diff --git a/.envrc b/.envrc
deleted file mode 100644
index 94840b3..0000000
--- a/.envrc
+++ /dev/null
@@ -1 +0,0 @@
-layout python3
diff --git a/.gitignore b/.gitignore
index 574ba10..606512b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
.direnv/
__pycache__/
+.envrc
diff --git a/.idea/.name b/.idea/.name
new file mode 100644
index 0000000..9b1c5e1
--- /dev/null
+++ b/.idea/.name
@@ -0,0 +1 @@
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diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
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new file mode 100644
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diff --git a/.idea/traffic-simulation.iml b/.idea/traffic-simulation.iml
new file mode 100644
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--- /dev/null
+++ b/.idea/traffic-simulation.iml
@@ -0,0 +1,16 @@
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new file mode 100644
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
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diff --git a/.ipynb_checkpoints/Traffic Simulation Plots-checkpoint.ipynb b/.ipynb_checkpoints/Traffic Simulation Plots-checkpoint.ipynb
new file mode 100644
index 0000000..a8fd18d
--- /dev/null
+++ b/.ipynb_checkpoints/Traffic Simulation Plots-checkpoint.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "import statistics as st\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from simulation import Sim\n",
+ "from itertools import count\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "sim = Sim()\n",
+ "sim.time = 60\n",
+ "meta_data = sim.main()\n",
+ "def annoy(data):\n",
+ " x_list = []\n",
+ " y_list = []\n",
+ " speed = []\n",
+ " \n",
+ " x_list = x_list + [x for x,y,z in data]\n",
+ " y_list = y_list + [y for x,y,z in data]\n",
+ " speed = speed + [z for x,y,z in data]\n",
+ " return x_list, y_list, speed\n",
+ "x, y, speed = annoy(meta_data)\n",
+ "\n",
+ "average_speed = (st.mean(speed) * 60**2) / 1000\n",
+ "stan_dev = (st.stdev(speed) * 60**2) / 1000\n",
+ "s_limit = average_speed + stan_dev\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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bDhjr3phhrFiMMcaUigfvjTFmguDB+wlO5S45OlkHUfUajqqv9/O96/dJEf28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\nAX6b2/NJwL2FvM4oHG8i6Ux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kh5K6yl/MvYinSC5aIO3Jca6keyJiz/wVd3r+54Vky71V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\nJjeyB05gtQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.xlabel(\"Time in Seconds\\n\\n Average Speed: {} Kilometers per hour\\n \\\n",
+ " Suggested Speed Limit: {} Kilometers per hour \".format(average_speed, s_limit))\n",
+ "plt.ylabel(\"Distance\")\n",
+ "plt.title(\"Traffic Flow on 1 km Road\")\n",
+ "colors = [np.random.choice([\"blue\", \"green\"]) for _ in x]\n",
+ "plt.scatter(y, x, s=1, c=colors, marker=\"|\")\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "##Findings \n",
+ "#### By looking at my data I have come to the conclusion that a lower speed limit is generally better in normal mode on this road because otherwise the cars will group up too much trying to match the car in fronts speed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "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": "3.4.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Traffic Simulation Plots.ipynb b/Traffic Simulation Plots.ipynb
new file mode 100644
index 0000000..a8fd18d
--- /dev/null
+++ b/Traffic Simulation Plots.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "import statistics as st\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from simulation import Sim\n",
+ "from itertools import count\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "sim = Sim()\n",
+ "sim.time = 60\n",
+ "meta_data = sim.main()\n",
+ "def annoy(data):\n",
+ " x_list = []\n",
+ " y_list = []\n",
+ " speed = []\n",
+ " \n",
+ " x_list = x_list + [x for x,y,z in data]\n",
+ " y_list = y_list + [y for x,y,z in data]\n",
+ " speed = speed + [z for x,y,z in data]\n",
+ " return x_list, y_list, speed\n",
+ "x, y, speed = annoy(meta_data)\n",
+ "\n",
+ "average_speed = (st.mean(speed) * 60**2) / 1000\n",
+ "stan_dev = (st.stdev(speed) * 60**2) / 1000\n",
+ "s_limit = average_speed + stan_dev\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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bDhjr3phhrFiMMcaUigfvjTFmguDB+wlO5S45OlkHUfUajqqv9/O96/dJEf28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\nAX6b2/NJwL2FvM4oHG8i6Ux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kh5K6yl/MvYinSC5aIO3Jca6keyJiz/wVd3r+54Vky71V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\nJjeyB05gtQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.xlabel(\"Time in Seconds\\n\\n Average Speed: {} Kilometers per hour\\n \\\n",
+ " Suggested Speed Limit: {} Kilometers per hour \".format(average_speed, s_limit))\n",
+ "plt.ylabel(\"Distance\")\n",
+ "plt.title(\"Traffic Flow on 1 km Road\")\n",
+ "colors = [np.random.choice([\"blue\", \"green\"]) for _ in x]\n",
+ "plt.scatter(y, x, s=1, c=colors, marker=\"|\")\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "##Findings \n",
+ "#### By looking at my data I have come to the conclusion that a lower speed limit is generally better in normal mode on this road because otherwise the cars will group up too much trying to match the car in fronts speed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "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": "3.4.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/cars.py b/cars.py
new file mode 100644
index 0000000..7cdaa8d
--- /dev/null
+++ b/cars.py
@@ -0,0 +1,35 @@
+import random
+from road import Road
+
+class Car:
+
+ def __init__(self, start_position, length=5, max_speed=33, road=Road() ):
+ self.max_speed = max_speed # speed in m / s
+ self.speed = 0 # m / s
+ self.length = length # in meters
+ self.position = start_position
+ self.pre_pos = 0
+ self.road = road
+ self.index_position = 0
+
+ def accelerate(self):
+ # accelerate 2 m/s
+ if self.speed < self.max_speed - 1:
+ self.speed += 2
+
+ def decelerate(self):
+ if self.speed > 2:
+ self.speed -= 2
+
+ def rand_slow(self):
+ # cars have a 10% chance of randomly slowing down by 2 m/s
+ if random.random() <= .1 and self.speed > 2:
+ self.decelerate()
+
+ def move(self):
+ new_point = self.position + self.speed
+ self.pre_pos = self.position
+ if new_point <= self.road.length:
+ self.position = new_point
+ elif new_point >= self.road.length:
+ self.position = new_point - self.road.length
diff --git a/requirements.txt b/requirements.txt
index 8edbdf1..336b823 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,19 @@
-ipython[notebook]
-matplotlib
+gnureadline==6.3.3
+ipython==3.2.1
+Jinja2==2.7.3
+jsonschema==2.5.1
+MarkupSafe==0.23
+matplotlib==1.4.3
+mistune==0.7
+nose==1.3.7
+numpy==1.9.2
+ptyprocess==0.5
+Pygments==2.0.2
+pyparsing==2.0.3
+python-dateutil==2.4.2
+pytz==2015.4
+pyzmq==14.7.0
+six==1.9.0
+terminado==0.5
+tornado==4.2.1
+wheel==0.24.0
diff --git a/road.py b/road.py
new file mode 100644
index 0000000..3a0f868
--- /dev/null
+++ b/road.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+class Road:
+ # road is only one lane so there only needs to be a one dimensional array
+ def __init__(self, length=1000):
+ self.length = length # length in meters
+ self.road_state = np.zeros(self.length, int)
\ No newline at end of file
diff --git a/simulation.py b/simulation.py
new file mode 100644
index 0000000..b5ba198
--- /dev/null
+++ b/simulation.py
@@ -0,0 +1,73 @@
+from road import Road
+from cars import Car
+
+# cars take a position argument
+# simulation takes a road argument
+
+class Sim:
+
+ def __init__(self, road=Road(), cars_count=30):
+ self.cars_count = cars_count
+ self.road_state = road.road_state
+ self.cars_on_road = [] # contains all the cars on the road
+ self.time = 60 # time in seconds
+ self.tick = 1
+ self.vehicle_data = []
+ self.speed_data = []
+ self.collisions = 0
+
+ def car_factory(self):
+ # * after initial code is finished come back here and design the simulation to
+ # * account for car length
+ init_location = 0
+ my_location = 0
+ for num in range(self.cars_count):
+ init_location = init_location + 33
+ f_car = Car(init_location) # you need to pass in a position from road state
+ f_car.index_position = my_location
+ f_car.rand_slow()
+ self.cars_on_road.append(f_car)
+ my_location += 1
+
+ def update(self, vehicle):
+ # updates the current state of the road i.e.(changes the index position of cars from 0 to 1
+ self.road_state[vehicle.pre_pos:vehicle.pre_pos + vehicle.speed] = 0
+ self.road_state[vehicle.position:vehicle.position + vehicle.length] = 1
+ self.min_distance_between_car(vehicle)
+
+ def min_distance_between_car(self, object):
+ # location >= int(speed) in meters from car in front of it
+ # if another car is too close, car will match that cars speed
+ min_distance = object.speed + object.position
+ if not any(self.road_state[object.position:min_distance]) and object.speed < object.max_speed:
+ object.accelerate()
+ else:
+ if not any(self.road_state[object.position:min_distance]):
+ pass
+ elif object.index_position + 1 < self.cars_count:
+ new_speed = self.cars_on_road[object.index_position + 1].speed
+ object.speed = new_speed
+ elif object.index_position + 1 > self.cars_count:
+ new_speed = self.cars_on_road[1].speed
+ object.speed = new_speed
+
+
+ def traffic(self):
+ while self.time > 0:
+ #temp_data = []
+ for vehicle in self.cars_on_road:
+ self.vehicle_data.append((self.tick, vehicle.position, vehicle.speed))
+ self.min_distance_between_car(vehicle)
+ vehicle.rand_slow()
+ vehicle.move()
+ self.update(vehicle)
+ #self.vehicle_data.append(temp_data)
+ self.tick += 1
+ self.time -= 1
+
+ def main(self):
+ self.car_factory()
+ self.traffic()
+ return self.vehicle_data
+
+