diff --git a/hyperdrive_json/hyperdrive_json.ipynb b/hyperdrive_json/hyperdrive_json.ipynb
index c2c1557..1aa6d97 100644
--- a/hyperdrive_json/hyperdrive_json.ipynb
+++ b/hyperdrive_json/hyperdrive_json.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7b9ba26a11e45c2199fa470220229e57416263e68ddf70420a48ee2b78051d28"
+  "signature": "sha256:0d99bab4f5de75a370e23272deeaa65b75d099ab8d1a96221c79333980014fe7"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -52,7 +52,47 @@
       "'battery_soc': 44\n",
       "}\n",
       "car2_run0_dict = makeDictOfArraysFromLists(car2_run0, names)\n",
-      "print(car2_run0_dict)\n"
+      "print(car2_run0_dict.keys())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#print(car2_run0_dict['brake_pedal'])\n",
+      "def int_OFF_ON(str):\n",
+      "    if str == 'OFF':\n",
+      "        return 0\n",
+      "    elif str == 'ON':\n",
+      "        return 1\n",
+      "    return -1\n",
+      "print(np.array([int_OFF_ON(str) for str in car2_run0_dict['brake_pedal']]))\n",
+      "print(np.array([int_OFF_ON(str) for str in car2_run0_dict['ev_mode']]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def int_PRNDB(str):\n",
+      "    if str == 'P':\n",
+      "        return 0\n",
+      "    elif str == 'R':\n",
+      "        return 1\n",
+      "    elif str == 'N':\n",
+      "        return 2\n",
+      "    elif str == 'D':\n",
+      "        return 3\n",
+      "    elif str == 'B':\n",
+      "        return 4\n",
+      "    return -1\n",
+      "print(np.array([int_PRNDB(str) for str in car2_run0_dict['at_shift']]))\n"
      ],
      "language": "python",
      "metadata": {},
diff --git a/hyperdrive_json/hyperdrive_json_belo.ipynb b/hyperdrive_json/hyperdrive_json_belo.ipynb
new file mode 100644
index 0000000..8cb360b
--- /dev/null
+++ b/hyperdrive_json/hyperdrive_json_belo.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pprint import pprint\n",
+    "\n",
+    "data_file = \"1481558375.249912.json\"\n",
+    "\n",
+    "def json_fix(filename):\n",
+    "    json_raw = open(filename, 'r').read()\n",
+    "    json_data = ''\n",
+    "    if json_raw[1] != '[' and json_raw[-2] == ',':\n",
+    "        json_data = '[' + json_raw[0:-2] + ']'\n",
+    "    return json.loads(json_data)\n",
+    "\n",
+    "data = json_fix(data_file)\n",
+    "\n",
+    "N = len(data)\n",
+    "time = np.zeros(N)\n",
+    "speed = np.zeros(N)\n",
+    "rThrottle = np.zeros(N)\n",
+    "aSteer = np.zeros(N)\n",
+    "BBrake = np.zeros(N, dtype=np.bool)\n",
+    "\n",
+    "for i, sample in enumerate(data):\n",
+    "    time[i] = sample[1]\n",
+    "    rThrottle[i] = sample[2]\n",
+    "    BBrake[i] = sample[3] \n",
+    "    aSteer[i] = sample[8]\n",
+    "    speed[i] = sample[13]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rBwRJT7NtJMvbkWos8kHBQgoj9B1u6OWT8OStxiIOqrFIn4KFFEa7O9xe75yy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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8b443c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time/1000, speed)\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W9ZfHUFXheh+uaQmIbPfOCaU7ppb8J90YOJB6RQNADaKllMFFIz5NDx7XquRv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\n1fPjZqsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7a369b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time/1000, rThrottle)\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e0H5Yku4Jn40i7SSSPGhg8Ti6B4IdQPe4aIAfVtWtSz6wXpLHAxcCa6rqjkWu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\n/zPna/sNMeJ7AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7c06208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time/1000, BBrake)\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QGT5cMyIiImlTEMkRFauWVzojEp3wbtiwrjMiHR0KIiIi9aQgkiOaESmvNIhE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\nAWoo/IuymZLkAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ac7320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time/1000, aSteer)\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [py27]",
+   "language": "python",
+   "name": "Python [py27]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/hyperdrive_json/plot_json.py b/hyperdrive_json/plot_json.py
new file mode 100644
index 0000000..922da9f
--- /dev/null
+++ b/hyperdrive_json/plot_json.py
@@ -0,0 +1,148 @@
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+from optparse import OptionParser
+from lists_dictionary import makeDictOfArraysFromLists
+
+parser = OptionParser()
+parser.add_option("-f", "--file", dest="file",
+                  help="write report to FILE", metavar="file")
+
+(options, args) = parser.parse_args()
+data_file = options.file
+
+def json_fix(filename):
+    json_raw = open(filename, 'r').read()
+    json_data = ''
+    if json_raw[1] != '[' and json_raw[-2] == ',':
+        json_data = '[' + json_raw[0:-2] + ']'
+    return json.loads(json_data)
+
+def int_OFF_ON(str):
+    if str == 'OFF':
+        return 0
+    elif str == 'ON':
+        return 1
+    return -1
+
+def int_PRNDB(str):
+    if str == 'P':
+        return 0
+    elif str == 'R':
+        return 1
+    elif str == 'N':
+        return 2
+    elif str == 'D':
+        return 3
+    elif str == 'B':
+        return 4
+    return -1
+
+names = {
+'time_ms': 1,
+'accel_pedal': 2,
+'brake_pedal': 3,
+'at_shift': 5,
+'steer_wheel': 8,
+'engine_rpm': 12,
+'speed_kph': 13,
+'accel_long': 14,
+'yaw_rate': 16,
+'odometer': 17,
+'fuel_mL': 18,
+'lat_deg': 24,
+'lon_deg': 26,
+'altitude': 30,
+'ev_mode': 43,
+'battery_soc': 44
+}
+
+data = json_fix(data_file)
+data_dict = makeDictOfArraysFromLists(data, names)
+
+time = data_dict['time_ms']/1000
+accel_pedal = data_dict['accel_pedal']
+steer_wheel = data_dict['steer_wheel']
+engine_rpm = data_dict['engine_rpm']
+speed_kph = data_dict['speed_kph']
+accel_long = data_dict['accel_long']
+yaw_rate = data_dict['yaw_rate']
+odometer = data_dict['odometer']
+fuel_mL = data_dict['fuel_mL']
+lat_deg = data_dict['lat_deg']
+lon_deg = data_dict['lon_deg']
+altitude = data_dict['altitude']
+battery_soc = data_dict['battery_soc']
+brake_pedal = np.array([int_OFF_ON(str) for str in data_dict['brake_pedal']])
+ev_mode = np.array([int_OFF_ON(str) for str in data_dict['ev_mode']])
+at_shift = np.array([int_PRNDB(str) for str in data_dict['at_shift']])
+
+plt.figure()
+ax1 = plt.subplot(3,2,1)
+plt.plot(time, steer_wheel, '-o')
+plt.ylabel('Steering Wheel Angle (deg)')
+plt.xlabel('Time (s)')
+
+ax2 = plt.subplot(3,2,3, sharex=ax1)
+plt.plot(time, accel_pedal, '-o')
+plt.ylabel('Accelerator Position (%)')
+plt.xlabel('Time (s)')
+
+ax3 = plt.subplot(3,2,5, sharex=ax1)
+plt.plot(time, brake_pedal, '-o')
+plt.ylabel('Brake Pedal Status (0-1)')
+plt.xlabel('Time (s)')
+
+ax4 = plt.subplot(2,2,2, sharex=ax1)
+plt.plot(time, at_shift, '-o')
+plt.ylabel('AT Shift Position (0-4)')
+plt.xlabel('Time (s)')
+
+ax5 = plt.subplot(2,2,4, sharex=ax1)
+plt.plot(time, ev_mode, '-o')
+plt.ylabel('EVM_MSG (0-1)')
+plt.xlabel('Time (s)')
+
+plt.figure()
+ax6 = plt.subplot(2,2,1, sharex=ax1)
+plt.plot(time, engine_rpm, '-o')
+plt.ylabel('Engine Speed (RPM)')
+plt.xlabel('Time (s)')
+
+ax7 = plt.subplot(2,2,2, sharex=ax1)
+plt.plot(time, speed_kph, '-o')
+plt.ylabel('Vehicle Speed (kph)')
+plt.xlabel('Time (s)')
+
+ax8 = plt.subplot(2,2,3, sharex=ax1)
+plt.plot(time, accel_long, '-o')
+plt.ylabel('Long Accel. (m/s2)')
+plt.xlabel('Time (s)')
+
+ax9 = plt.subplot(2,2,4, sharex=ax1)
+plt.plot(time, yaw_rate, '-o')
+plt.ylabel('Yaw-rate (deg/s)')
+plt.xlabel('Time (s)')
+
+plt.figure()
+ax10 = plt.subplot(2,2,1, sharex=ax1)
+plt.plot(time, odometer, '-o')
+plt.ylabel('Odometer (m)')
+plt.xlabel('Time (s)')
+
+ax11 = plt.subplot(2,2,2, sharex=ax1)
+plt.plot(time, fuel_mL, '-o')
+plt.ylabel('Fuel Consumption (mL)')
+plt.xlabel('Time (s)')
+
+ax12 = plt.subplot(2,2,3, sharex=ax1)
+plt.plot(time, battery_soc, '-o')
+plt.ylabel('Battery SOC (%)')
+plt.xlabel('Time (s)')
+
+ax13 = plt.subplot(2,2,4)
+plt.plot(lon_deg, lat_deg, '-o')
+plt.ylabel('Lat GPS (deg)')
+plt.xlabel('Long GPS (deg)')
+
+plt.show()