Distance sensor code finished

Manual/4_Module_2.ipynb

@@ -325,13 +325,6 @@
     "- Also document the motor speed setting (e.g. use this as part of your file name)."
    ]
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "serial.close()"
-  },
   {
    "metadata": {},
    "cell_type": "code",
@@ -341,7 +334,6 @@
     "### Student Version ###\n",
     "from pathlib import Path\n",
     "\n",
-    "# TODO: Open the serial connection to KITT, set the motor speed\n",
     "serial = Serial('/dev/rfcomm3', 115200)\n",
     "serial.write(b'D150\\n')\n",
     "serial.write(b'M165\\n')\n",
@@ -360,8 +352,6 @@
     "    # Read the status response\n",
     "    status = serial.read_until(b'\\x04').decode('utf-8')\n",
     "    \n",
-    "    # TODO: Extract the distance values from the status response\n",
-    "\n",
     "    dist_L = None\n",
     "    dist_R = None\n",
     "\n",
@@ -377,8 +367,6 @@
     "            dist_R = int(words[5])\n",
     "            break\n",
     "\n",
-    "    # TODO: Record current time and distances\n",
-    "\n",
     "    current_time = time.time() - start_time\n",
     "    data.append([current_time, dist_L, dist_R])\n",
     "    \n",
@@ -396,7 +384,6 @@
     "# Close the serial connection\n",
     "serial.close()\n",
     "\n",
-    "# TODO: Write the recorded data to a CSV file\n",
     "filepath = Path('./kitt_wall_data_165.csv')\n",
     "df = pd.DataFrame(data,columns = [\"Time\",\"Distance_L\",\"Distance_R\"])\n",
     "df.to_csv(filepath,index=False)\n",
@@ -446,14 +433,15 @@
     "motor_speed_value = 160  # Use the same motor speed as during recording\n",
     "\n",
     "# Load the recorded data from the CSV file\n",
-    "csv_filename = f'Files/Recordings/kitt_distance_data_{motor_speed_value}.csv'\n",
+    "csv_filename = f'./kitt_wall_data_160.csv'\n",
     "data = pd.read_csv(csv_filename)\n",
     "\n",
     "# Consider discarding the first few readings (inaccurate readings)\n",
+    "data = data[2:]\n",
     "\n",
-    "# TODO: Remove duplicate time stamps and merge L and R data\n",
     "# Create a new DataFrame to hold your processed data\n",
     "merged_data = []\n",
+    "plotting_data = []\n",
     "\n",
     "# Iterate over the data\n",
     "for index, row in data.iterrows():\n",
@@ -462,16 +450,27 @@
     "    dist_L = row['Distance_L']\n",
     "    dist_R = row['Distance_R']\n",
     "    \n",
-    "    # TODO: Decide which distance to use or how to merge them\n",
-    "    distance = \n",
+    "    distance = min(dist_L,dist_R)\n",
     "\n",
     "    merged_data.append([time_stamp, distance])\n",
+    "    plotting_data.append([time_stamp, dist_L, dist_R])\n",
     "\n",
     "# Convert merged data to DataFrame\n",
     "merged_df = pd.DataFrame(merged_data, columns=['Time', 'Distance'])\n",
+    "plotting_df = pd.DataFrame(plotting_data, columns=['Time', 'Dist_L', 'Dist_R'])\n",
     "\n",
-    "# TODO: calculate velocity (change in distance over change in time)\n",
-    "merged_df['Velocity'] = \n",
+    "# Plotting the distance measured by the left and the right sensor\n",
+    "plt.figure()\n",
+    "plt.plot(plotting_df['Time'], plotting_df['Dist_L'], color='blue', label='Left sensor')\n",
+    "plt.plot(plotting_df['Time'], plotting_df['Dist_R'], color='red', label='Right sensor')\n",
+    "plt.xlabel('Time (s)')\n",
+    "plt.ylabel('Distance (cm)')\n",
+    "plt.title('Distance from the left and right sensor')\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "merged_df['Velocity'] = -merged_df['Distance'].diff().div(merged_df['Time'].diff())\n",
     "\n",
     "# Note: Use a negative sign because distance to the wall decreases as KITT moves forward\n",
     "\n",
@@ -497,7 +496,9 @@
     "plt.title('Velocity of KITT Over Time')\n",
     "plt.grid(True)\n",
     "plt.legend()\n",
-    "plt.show()"
+    "plt.show()\n",
+    "\n",
+    "print(merged_df)"
    ]
   },
   {