rw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "703ff0a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f6dded2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as lines\n",
    "\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51bfb3ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(dist, state):\n",
    "    \n",
    "    unit_dir = np.array([np.sin(state[\"dir\"]*np.pi/180), np.cos(state[\"dir\"]*np.pi/180) ])\n",
    "    \n",
    "    displacement = unit_dir*dist\n",
    "    \n",
    "    new_loc = state[\"loc\"] + displacement\n",
    "    \n",
    "    plt.plot([state[\"loc\"][0], new_loc[0]], [state[\"loc\"][1], new_loc[1]], \n",
    "             color=\"black\", alpha=.5, marker=None)\n",
    "    \n",
    "    state[\"loc\"] = new_loc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3df9179",
   "metadata": {},
   "outputs": [],
   "source": [
    "def turn(state):\n",
    "    if random.random() < .5:\n",
    "        state[\"dir\"] = (state[\"dir\"] + 90) % 360\n",
    "    else:\n",
    "        state[\"dir\"] = (state[\"dir\"] - 90) % 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "224b1d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 421,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10), facecolor=\"white\")\n",
    "ax.set_aspect(1)\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "n_iterations = 1000\n",
    "\n",
    "p_turn = .5\n",
    "\n",
    "state = {\"loc\": np.array([0,0]), \"dir\": 0}\n",
    "\n",
    "for i in range(n_iterations):\n",
    "    \n",
    "    if random.random() < p_turn:\n",
    "        \n",
    "        turn(state)\n",
    "        \n",
    "        forward(10,state)\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        forward(10,state)\n",
    "        \n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:metis] *",
   "language": "python",
   "name": "conda-env-metis-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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}