diff --git a/README.md b/README.md
index b5c2779..c17c7bc 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ pip install -r requirements.txt
 
 **Step 3: Run the Application**
 ```bash
-python src/lidar.py
+python src/main.py
 ```
 
 ## License
diff --git a/src/utils/lidar.py b/src/utils/lidar.py
index 4f39a1d..b33e774 100644
--- a/src/utils/lidar.py
+++ b/src/utils/lidar.py
@@ -1,83 +1,53 @@
 import numpy as np
-import matplotlib.pyplot as plt
 from math import floor
 from adafruit_rplidar import RPLidar
-from sklearn.cluster import DBSCAN
-import matplotlib
+
 PORT_NAME = "COM4"
 lidar = RPLidar(None, PORT_NAME, timeout=3)
-
 max_distance = 0
 
-def find_obstacle_distance(data, anglemin, anglemax):
+def find_obstacle_distance(data :list[int], anglemin: int, anglemax:int) -> int:
+    """A function that finds the distance to the closest obstacle in a certain angle range.
+
+    :param data: the data from the lidar
+    :param anglemin: the minimum angle to check
+    :param anglemax: the maximum angle to check
+
+    :return: the distance to the closest obstacle
+    """
     point = 15000
     for i in range(anglemin, anglemax):
         if point > data[i] > 500:
             point = data[i]
     return point
-
-def detect_walls(data):
-    """A function used to detect the walls around the car
-    to the right of the car, there will be a straight line with a hole in it, detect this line
-    """
-    angles = np.linspace(0, 2 * np.pi, len(data), endpoint=False)
-    coordinates = [(angle, distance) for angle, distance in zip(angles, data)]
-
-    # Voer DBSCAN clustering uit op de coördinaten
-    dbscan = DBSCAN(eps=eps, min_samples=min_samples)
-    dbscan.fit(coordinates)
-    # Verzamel de clusterpunten die als muren worden beschouwd
-    wall_points = [coordinates[i] for i in range(len(coordinates)) if dbscan.labels_[i] != -1]
-
-    return wall_points
-
-
-def display_minimum(data):
-    #find the point on 0:40 and on 320:360
-    point = min(data[0:40])
-    pointleft = min(data[320:360])
-    if point < pointleft:
-        point = pointleft
-    if point > 500:
-        plt.plot(point,0, "ro")  # Plot het eerste punt in het rood
-    leftpoint = min(data[45:120])
-    rightpoint = min(data[225:320])
-    if leftpoint > 500:
-        plt.plot(0, leftpoint, "ro")
-    if rightpoint > 500:
-        plt.plot(0, -rightpoint, "ro")
-
-def right_free(data) -> bool:
-    """A function that checks if the right side of the car is free
+def right_free(data: list[int]) -> bool:
+    """A function that checks if the right side of the car is free.
 
     :param data: the data from the lidar
     :return: True if the right side is free, False otherwise
     """
-    for i in range(20,120):
-        if data[i] < 2500:
-            return False
-
     if min(data[20:120]) < 2500:
-        if data[i+1] < 2500 or data[i-1] < 2500:
-            return False
+        for i in range(20, 120):
+            if data[i] < 2500 and(data[i+1] < 2500 or data[i-1] < 2500):
+                return False
     return True
 
-def free_side(data, anglemin, anglemax) -> bool:
-    """A function that checks if the right side of the car is free
+def free_side(data: list[int], anglemin: int, anglemax: int) -> bool:
+    """A function that checks if the side between anglemin and anglemax of the car is free.
 
     :param data: the data from the lidar
     :param side: the side to check, either "right" or "left"
     :return: True if the side is free, False otherwise
     """
-    for i in range(anglemin, anglemax):
-        if data[i] < 2500 and (data[i+1] < 2500 or data[i-1] < 2500):
-            return False
+    return all(not (data[i] < 2500 and (data[i + 1] < 2500 or data[i - 1] < 2500)) for i in range(anglemin, anglemax))
 
-        return True
+def data_processing(data: list[int]) -> list[int]:
+    """A function that processes the data from the lidar.
 
-def process_data(data):
-    matplotlib.use("TkAgg")
-    angles = np.linspace(0, 2*np.pi, len(data), endpoint=False)
+    :param data: the data from the lidar
+
+    :return: the processed data
+    """
     distances = np.array(data)
     newdistances = distances.copy()
     for i in range(len(distances) - 1):
@@ -86,25 +56,7 @@ def process_data(data):
 
         if np.abs(distances[i] - distances[i + 1]) > 1500:
             newdistances[i] = np.nan
-
-    plt.plot(find_obstacle_distance(newdistances, 0,40), 0, "ro")
-    plt.plot()
-    # Omzetten naar cartesische coördinaten
-    x = newdistances * np.cos(angles)
-    y = newdistances * np.sin(angles)
-    #detect_lines(newdistances)
-    plt.clf()  # Wis de plot
-    plt.scatter(x, y, s=5)  # s is de grootte van de punten
-    plt.title("2D Lidar")
-    plt.xlabel("X")
-    plt.ylabel("Y")
-    display_minimum(newdistances)
-    # print(right_free(newdistances))
-    print(free_side(newdistances,60, 120))
-    plt.xlim(-1000, 10000)
-    plt.ylim(-2000,2000)
-    plt.grid(True)
-    plt.pause(0.01)
+    return newdistances
 
 try:
     scan_data = [0]*360
@@ -112,8 +64,9 @@ def process_data(data):
         scan_data = [0]*360
         for (_, angle, distance) in scan:
             scan_data[min([359, floor(angle)])] = distance
-        process_data(scan_data)
+        processed_data = data_processing(scan_data)
+
 except KeyboardInterrupt:
-    print("Stopping.")
+    lidar.stop()
 lidar.stop()
 lidar.disconnect()