diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
.direnv/
__pycache__/
+.envrc
diff --git a/.idea/.name b/.idea/.name
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diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
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diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
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--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
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diff --git a/.idea/misc.xml b/.idea/misc.xml
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diff --git a/.idea/modules.xml b/.idea/modules.xml
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diff --git a/.idea/traffic-simulation.iml b/.idea/traffic-simulation.iml
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--- /dev/null
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diff --git a/.idea/vcs.xml b/.idea/vcs.xml
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
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diff --git a/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb
new file mode 100644
index 0000000..6459344
--- /dev/null
+++ b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "import math\n",
+ "import statistics\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#import traffic_sim.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[]\n"
+ ]
+ }
+ ],
+ "source": [
+ "class Road:\n",
+ " def __init__(self):\n",
+ " self.length = 1000\n",
+ "\n",
+ " def __str__(self):\n",
+ " return self.length\n",
+ "\n",
+ "\n",
+ "class Car:\n",
+ " def __init__(self, location, following_who=None):\n",
+ " self.speed = 30\n",
+ " self.max_speed = 60\n",
+ " self.min_distance = int(self.speed + 5)\n",
+ " self.location = location\n",
+ " self.following_who = following_who\n",
+ "\n",
+ " @property\n",
+ " # wha? So we use this to call without passing variables right?\n",
+ " def __str__(self):\n",
+ " return \"{}\".format(self.location)\n",
+ "\n",
+ " def accelerate(self):\n",
+ " if self.speed < self.max_speed:\n",
+ " self.speed += 2\n",
+ " # return\n",
+ " else:\n",
+ " self.speed = self.following_who.speed\n",
+ " # return\n",
+ "\n",
+ " def decelerate(self):\n",
+ " distraction = random.randint(0, 9)\n",
+ " if distraction == 0:\n",
+ " self.speed -= 2\n",
+ " if self.speed < 0:\n",
+ " self.speed = 0\n",
+ "\n",
+ " def simulate(self):\n",
+ " if self.following_who.location > self.min_distance:\n",
+ " self.accelerate()\n",
+ " self.decelerate()\n",
+ " self.location += self.speed\n",
+ " if self.location >= road.length:\n",
+ " self.location -= road.length\n",
+ " else:\n",
+ " self.decelerate()\n",
+ " self.location += self.speed\n",
+ " if self.location >= road.length:\n",
+ " self.location -= road.length\n",
+ "\n",
+ "\n",
+ "road = Road()\n",
+ "car_list = []\n",
+ "location = 980\n",
+ "car_in_front = None\n",
+ "traffic = []\n",
+ "traffic_log = []\n",
+ "print(traffic_log)\n",
+ "in_range = []\n",
+ "itteration_log = []\n",
+ "speed_log = []\n",
+ "\n",
+ "for _ in range(30):\n",
+ " # this creates the car instances\n",
+ " car_to_spawn = Car(location, car_in_front)\n",
+ " car_list.append(car_to_spawn)\n",
+ " # this next line allows the first car to spawn without knowing who it is following by setting var above to none\n",
+ " # Each iteration allows the first car to be assigned the last car no matter how many are put on the road\n",
+ " car_list[0].following_who = car_list[-1]\n",
+ " location -= 32\n",
+ " car_in_front = car_to_spawn\n",
+ "\n",
+ "for _ in range(120):\n",
+ " for car in car_list:\n",
+ " car.simulate()\n",
+ " [speed_log.append(car.speed)]\n",
+ " [traffic.append(car.location)]\n",
+ " [in_range.append(_)]\n",
+ " [traffic_log.append(traffic)]\n",
+ " #for use with scatter plot\n",
+ " itteration_log.append(in_range)\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AZzT9IKOWPbyLfXmQTrsaW+nhNex9LdGJXGvszqUX1tpmO3g3D9J3\nD+9c5vVmOniHvmfkdVir25vq/W0lx8yNlOO00HqX6bXr4A1bosM6l7NplsMGafoBAAAAQAKafgAA\nAADAXjb9AAAAAKAzmn7AXQfaKGlaIzP0b5r2ZXZ/9oodnaHXddMPk1gYtPkeXsTqjSM9vOk218Ob\n8n37ftyM6/Kdx0nUOlvamDUp1TjN3MFL83knZnjfSNBMbSHTNZpiqXUuhYbvEfvui6GW+dbmBLSm\n6QcAAAAACWj6AQAAAAB72fQDAAAAgM7Y9AMAAACAzjjIA6Y6EEROFb2+bWJId7HDCF48XpJY88cf\nlvqFt17++Z1nEU+faJMOWfowjFmj1ytFz+dcP155GEYHzd055uCotW/qvGk475a4Z9K+381o7Lhm\neS+ba01L8/rC4ReH9HCvOvwiRs+/wXti4NCLqx+9rfkAHOYgDwAAAABIwEEeAAAAAMBeNv0AAAAA\noDOafrCQpTt4e59HgpaLNt7xlm7jRcw4Jxt1acY/7HJjeK+Nd/VAm5/bc6wbRzeZps6bGebdnPdO\nhjV6Sae2uVYdpznmlB5eepmu0RhZPn8upsF8GxyzJ5+PeO/hwI/Off1PdmCdS30vdLLeQEuafgAA\nAACQgKYfAAAAALCXTT8AAAAA6IymH0yUob+028iL0Mk7ZK5rppE3zxje6+R1kIZYtZF39QROb950\n0MiLSNIeaujUNt7N96/1Xtawv5Th/fjmuUy8Hnd/2Dpr/Ry2cj/q4MWo+TU4XhdDPzLXdW7miHs0\n9dyfq4MYHd8zsCGafgAAAACQgKYfAAAAALCXTT8AAAAA6IymH3DQnI2kps2jox6wXTtq3MMu1xA8\nl/5dxLQxHNWemTpvZph3c947mbpoc5naHlqtzTRz/+7qxy1/rZu3oFZa65eW9V49u7bXifPt7Dt4\nEa8cu0zr1F4Nr3+meyTD2GuWQxuafgAAAACQgKYfAAAAALCXTT8AAAAA6IymHzQ0Z/9i8UbOUKMk\nYvYu0pJjeK9/d/VAm88OpOgwnjp/Zpx3c/Rzsja1Wps6dquM0wxzKUPj6Oa56OCNsrV7NXvvq6kT\n7lU9vDjLHl7W+yLLWO828PTvgAhNPwAAAABIQdMPAAAAANjLph8AAAAAdMamHwAAAAB0xkEe0JLD\nLzZr7sj7qOj+sWHrBedby3h2luD13DZ5+MXVAzc70CHT4QlNA/ArrfVL2do9uvhBV2trcPhBPPl8\nxHsPB35Mzms82dgx28I90MEhGBneIxx+cbyuDiyEM+IgDwAAAABIwEEeAAAAAMBeNv0AAAAAoDOa\nftDQwR5Fxw2ozfXwGjbLjtWqVZKhf7OEU9tDq47PxHs8U0OqeVtnhXtuaZmu36tkbHvNZuR9uW/u\nx8XQFM53bZsaMXbp35tadBBj3fskwxjv9u8iNPAOmfOaLb6Or/HZeUPvq5CZph8AAAAAJKDpBwAA\nAADsZdMPAAAAADqj6QcJLNGsOLr9cajhs3KXUBfvOOfUw8vUe9HBO12m67jrrPp3ESfPu8H5r4P3\n0q0xTP8edOp6vJEOXsSy462Dd5w5r9U59O/uPvw8a0zzxjawCE0/AAAAAEhA0w8AAAAA2MumHwAA\nAAB0RtMPpjrUrFixZ6F/d7pTx261dpAe3sAPPL+WTKZ7NWPba1YNO3gvxijT9VzMgbUs03o1aGLz\nNss9k2He7Y7Fo8uIi8udv9T5en6MFN22ltbuRs8wnk162hs395qSZe2EzDT9AAAAACABTT8AAAAA\nYC+bfgAAAADQGU0/aCh9c2gFm2lWndrkSvQajm7HHPXD+u3LHJLheja9jlswQw/v6see6Xo8MJ5D\nQ5FqHBqtN2vdO+nXjTNdz4d01cFL1K5tMa6jxjDRa29pqfetZmO90nU4u89JMJGmHwAAAAAkoOkH\nAAAAAOxl0w8AAAAAOtNk06+U8mtKKT9XSvnp6z9/sJTypVLKV0spXyylvNbicQAAAACAV2vS9Cul\n/PGI+L0R8ZtqrT9cSvlMRHyr1vqZUsonI+IDtdZ3d75H0w9OUN56UuO9hy+/8OaTqM8e5rmXTgyG\nZ4ruNw9ydxqcPmTt63lWkedT77kR8zzDQQWLGxjXi3gUj+Pi5s8pxqDxIQ1L3zsZ5tZRr/kM1/Fd\nXRyCkfQ6nvqe6fCLDR1+sc/M16Dl/bX2Zzs4Jy33yyZv+pVSPhwRfy4i/qOI+OO11n+llPILEfF2\nrfV5KeWNiListX7/zvfZ9AMAAACAa9kO8vhPIuLfj4jv3vra67XW59f/+3lEvN7gcQAAAACAI0za\n9Cul/MsR8c1a689FDP9ab736VcLp/4YYAAAAADjK+yd+/z8bET9cSvlDEfGPRsQ/Vkr58xHxvJTy\nRq31G6WUD0XEN4e+uZRyceuPl7XWy4nPB7bnxMZKhg7SzXNp2YBq3KfagsyNlLNq40Wcfj8eOU6Z\nr/WsdsZ1t40XsfIYzNC6WureyTKnjn69Z7jGvzDHtdLDe2ns56Jz7uEtvW4cNdaddvAicn1mz+pm\nzL/8KOIrF3f+m7HiHJRSHkTEg1l+douDPCIiSilvR8S/d930+0xEfLvW+ulSyrsR8ZqDPAAAAABg\nv2xNv9te7CD+6Yj4A6WUr0bEv3D9ZwAAAABgAc1+02/0A/tNPwAAAAC40XK/bGrTDxipaSfkjNtI\nEfkaKYu2lTJo2L+LOL6B133bZYv9u4hJ686S9072OaWNd5yW13GxdukGrlmLLl7v81UP77CW63mW\nZmlW98Y6Uw+vg2Y59MJv+gEAAABAApmbfgAAAADAymz6AQAAAEBnNP3gDGVrpCzWU1rTxJ6RDt4e\ne8Z1aIrr4J0u65waNQYn9oV6MHf/LmLBBl6ia9aifxfR/3xdev145WeKRP27iLafgbKu1ZncjPdA\n/y5im58VMnyun+W9oaMWKKxJ0w8AAAAAEtD0AwAAAAD2sukHAAAAAJ3R9IMNW7SttJYJXSMdvD0G\nxvQiHsXjuLj58+qvfYae1VLtyAxtnX1Gj0FHXbGxWl5HHbzTxvPo+dpR9ylD/y5i5Djf+2YdvJ7c\nGetMHbypreQk79WzfDZJ+j6wBPcz3KfpBwAAAAAJaPoBAAAAAHvZ9AMAAACAzmj6QU8mNjyO7Ypk\n6aAsZmdcd/t3ESu/9hl6Vks2yLL3V86xK3aKltdxqf5i5ms2djxHjVknvae13ouOWh+PGeOFrkPL\n9fzs3v9PNDjmF/uWm4XH7sR5l+G9Wv+urcz382KfA+AMaPoBAAAAQAKafgAAAADAXjb9AAAAAKAz\nNv0AAAAAoDMO8oCJjgnNZogfL24njPwk3olPxNM7f2W1MZgh2rx0cDjrnBoVjBfPvqPloRcRCx58\nkeCanRojP9f5usb6cfLhFxFX47zwoSsOv1jOvrEeOvjCoRfHm+W9IfHhR3PLfB8v+jlgDWc876AF\nB3kAAAAAQAIO8gAAAAAA9rLpBwAAAACd0fSDDPa0KobyITp4JzzOxjosR/fEIs6iZ9L6+i3dX8za\ngjtlXM9xvq7V5nrlWB8zxgteh5b3VYYeWmaD/a6s/burJ3K4rZnkPXqW94ak6/+Sst3P3ffvXpi4\n/o8Zp2zXGLhL0w8AAAAAEtD0AwAAAAD2sukHAAAAAJ3R9IMlNGi6LN0gydLYGXKOXbGxNtvBS3zN\nxjZrznmerrV+HDXmx67HC1wXHbzlHNvBi1h43CbMswzXXAevrQzXdJ/Fe7hr0MGbx8C4XsSjeBwX\nN3/u9rVDApp+AAAAAJCAph8AAAAAsJdNPwAAAADojKYfNJK1zTGqBaijc0fL/l3EjO2bxE24Fh28\niL7na5Z+ph7ezs9KsH6vZd99ONTAW3ycTrzvM1zjWd4bEq//c8uydg45i/5dxGLN6szXelY747vb\nwYtYeQwafg5b/LMznBFNPwAAAABIQNMPAAAAANjLph8AAAAAdEbTD06lo3PPlK7Ioi2P5C24Y9tT\n59i/u23NRtdRY3/qmM9wrebu30WcQVvpFc6lg3f1rcs9fx289jL0DW87m5ZXo3l37Hqe4f5dVPb+\n3Qsb6+BlWy9eOJt1AxrQ9AMAAACABDT9AAAAAIC9bPoBAAAAQGds+gEAAABAZxzkAQtx+MW8xozv\n0YcadBp7XyvufXC+njrWM16jlvfX2QXVRzqXQzCWfu4OwWgr233c8oCetBx+Ma+B8d09ACPFa194\nHpwq8/wZ/drPeK2H3jjIAwAAAAAScJAHAAAAALCXTT8AAAAA6IymH8xhYx2MY9tTo/pQyRuAp8rU\nw3vnWcTTp4NPZpUOXstWToYeWmaD92LW/t3VEzn4PLJ0kGbpPXW6Fh4j6328aA93TXp489oZ390W\nXkSS1z5xHpxjB2/yGnFm6/4ca/1ZNEthIZp+AAAAAJCAph8AAAAAsJdNPwAAAADojKYfjDS1PaKL\nt17naXfsH11GXFzeeyL7n8cM10MHbznHdvAiFh43HbyBH9rn2neMjPfx2bSVGsy7Me/xGa/1bAbG\ndreBl/K1T5wT59TDm6WDF9Ht2t/y/l+8U3pm1wq2TtMPAAAAABLQ9AMAAAAA9rLpBwAAAACd0fSD\n5NbqtQz1Qe418BL07yJOa5Zk6uBswc3Yf/lRxFcu7vy3VcdMD2/gh+rh3bbW/Oy+g9eo6XTsOGW5\nZxe1tQ7exDmxRJesqzXijNb6lvf/4v27e08g93U7daw1yyEvTT8AAAAASEDTDwAAAADYy6YfAAAA\nAHRG0w8aOrmD16iN9KrnEjGtWXKWTaUT3Bv7TB28hh2VDG2kWdo8M9yPW7LmdV29tTQ3Hbx57Yzv\nbv8uItlrn7gez92NzDh/Tn7NZ9YQm2M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AAAAAyJU7etwHkly7dvvarN7tt0dVnVq7eVtr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\nDzYM+WhAhWzLAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = traffic_log\n",
+ "y = itteration_log\n",
+ "plt.rcParams['figure.figsize'] = 22, 12\n",
+ "plt.scatter(x, y, marker = \"_\", c=['b', 'r', 'g'])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "62.216772722464235\n"
+ ]
+ }
+ ],
+ "source": [
+ "average_speed = (sum(speed_log) / len(speed_log))\n",
+ "variation = []\n",
+ "for _ in speed_log:\n",
+ " variation.append((_ - average_speed)**2)\n",
+ "variation = (sum(variation) / len(variation))\n",
+ "standard_dev = variation**(.5)\n",
+ "optimum_speed = average_speed + standard_dev\n",
+ "\n",
+ "print(optimum_speed)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.4.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/README.md b/README.md
index f63bc20..58fb3a0 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
-# Traffic simulation
+# Run traffic_sim.ipynb using Ipython Notebook
+## Traffic simulation
+
## Description
Analyze the behavior of drivers on a new road to determine the optimal speed limits.
diff --git a/traffic_sim.ipynb b/traffic_sim.ipynb
new file mode 100644
index 0000000..6459344
--- /dev/null
+++ b/traffic_sim.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "import math\n",
+ "import statistics\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "\n",
+ "#import traffic_sim.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[]\n"
+ ]
+ }
+ ],
+ "source": [
+ "class Road:\n",
+ " def __init__(self):\n",
+ " self.length = 1000\n",
+ "\n",
+ " def __str__(self):\n",
+ " return self.length\n",
+ "\n",
+ "\n",
+ "class Car:\n",
+ " def __init__(self, location, following_who=None):\n",
+ " self.speed = 30\n",
+ " self.max_speed = 60\n",
+ " self.min_distance = int(self.speed + 5)\n",
+ " self.location = location\n",
+ " self.following_who = following_who\n",
+ "\n",
+ " @property\n",
+ " # wha? So we use this to call without passing variables right?\n",
+ " def __str__(self):\n",
+ " return \"{}\".format(self.location)\n",
+ "\n",
+ " def accelerate(self):\n",
+ " if self.speed < self.max_speed:\n",
+ " self.speed += 2\n",
+ " # return\n",
+ " else:\n",
+ " self.speed = self.following_who.speed\n",
+ " # return\n",
+ "\n",
+ " def decelerate(self):\n",
+ " distraction = random.randint(0, 9)\n",
+ " if distraction == 0:\n",
+ " self.speed -= 2\n",
+ " if self.speed < 0:\n",
+ " self.speed = 0\n",
+ "\n",
+ " def simulate(self):\n",
+ " if self.following_who.location > self.min_distance:\n",
+ " self.accelerate()\n",
+ " self.decelerate()\n",
+ " self.location += self.speed\n",
+ " if self.location >= road.length:\n",
+ " self.location -= road.length\n",
+ " else:\n",
+ " self.decelerate()\n",
+ " self.location += self.speed\n",
+ " if self.location >= road.length:\n",
+ " self.location -= road.length\n",
+ "\n",
+ "\n",
+ "road = Road()\n",
+ "car_list = []\n",
+ "location = 980\n",
+ "car_in_front = None\n",
+ "traffic = []\n",
+ "traffic_log = []\n",
+ "print(traffic_log)\n",
+ "in_range = []\n",
+ "itteration_log = []\n",
+ "speed_log = []\n",
+ "\n",
+ "for _ in range(30):\n",
+ " # this creates the car instances\n",
+ " car_to_spawn = Car(location, car_in_front)\n",
+ " car_list.append(car_to_spawn)\n",
+ " # this next line allows the first car to spawn without knowing who it is following by setting var above to none\n",
+ " # Each iteration allows the first car to be assigned the last car no matter how many are put on the road\n",
+ " car_list[0].following_who = car_list[-1]\n",
+ " location -= 32\n",
+ " car_in_front = car_to_spawn\n",
+ "\n",
+ "for _ in range(120):\n",
+ " for car in car_list:\n",
+ " car.simulate()\n",
+ " [speed_log.append(car.speed)]\n",
+ " [traffic.append(car.location)]\n",
+ " [in_range.append(_)]\n",
+ " [traffic_log.append(traffic)]\n",
+ " #for use with scatter plot\n",
+ " itteration_log.append(in_range)\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AZzT9IKOWPbyLfXmQTrsaW+nhNex9LdGJXGvszqUX1tpmO3g3D9J3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\nbv8uItlrn7gez92NzDh/Tn7NZ9YQm2M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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = traffic_log\n",
+ "y = itteration_log\n",
+ "plt.rcParams['figure.figsize'] = 22, 12\n",
+ "plt.scatter(x, y, marker = \"_\", c=['b', 'r', 'g'])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "62.216772722464235\n"
+ ]
+ }
+ ],
+ "source": [
+ "average_speed = (sum(speed_log) / len(speed_log))\n",
+ "variation = []\n",
+ "for _ in speed_log:\n",
+ " variation.append((_ - average_speed)**2)\n",
+ "variation = (sum(variation) / len(variation))\n",
+ "standard_dev = variation**(.5)\n",
+ "optimum_speed = average_speed + standard_dev\n",
+ "\n",
+ "print(optimum_speed)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.4.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/traffic_sim.py b/traffic_sim.py
new file mode 100644
index 0000000..5d59266
--- /dev/null
+++ b/traffic_sim.py
@@ -0,0 +1,87 @@
+import random
+
+class Road:
+ def __init__(self):
+ self.length = 1000
+
+ def __str__(self):
+ return self.length
+
+
+class Car:
+ def __init__(self, location, following_who=None):
+ self.speed = 0
+ self.max_speed = 33
+ self.min_distance = int(self.speed + 15)
+ self.location = location
+ self.following_who = following_who
+
+ @property
+ # wha? So we use this to call without passing variables right?
+ def __str__(self):
+ return "{}".format(self.location)
+
+ def accelerate(self):
+ if self.speed < self.max_speed:
+ self.speed += 2
+ return
+ else:
+ self.speed = self.following_who.speed
+ return
+
+ def decelerate(self):
+ distraction = random.randint(0, 9)
+ if distraction == 0:
+ self.speed -= 2
+ if self.speed < 0:
+ self.speed = 0
+
+ def simulate(self):
+ if self.following_who.location > self.min_distance:
+ self.accelerate()
+ self.decelerate()
+ self.location += self.speed
+ if self.location >= road.length:
+ self.location -= road.length
+ else:
+ self.decelerate()
+ self.location += self.speed
+ if self.location >= road.length:
+ self.location -= road.length
+
+
+road = Road()
+car_list = []
+location = 600
+car_in_front = None
+traffic = []
+traffic_log = []
+in_range = []
+itteration_log = []
+print(traffic_log)
+
+for _ in range(30):
+ # this creates the car instances
+ car_to_spawn = Car(location, car_in_front)
+ car_list.append(car_to_spawn)
+ # this next line allows the first car to spawn without knowing who it is following by setting var above to none
+ # Each iteration allows the first car to be assigned the last car no matter how many are put on the road
+ car_list[0].following_who = car_list[-1]
+ location -= 20
+ car_in_front = car_to_spawn
+
+print("DEBUG: first run")
+for _ in range(60):
+ print(_, " RUN")
+ for car in car_list:
+ car.simulate()
+ [traffic.append(car.location)]
+ [in_range.append(_)]
+ [traffic_log.append(traffic)]
+ # itteration log is for using with a scatter plot in homework
+ itteration_log.append(in_range)
+ traffic = []
+ in_range = []
+print(len(traffic_log[0]))
+print(len(traffic_log))
+print(traffic_log)
\ No newline at end of file