demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Generic-example\" data-toc-modified-id=\"Generic-example-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Generic example</a></div><div class=\"lev2 toc-item\"><a href=\"#Combining-polygons-into-a-single-feature\" data-toc-modified-id=\"Combining-polygons-into-a-single-feature-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Combining polygons into a single feature</a></div><div class=\"lev1 toc-item\"><a href=\"#Bar-class-demo\" data-toc-modified-id=\"Bar-class-demo-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Bar class demo</a></div><div class=\"lev1 toc-item\"><a href=\"#TEF-device\" data-toc-modified-id=\"TEF-device-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>TEF device</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import importlib\n",
    "from shapely.geometry import Polygon\n",
    "from shapely.geometry.polygon import orient\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import device_geo as geo\n",
    "importlib.reload(geo)\n",
    "\n",
    "import bar\n",
    "importlib.reload(bar)\n",
    "\n",
    "import hallbar\n",
    "importlib.reload(hallbar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generic example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of how to interact with device_geo Feature and Device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_points = [(0,0),(1,0),(1,1),(0,1)]\n",
    "feat_1 = geo.Feature(bar_points)\n",
    "feat_2 = feat_1.copy()\n",
    "feat_2.scale(2, 2)\n",
    "\n",
    "dev = geo.Device()\n",
    "dev.register_feature(feat_1, 0, 0, 0, 0)\n",
    "dev.register_feature(feat_1, 2, 2, 0, 0)\n",
    "dev.register_feature(feat_1, 2.5, 2.5, 0, 0)\n",
    "dev.register_feature(feat_2, 0, 0, 45, 1)\n",
    "\n",
    "dev.heal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "fig = dev.gen_fig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev.write_dxf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining polygons into a single feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_points = [(0,0),(1,0),(1,1),(0,1)]\n",
    "new_points = [(0.5, 1.5), (1.5, 0.5), (0.5, 0.5)]\n",
    "feat = geo.Feature(bar_points)\n",
    "feat.union(new_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array('d', [0.0, 0.0, 0.5, 0.5, 1.0, 1.5, 1.0, 1.0, 0.0]),\n",
       " array('d', [0.0, 1.0, 1.0, 1.5, 1.0, 0.5, 0.5, 0.0, 0.0]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat.poly.exterior.coords.xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "fig = feat.gen_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As written now, a single feature cannot be a set of disjoint polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Polygons are disjoint",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-08aaec70a6f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mnew_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_points\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFeature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbar_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfeat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/mnt/c/Users/Aaron/pypolyfab/device_geo.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, ps)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0mnew_poly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_poly\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_poly\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mPolygon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Polygons are disjoint'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_poly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Polygons are disjoint"
     ]
    }
   ],
   "source": [
    "bar_points = [(0,0),(1,0),(1,1),(0,1)]\n",
    "new_points = [(0.5, 1.5), (1.5, 0.5), (0.5, 0.5)]\n",
    "new_points = [(x+10,y) for (x,y) in new_points]\n",
    "feat = geo.Feature(bar_points)\n",
    "feat.union(new_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar class demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a second example using a prebuilt Device class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "bar_1 = bar.Bar(3,1)\n",
    "fig = bar_1.gen_fig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAANSUlEQVR4nO3df4zkdX3H8edLrtfGCCreCZQDFlIguXgNthusscZWsaFooEmhUkuj16YkNU1samOwJE1j/7Kmtk000YuW0lYDKVUhBS2gEtNGKIsihKMgUn8cBV21So1pkfjuHzvXrOceM7vznZl9w/ORbG5+fO/zI8M873vfneVSVUiS+nrWohcgSZqOIZek5gy5JDVnyCWpOUMuSc3tWMSku3btqqWlpUVMLUlt3XXXXd+oqt1HPr6QkC8tLbGysrKIqSWprSRf3uhxL61IUnOGXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzS3kc+Rbte/qfQAsn7C84JVI0tZccMYFXHLWJYOO6Rm5JM3JA996gJsevmnwcVudkR8+E7/q/KtmOs/SFTcC8JLTj5/pPJK2j4vOOZnXv+TUmc6x/+P7ZzKuZ+SSnvEOPvo419/9yKKXsWWtzshh7a8ms/pT7bATzn4cgGefdNxM55G0PTx75+N8Cdj/8dm+5x/41gOcffzZg4/bKuQXnHHBopcgSVt29vFnz6RjrUJ+yVmXDP7d3o287n2fAeCq818687kkLV7397zXyCWpOUMuSc0ZcklqzpBLUnOGXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzRlySWrOkEtSc4OFPMkxST6X5J+GGlOSNN6QZ+RvBu4fcDxJ0gQGCXmSPcBrgPcPMZ4kaXJDnZH/JfBW4AcDjSdJmtDUIU/yWuDrVXXXmOMuT7KSZGV1dXXaaSVJI0Ockb8MuDDJl4BrgFcm+fsjD6qqA1W1XFXLu3fvHmBaSRIMEPKqeltV7amqJeBS4JNVddnUK5MkTcTPkUtSc4P+48tVdRtw25BjSpKemmfkktScIZek5gy5JDVnyCWpOUMuSc0ZcklqzpBLUnOGXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzRlySWrOkEtSc4Zckpoz5JLUnCGXpOYMuSQ1Z8glqTlDLknNGXJJas6QS1JzhlySmjPkktScIZek5gy5JDVnyCWpOUMuSc0ZcklqbuqQJzklyaeSHExyX5I3D7EwSdJkdgwwxpPAW6rqs0mOBe5KcktVHRxgbEnSGFOfkVfVo1X12dHt/wbuB06edlxJ0mQGvUaeZAl4MXDHBs9dnmQlycrq6uqQ00rSM9pgIU/yHOAfgd+vqsePfL6qDlTVclUt7969e6hpJekZb5CQJ/kx1iL+war68BBjSpImM8SnVgJ8ALi/qt41/ZIkSZsxxBn5y4DfBF6Z5O7R1wUDjCtJmsDUHz+sqn8BMsBaJElb4E92SlJzhlySmjPkktScIZek5gy5JDVnyCWpOUMuSc0ZcklqzpBLUnOGXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzRlySWrOkEtSc4Zckpoz5JLUnCGXpOYMuSQ1Z8glqTlDLknNGXJJas6QS1JzhlySmjPkktScIZek5gy5JDVnyCWpuUFCnuT8JA8keSjJFUOMKUmazNQhT3IM8B7gl4G9wK8n2TvtuJKkyQxxRn4u8FBVPVxVTwDXABcNMK4kaQJDhPxk4Kvr7h8aPfZDklyeZCXJyurq6gDTSpJgjt/srKoDVbVcVcu7d++e17SS9LQ3RMgfAU5Zd3/P6DFJ0hwMEfI7gTOTnJ5kJ3ApcMMA40qSJrBj2gGq6skkvwf8M3AM8NdVdd/UK5MkTWTqkANU1U3ATUOMJUnaHH+yU5KaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzRlySWrOkEtSc4Zckpoz5JLUnCGXpOYMuSQ1Z8glqTlDLknNGXJJas6QS1JzhlySmjPkktScIZek5gy5JDVnyCWpOUMuSc0ZcklqzpBLUnOGXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzU0V8iTvTPLvSe5J8pEkzxtoXZKkCU17Rn4L8KKq+mngQeBt0y9JkrQZU4W8qm6uqidHd28H9ky/JEnSZgx5jfy3gI8d7ckklydZSbKyuro64LSS9My2Y9wBSW4FTtzgqSur6vrRMVcCTwIfPNo4VXUAOACwvLxcW1qtJOlHjA15VZ33VM8neSPwWuBVVWWgJWnOxob8qSQ5H3gr8Iqq+t4wS5Ikbca018jfDRwL3JLk7iTvHWBNkqRNmOqMvKp+aqiFSJK2xp/slKTmDLkkNWfIJak5Qy5JzRlySWrOkEtSc4Zckpoz5JLUnCGXpOYMuSQ1Z8glqTlDLknNGXJJas6QS1JzhlySmjPkktScIZek5gy5JDVnyCWpOUMuSc0ZcklqzpBLUnOGXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzRlySWpukJAneUuSSrJriPEkSZObOuRJTgF+CfjK9MuRJG3WEGfkfwG8FagBxpIkbdJUIU9yEfBIVX1+oPVIkjZpx7gDktwKnLjBU1cCf8TaZZWxklwOXA5w6qmnbmKJkqSnMjbkVXXeRo8n2QecDnw+CcAe4LNJzq2qxzYY5wBwAGB5ednLMJI0kLEhP5qquhd44eH7Sb4ELFfVNwZYlyRpQn6OXJKa2/IZ+ZGqammosSRJk/OMXJKaM+SS1Jwhl6TmDLkkNWfIJak5Qy5JzRlySWrOkEtSc4Zckpoz5JLUnCGXpOYG+3+tzMWfPHft19N+fqbT/PE3v7N246rnznQeSdvDXN/z+y6G5f2DDukZuSTNy2P3wr3XDT5srzPyw2fi+2+c6TRvf99nOPjo4+x94riZziNpezj4xOPsPek4rt3/0tlOdNVrZjJsr5DPyUXnnLzoJUiao70nHdf6fd8v5I/dO7M/1Q57PfD6nTOdQtJ2c3D0NUuP3Qsn7ht82F4h33fxolcgSVt34r6ZdKxXyJf3D/7dXknqzk+tSFJzhlySmjPkktScIZek5gy5JDVnyCWpOUMuSc0ZcklqLlU1/0mTVeDLW/ztu4BvDLicRXIv28/TZR/gXrarafZyWlXtPvLBhYR8GklWqmp50esYgnvZfp4u+wD3sl3NYi9eWpGk5gy5JDXXMeQHFr2AAbmX7efpsg9wL9vV4Htpd41ckvTDOp6RS5LWMeSS1Ny2D3mS45PckuQLo1+ff5TjTk1yc5L7kxxMsjTnpY416V5Gxx6X5FCSd89zjZOaZC9JzknymST3JbknyesWsdaNJDk/yQNJHkpyxQbP/3iSa0fP37Ed/3s6bIK9/MHoPXFPkk8kOW0R65zEuL2sO+5Xk1SSbfmRxEn2keTXRq/LfUk+NNWEVbWtv4A/A64Y3b4CeMdRjrsNePXo9nOAZy967Vvdy+j5vwI+BLx70eve6l6As4AzR7d/EngUeN42WPsxwBeBM4CdwOeBvUcc8ybgvaPblwLXLnrdU+zlFw+/H4Df7byX0XHHAp8GbgeWF73uLb4mZwKfA54/uv/Caebc9mfkwEXA1aPbVwO/cuQBSfYCO6rqFoCq+m5VfW9uK5zc2L0AJPlZ4ATg5vksa0vG7qWqHqyqL4xu/yfwdeBHfiptAc4FHqqqh6vqCeAa1vaz3vr9XQe8KknmuMZJjd1LVX1q3fvhdmDPnNc4qUleF4A/Bd4B/M88F7cJk+zjd4D3VNV/AVTV16eZsEPIT6iqR0e3H2MtcEc6C/h2kg8n+VySdyY5Zn5LnNjYvSR5FvDnwB/Oc2FbMMnr8v+SnMva2ckXZ72wCZwMfHXd/UOjxzY8pqqeBL4DvGAuq9ucSfay3m8DH5vpirZu7F6S/AxwSlXdOM+FbdIkr8lZwFlJ/jXJ7UnOn2bCbfGPLye5FThxg6euXH+nqirJRp+X3AG8HHgx8BXgWuCNwAeGXel4A+zlTcBNVXVo0SeAA+zl8DgnAX8HvKGqfjDsKjWpJJcBy8ArFr2WrRid5LyLtfd2dztYu7zyC6z9DenTSfZV1be3OtjCVdV5R3suydeSnFRVj46CsNFfQQ4Bd1fVw6Pf81Hg51hAyAfYy0uBlyd5E2vX+ncm+W5VHfUbP7MywF5IchxwI3BlVd0+o6Vu1iPAKevu7xk9ttExh5LsAJ4LfHM+y9uUSfZCkvNY+wP4FVX1v3Na22aN28uxwIuA20YnOScCNyS5sKpW5rbK8SZ5TQ4Bd1TV94H/SPIga2G/cysTdri0cgPwhtHtNwDXb3DMncDzkhy+/vpK4OAc1rZZY/dSVb9RVadW1RJrl1f+dhERn8DYvSTZCXyEtT1cN8e1jXMncGaS00drvJS1/ay3fn8XA5+s0Xeltpmxe0nyYuB9wIXTXoudsafcS1V9p6p2VdXS6P1xO2t72k4Rh8n++/ooa2fjJNnF2qWWh7c846K/wzvBd4BfAHwC+AJwK3D86PFl4P3rjns1cA9wL/A3wM5Fr32re1l3/BvZvp9aGbsX4DLg+8Dd677OWfTaR2u7AHiQtWv2V44eeztrYQD4CeAfgIeAfwPOWPSap9jLrcDX1r0GNyx6zVvdyxHH3sY2/NTKhK9JWLtMdHDUrEunmc8f0Zek5jpcWpEkPQVDLknNGXJJas6QS1JzhlySmjPkktScIZek5v4PLrg21YbDTmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_2 = bar.Bar(10,1)\n",
    "fig = bar_2.gen_fig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_2.write_dxf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hall bar device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a demo class for a Hall bar where we set the parameters in the following order:\n",
    "\n",
    "bar_width\n",
    "num_contacts\n",
    "contact_width\n",
    "contact_length\n",
    "sq_btw_contacts\n",
    "\n",
    "Optional parameters\n",
    "inject_sq: whether to include traditional sourcing contacts and how many squares to buffer them by.\n",
    "\n",
    "bar_width, num_contacts, contact_width, contact_length, sq_btw_contacts, inject_sq = 0, pad=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import hallbar\n",
    "importlib.reload(hallbar)\n",
    "\n",
    "test_bar= hallbar.HallBar(3, 4, 0.3, 0.3, 1, inject_sq=1)\n",
    "fig = test_bar.gen_fig()\n",
    "test_bar.bar_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aoeuasdfarsttsaaoeuasdf"
   ]
  }
 ],
 "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": "3.8.2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "103.267px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": true,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}