2019-12-6_part1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%run tests.py\n",
    "\n",
    "class Orbits():\n",
    "    def __init__(self,label, mother=False):\n",
    "        self.name = label\n",
    "        self.daughters = []\n",
    "        self.mother = mother\n",
    "        if mother:\n",
    "            mother.add_daughter(self)\n",
    "    \n",
    "    def get_name(self):\n",
    "        return self.name\n",
    "    \n",
    "    def add_daughter(self,daughter):\n",
    "        self.daughters.append(daughter)\n",
    "        \n",
    "    def add_mother(self, mother):\n",
    "        self.mother=mother\n",
    "\n",
    "    def __repr__(self):\n",
    "        if not self.mother:\n",
    "            stringrep = \"*\"\n",
    "        else:\n",
    "            stringrep = \"\"\n",
    "        if len(self.daughters)==0:\n",
    "            return stringrep+\"Name = {}\".format(self.name)\n",
    "        else:\n",
    "            stringrep += \"Name = {}, daughters = [\".format(self.name)\n",
    "            for d in self.daughters:\n",
    "                stringrep += \"{},\".format(d)\n",
    "            stringrep+=(\"]\")\n",
    "            return stringrep\n",
    "    \n",
    "    \n",
    "    def get_moms_name(self):\n",
    "        if self.mother:\n",
    "            return self.mother.get_name()\n",
    "        else:\n",
    "            return \"root\"\n",
    "    \n",
    "    def get_self(self,name):\n",
    "        if name ==self.name:\n",
    "            return self\n",
    "        else:\n",
    "            return None\n",
    "        \n",
    "\n",
    "def get_height(node, height):\n",
    "    if not node.mother:\n",
    "        return height\n",
    "    else:\n",
    "        return get_height(node.mother, height+1)\n",
    "\n",
    "def get_daughter_heights(node, heightdict):\n",
    "    heightdict[node.get_name()]=get_height(node,0)\n",
    "    if len(node.daughters)>0:\n",
    "        for d in node.daughters:\n",
    "            heightdict.update(get_daughter_heights(d,heightdict))\n",
    "    return heightdict    \n",
    "\n",
    "def add_up_all_height(node):\n",
    "    if isinstance(node,list):\n",
    "        node = build_tree(node)\n",
    "    heightdict = get_daughter_heights(node, {})\n",
    "    return pd.Series(heightdict).sum()\n",
    "\n",
    "\n",
    "def labels_and_locs(node, momx,momy, myx,myy):\n",
    "    newx = myx\n",
    "    newy = myy\n",
    "    name = node.get_name()\n",
    "    row = pd.DataFrame({name:{'momx':momx,'momy':momy, 'myx':myx, 'myy':myy}}).T\n",
    "    if len(node.daughters)==0:\n",
    "        return row\n",
    "    elif len(node.daughters)==1:\n",
    "        return pd.concat([row,labels_and_locs(node.daughters[0],myx,myy,newx,newy+1)], sort=False)\n",
    "    else:\n",
    "        a = labels_and_locs(node.daughters[0],myx,myy,newx-2, newy+1)\n",
    "        b = labels_and_locs(node.daughters[1],myx,myy,newx+2,newy+1)\n",
    "        return pd.concat([row,a,b], sort=False) \n",
    "\n",
    "def draw_nodemap(root):\n",
    "    if isinstance(root,list):\n",
    "        root = build_tree(root)\n",
    "    nodedf = labels_and_locs(root,0,0,0,0)\n",
    "    minx = np.min([nodedf['momx'].min(),nodedf['myx'].min()])\n",
    "    maxx = np.max([nodedf['momx'].max(),nodedf['myx'].max()])\n",
    "    miny = np.min([nodedf['momy'].min(),nodedf['myy'].min()])\n",
    "    maxy = np.max([nodedf['momy'].max(),nodedf['myy'].max()])\n",
    "    rangex = maxx-minx\n",
    "    rangey = maxy-miny\n",
    "    wh = plt.figaspect(10*rangex/rangey)\n",
    "    fig, ax = plt.subplots()\n",
    "    for label, row in nodedf.iterrows():\n",
    "        xs = [row['momx'],row['myx']]\n",
    "        ys = [row['momy'],row['myy']]\n",
    "        ax.annotate(label,xy=(row['myx'],row['myy']))\n",
    "        ax.plot(xs,ys, linestyle=\":\", color='black')\n",
    "    return nodedf\n",
    "    ax.set_xlim(minx-1,maxx+1)\n",
    "    ax.set_ylim(miny-1,maxy+1)\n",
    "\n",
    "def build_tree(edges):\n",
    "    roots = set()\n",
    "    nodenames = list()\n",
    "    for edge in edges:\n",
    "        mother, child = edge.split(\")\")\n",
    "        if not mother in nodenames:\n",
    "            newroot = (Orbits(mother))\n",
    "            roots.add(newroot)\n",
    "            nodenames.append(mother)\n",
    "        for root in roots:\n",
    "            existingmom = find_node(root,mother)\n",
    "            if existingmom:\n",
    "                break \n",
    "        if not child in nodenames:\n",
    "            newkid = Orbits(child,mother=existingmom)\n",
    "            nodenames.append(child)\n",
    "            continue\n",
    "        else:\n",
    "            for root in roots:\n",
    "                existingkid = find_node(root,child)\n",
    "                if existingkid:\n",
    "                    break\n",
    "            existingmom.add_daughter(existingkid)\n",
    "            existingkid.add_mother(existingmom)\n",
    "            if existingkid in roots:\n",
    "                roots.remove(existingkid)\n",
    "    if len(roots) > 1:\n",
    "        return \"Not valid description\"\n",
    "    else:\n",
    "        return list(roots)[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins = [\"\"\"COM)B\n",
    "B)C\n",
    "C)D\n",
    "D)E\n",
    "E)F\n",
    "B)G\n",
    "G)H\n",
    "D)I\n",
    "E)J\n",
    "J)K\n",
    "K)L\"\"\".split(\"\\n\"),]\n",
    "outs = [42]\n",
    "simple_test(add_up_all_height,ins,outs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294191"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Answer\n",
    "with open(\"2019_12_6_input.txt\") as fileobj:\n",
    "    raw = fileobj.read()\n",
    "edges = raw.strip().split(\"\\n\")\n",
    "add_up_all_height(edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = draw_nodemap(ins[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = draw_nodemap(ins[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-simpleenv]",
   "language": "python",
   "name": "conda-env-.conda-simpleenv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}