DjWfdnv0/ww1qNHj9YPfvADfe9737vgtpka74yVkBMnTsh13XMuZHfppZeec8G70xobG3vcPplM6sSJE5mKllX92e/LL79cM2fO1OLFi/XGG2/o2muv1c0336y1a9fmIrI1fhjv/vDjeBtjdM899+gb3/iGrr/++l6389uY93W//TLmO3bs0NChQ1VUVKQ77rhDS5Ys0Ze//OUet/XTWKez334Z60WLFqmmpkYVFRV92j5T453x9WYdxznja2PMOd+70PY9fT/fpbPf1157ra699trur0eOHKn6+nqNHz9e3/rWt7Ka0za/jHc6/Djed955p7Zv367169dfcFs/jXlf99svY37ttdeqrq5Ozc3NWrx4sUaNGqU1a9b0+obsl7FOZ7/9MNb19fW66667tGzZMhUXF/f5fpkY74zNhHzuc59TOBw+56//Y8eOndOWTrvssst63D4SiWj48OGZipZV/dnvnnzta1/T3r17Mx0vr/hhvDPFy+P985//XG+++aZWrVqlK6+88rzb+mnM09nvnnhxzAsLC/U3f/M3uummm1RRUaEbb7xRzz33XI/b+mms09nvnnhtrGtqanTs2DGNGDFCkUhEkUhEa9as0aRJkxSJROS67jn3ydR4Z6yEFBYWasSIEaqsrDzj+5WVlfr617/e431Gjhx5zvbLli3TTTfdpIKCgkxFy6r+7HdPamtrdfnll2c6Xl7xw3hnihfH2xijO++8U2+88YZWrlypq6+++oL38cOY92e/e+LFMT+bMUaxWKzH/+eHse7N+fa7J14b65tvvlk7duxQXV1d9+2mm27S7bffrrq6OoXD4XPuk7HxTusw1gtYtGiRKSgoMHPmzDG7du0yd999txkyZIg5ePCgMcaYMWPGmJ/85Cfd2+/fv98MHjzY/O///q/ZtWuXmTNnjikoKDCvv/56JmNlXbr7/eyzz5olS5aYPXv2mA8++MCMGTPGSDKLFy+2tQv90traampra01tba2RZCZMmGBqa2vNoUOHjDH+He9099sv4/2zn/3MlJWVmdWrV5uGhobuW0dHR/c2fhzz/uy3H8Z87NixZu3atebAgQNm+/bt5sEHHzShUMgsW7bMGOPPsTYm/f32w1j35OyzY7I13hktIcYYM2XKFHPVVVeZwsJC8/d///dnnMY2atQo8+1vf/uM7VevXm2+8pWvmMLCQvNXf/VXZtq0aZmOlBPp7PeTTz5pvvjFL5ri4mJz8cUXm2984xvm7bfftpB6YE6fmnb2bdSoUcYY/453uvvtl/HuaZ8lmblz53Zv48cx789++2HM//M//7P7Ne3zn/+8ufnmm7vfiI3x51gbk/5++2Gse3J2CcnWeDvG/OVIEgAAgBzi2jEAAMAKSggAALCCEgIAAKyghAAAACsoIQAAwApKCAAAsIISAgAArKCEAAAAKyghAADACkoIAACwghICAACsoIQAAAAr/h/Bvts8NB7iswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Code to create the template ###\n",
    "# Modify this code if you wish but this creates your background template\n",
    "\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcdefaults()         # Set axes labels to be default style\n",
    "fig, ax = plt.subplots()\n",
    "plt.xkcd()               # Set xkcd (sketch style) for everything else\n",
    "\n",
    "## Define parameters for the tree\n",
    "tree_x = [1.0, 2.0, 3.0]\n",
    "tree_y = [1.0, 5.0, 1.0]\n",
    "tree_colour = \"green\"\n",
    "\n",
    "tree = list(zip(tree_x,tree_y)) # create [x,y] pairs for Polygon function\n",
    "\n",
    "## Define parameters for the tree trunk\n",
    "trunk_x = [1.8, 1.8, 2.2, 2.2]\n",
    "trunk_y = [0.0, 1.0, 1.0, 0.0]\n",
    "trunk_colour = \"brown\"\n",
    "\n",
    "trunk = list(zip(trunk_x,trunk_y)) # create [x,y] pairs for Polygon function\n",
    "\n",
    "## Plot the both aspects of the tree (using Polygon patches)\n",
    "# Set zorder to make sure any other features are on top of this\n",
    "t1 = plt.Polygon(tree, color=tree_colour,zorder=-1)\n",
    "ax.add_patch(t1)\n",
    "\n",
    "t2 = plt.Polygon(trunk, color=trunk_colour,zorder=-2)\n",
    "ax.add_patch(t2)\n",
    "\n",
    "## Limit the plotting area\n",
    "ax.set_xlim([0,4])\n",
    "ax.set_ylim([0,5.5])\n",
    "\n",
    "#### ADD ADDITIONAL FEATURES BELOW ####\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## To save the figure to file - uncomment the code below\n",
    "#fig.savefig(\"my_decorated_tree.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decorate a snowman\n",
    "\n",
    "As an alternative, the code below creates the outline of a snowman which you can add features to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 4.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Code to create the template ###\n",
    "# Modify this code if you wish but this creates your background template\n",
    "\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcdefaults()         # Set axes labels to be default style\n",
    "fig, ax = plt.subplots()\n",
    "plt.xkcd()               # Set xkcd (sketch style) for everything else\n",
    "ax.set_aspect(\"equal\")   # Make x aspect = y aspect so circles are not squashed\n",
    "\n",
    "ax.set_facecolor(\"xkcd:navy blue\") # Set colour of plotting area\n",
    "\n",
    "## Define details for the snowman\n",
    "snowman_centre = [(2.0, 0.4), (2.0, 1.4), (2.0, 2.2)]\n",
    "snowman_colour = \"white\"\n",
    "snowman_radius = [0.6, 0.5, 0.4]\n",
    "\n",
    "## Plot the snowman (using Circle patches)\n",
    "# Set zorder to make sure any other features are on top of this\n",
    "t1 = plt.Circle(snowman_centre[0], snowman_radius[0], color=snowman_colour,zorder=-3)\n",
    "t2 = plt.Circle(snowman_centre[1], snowman_radius[1], color=snowman_colour,zorder=-2)\n",
    "t3 = plt.Circle(snowman_centre[2], snowman_radius[2], color=snowman_colour,zorder=-1)\n",
    "\n",
    "ax.add_patch(t1)\n",
    "ax.add_patch(t2)\n",
    "ax.add_patch(t3)\n",
    "\n",
    "## Limit the plotting area\n",
    "ax.set_xlim([0,4])\n",
    "ax.set_ylim([0,4])\n",
    "\n",
    "#### ADD ADDITIONAL FEATURES BELOW ####\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## To save the figure to file - uncomment the code below\n",
    "#fig.savefig(\"my_decorated_snowman.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some options in matplotlib\n",
    "\n",
    "Here are some examples of some `matplotlib` options and functionality that you could use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x19b1fc69f70>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "fig2, ax2 = plt.subplots()\n",
    "plt.xkcd()\n",
    "\n",
    "## You can add points using the `scatter(x, y)` function setting:\n",
    "## the colour (`color`), point size (`s`) and shape (`marker`)\n",
    "ax2.scatter([2.0],[2.9],color=\"magenta\",marker=\"o\",s=30)\n",
    "ax2.scatter([2.5, 1.5],[2.5, 2.5],color=\"red\",marker=\"*\",s=400)\n",
    "\n",
    "\n",
    "## Or you can add lines using the `plot(x, y)` function setting:\n",
    "## the colour (`color`), line width (`linewidth`) and line style (`linestyle`)\n",
    "ax2.plot([1.2,2.8],[3.2,2.8],color=\"yellow\",linewidth=\"2\",linestyle=\"-\")\n",
    "ax2.plot([1.2,2.8],[1.9,2.1],color=\"blue\",linewidth=\"10\",linestyle=\":\")\n",
    "\n",
    "\n",
    "## Or add a patch such as a rectangle (using `plt.Rectangle` definition)\n",
    "## or `plt.Polygon()` or `plt.Circle()` as defined in previous cells.\n",
    "# plt.Rectangle((x, y), width, height, angle, color=color)\n",
    "rectangle = plt.Rectangle((1.9,2.2), width=0.2, height=0.2, color=\"#660033\")\n",
    "ax2.add_patch(rectangle)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More options\n",
    "\n",
    "Colour options:\n",
    " - https://matplotlib.org/api/colors_api.html\n",
    " - specify the hexadecimal or RGB colour code directly to pick any colour you want - https://www.w3schools.com/colors/colors_picker.asp\n",
    "\n",
    "Marker and line style options:\n",
    " - https://matplotlib.org/api/markers_api.html\n",
    " - https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html\n",
    "\n",
    "Patches (Rectangle, Cirle, Polygon)\n",
    " - https://matplotlib.org/api/_as_gen/matplotlib.patches.Rectangle.html#matplotlib.patches.Rectangle\n",
    " - https://matplotlib.org/api/_as_gen/matplotlib.patches.Polygon.html#matplotlib.patches.Polygon\n",
    " - https://matplotlib.org/api/_as_gen/matplotlib.patches.Circle.html#matplotlib.patches.Circle\n",
    " \n",
    " \n",
    "Or look in the matplotlib gallery to see if anything provides inspiration:\n",
    " - https://matplotlib.org/gallery.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extra Challenge**: can you use *random number generation* to add detail to your picture?"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}