precommit

docs/courses/hello-world/1.5-data-logging.md

@@ -119,9 +119,9 @@ for _ in range(num_retries):
     txt = str(response.text)
     status_code = response.status_code
 
-     if status_code != 200:
-           print("Retrying in 5 seconds...")
-           time.sleep(5)
+    if status_code != 200:
+        print("Retrying in 5 seconds...")
+        time.sleep(5)
 
     print(f"Response: ({status_code}), msg = {txt}")
 
@@ -151,23 +151,31 @@ import json
 import pymongo
 import os
 
+
 def lambda_handler(event, context):
     try:
-        client = pymongo.MongoClient(os.environ['MONGODB_URI'])
+        client = pymongo.MongoClient(os.environ["MONGODB_URI"])
         db = client["your_database_name"]
         collection = db["your_collection_name"]
-        
-        body = json.loads(event['body'])
+
+        body = json.loads(event["body"])
         result = collection.insert_one(body)
-        
+
         return {
-            'statusCode': 200,
-            'body': json.dumps({'message': 'Document inserted successfully', 'id': str(result.inserted_id)})
+            "statusCode": 200,
+            "body": json.dumps(
+                {
+                    "message": "Document inserted successfully",
+                    "id": str(result.inserted_id),
+                }
+            ),
         }
     except Exception as e:
         return {
-            'statusCode': 500,
-            'body': json.dumps({'message': 'Error inserting document', 'error': str(e)})
+            "statusCode": 500,
+            "body": json.dumps(
+                {"message": "Error inserting document", "error": str(e)}
+            ),
         }
 ```
 
@@ -248,4 +256,4 @@ https://q.utoronto.ca/courses/350933/assignments/1274182?display=full_width
 https://q.utoronto.ca/courses/351407/assignments/1286935?display=full_width
 :::
 
-::::
+::::

docs/courses/hello-world/1.5.1-aws-lambda-read.json

@@ -141,4 +141,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/courses/hello-world/index.md

@@ -81,5 +81,3 @@ Begin
 ```
 
 <script async defer src="https://buttons.github.io/buttons.js"></script>
-
-

docs/courses/robotics/3.4-mobile-robotics.md

@@ -15,50 +15,50 @@ In this module, you will develop software to:
 
 ### Bill of Materials
 
-- [MyCobot 280 Pi: - World's Smallest and Lightest Six-Axis Collaborative Robot](https://shop.elephantrobotics.com/en-ca/collections/mycobot-280/products/mycobot-pi-worlds-smallest-and-lightest-six-axis-collaborative-robot)  
+- [MyCobot 280 Pi: - World's Smallest and Lightest Six-Axis Collaborative Robot](https://shop.elephantrobotics.com/en-ca/collections/mycobot-280/products/mycobot-pi-worlds-smallest-and-lightest-six-axis-collaborative-robot)
   A versatile and compact six-axis robot, ideal for mobile robotics applications.
 
-- [Camera Flange 2.0](https://shop.elephantrobotics.com/en-ca/collections/camera-modules/products/camera-flange-2-0)  
+- [Camera Flange 2.0](https://shop.elephantrobotics.com/en-ca/collections/camera-modules/products/camera-flange-2-0)
   Used for vision-based tasks in mobile robotics, such as object recognition and navigation.
 
-- [Adaptive Gripper](https://shop.elephantrobotics.com/en-ca/collections/grippers/products/adaptive-gripper)  
+- [Adaptive Gripper](https://shop.elephantrobotics.com/en-ca/collections/grippers/products/adaptive-gripper)
   A flexible gripper designed for precise manipulation and picking tasks in collaborative robotic systems.
 
-- [G-Shape Base 2.0](https://shop.elephantrobotics.com/en-ca/collections/fixed-bases/products/g-shape-base-2-0)  
+- [G-Shape Base 2.0](https://shop.elephantrobotics.com/en-ca/collections/fixed-bases/products/g-shape-base-2-0)
   Provides a sturdy mounting platform for the MyCobot, ensuring stability during robotic operations.
 
-- [LIDAR sensor for obstacle detection] 
+- [LIDAR sensor for obstacle detection]
   Used for real-time obstacle detection and mapping in mobile robotics.
 
 - Printed AprilTags (can be generated and printed from [AprilTag Generation](https://github.com/AprilRobotics/apriltag-generation))
 
-- USB-A to micro USB-B cable: 
+- USB-A to micro USB-B cable:
   Used to connect and power devices such as the Raspberry Pi or peripherals.
 
 - SD Card with Raspbian OS:
   Pre-loaded with the Raspbian OS for use with the Raspberry Pi to facilitate software installations and configurations.
 
 ### Required Software
 
-- [Prefect](https://www.prefect.io/)  
+- [Prefect](https://www.prefect.io/)
   A workflow orchestration tool used to manage and coordinate asynchronous tasks in the system.
 
-- [AprilTags Python Library](https://pypi.org/project/apriltag/)  
+- [AprilTags Python Library](https://pypi.org/project/apriltag/)
   A computer vision library for identifying and tracking AprilTags, used for spatial referencing and navigation.
 
-- [ROS2 Humble](https://docs.ros.org/en/humble/Installation.html)  
+- [ROS2 Humble](https://docs.ros.org/en/humble/Installation.html)
   ROS2 (Robot Operating System) is an open-source framework for building robot applications. **Humble Hawksbill** is the currently recommended version due to its stability and long-term support.
 
-- [Ubuntu 22.04 LTS](https://releases.ubuntu.com/22.04/)  
+- [Ubuntu 22.04 LTS](https://releases.ubuntu.com/22.04/)
   Ubuntu 22.04 LTS is the recommended Linux distribution for running ROS2, as it provides a stable and compatible environment. **Ubuntu 24.04** (the latest version) may encounter compatibility issues with ROS2 and other tools.
 
 
 ### Documentation
 
-- [MyCobot Pi Documentation](https://docs.elephantrobotics.com/docs/gitbook-en/2-serialproduct/2.1-280/2.1.2-PI.html)  
+- [MyCobot Pi Documentation](https://docs.elephantrobotics.com/docs/gitbook-en/2-serialproduct/2.1-280/2.1.2-PI.html)
   Detailed guide on setting up and operating the MyCobot Pi.
 
-- [Gripper Control via Python](https://docs.elephantrobotics.com/docs/gitbook-en/7-ApplicationBasePython/7.5_gripper.html)  
+- [Gripper Control via Python](https://docs.elephantrobotics.com/docs/gitbook-en/7-ApplicationBasePython/7.5_gripper.html)
   Guide for controlling the adaptive gripper using Python commands.
 
 
@@ -71,7 +71,7 @@ First, you will learn how to use ROS to control a mobile cobot. ROS is an open-s
 
 #### ROS2 Overview
 
-ROS2 (Robot Operating System 2) is an extension of the original ROS framework, designed to be modular and scalable for building complex robotic systems. It is composed of numerous **software packages**, which offer a wide range of functionalities like hardware abstraction, device control, message-passing, and software development tools. 
+ROS2 (Robot Operating System 2) is an extension of the original ROS framework, designed to be modular and scalable for building complex robotic systems. It is composed of numerous **software packages**, which offer a wide range of functionalities like hardware abstraction, device control, message-passing, and software development tools.
 
 ROS2 heavily relies on the **Ubuntu Linux** operating system, and it is recommended to use **Ubuntu 22.04 LTS** for stability and compatibility. One of ROS2’s key advantages is its distributed architecture, which allows different components (nodes) to communicate with each other over a network. This distributed nature is essential for enabling flexibility in robot systems development.
 
@@ -207,7 +207,7 @@ microscope = MicroscopeDemo(
 def detect_apriltag(frame):
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     tags = at_detector.detect(gray)
-    
+
     for tag in tags:
         if tag.tag_id == 1:  # Assuming we're looking for tag with ID 1
             return tag.center
@@ -230,13 +230,13 @@ def microscope_scan():
     microscope.focus()
     image = microscope.take_image()
     image.show()
-    
+
     print("Scanning area...")
     scan_images = microscope.scan([2000, 2000], [-2000, -2000])
     for i, img in enumerate(scan_images):
         img.show()
         print(f"Showing scan image {i+1}/{len(scan_images)}")
-    
+
     microscope.move(0, 0)
 
 def main():
@@ -254,7 +254,7 @@ def main():
                 move_arm_to_target(x, y)
                 grab_object()
                 print("Object grasped")
-                
+
                 # Perform microscope scan
                 microscope_scan()
                 break
@@ -334,56 +334,56 @@ import actionlib
 class MyCobotNavigator:
     def __init__(self):
         rospy.init_node('mycobot_navigator', anonymous=True)
-        
+
         # Initialize MyCobot
         self.mycobot = MyCobot("/dev/ttyUSB0", 115200)
-        
+
         # ROS Publishers
         self.cmd_vel_pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
         self.goal_pub = rospy.Publisher('/move_base_simple/goal', PoseStamped, queue_size=10)
-        
+
         # ROS Subscribers
         rospy.Subscriber('/camera/rgb/image_raw', Image, self.image_callback)
         rospy.Subscriber('/camera/depth/points', PointCloud2, self.pointcloud_callback)
         rospy.Subscriber('/map', OccupancyGrid, self.map_callback)
         rospy.Subscriber('/detected_objects', DetectedObjectsArray, self.object_detection_callback)
-        
+
         # Initialize CV Bridge
         self.bridge = CvBridge()
-        
+
         # Initialize MoveBase Action Client
         self.move_base_client = actionlib.SimpleActionClient('move_base', MoveBaseAction)
         self.move_base_client.wait_for_server()
-        
+
         # Initialize TF listener
         self.tf_listener = tf.TransformListener()
-        
+
         # State variables
         self.current_image = None
         self.current_pointcloud = None
         self.current_map = None
         self.detected_objects = []
         self.navigation_goal = None
-        
+
     def image_callback(self, msg):
         try:
             self.current_image = self.bridge.imgmsg_to_cv2(msg, "bgr8")
             self.process_image()
         except Exception as e:
             rospy.logerr(f"Error processing image: {e}")
-    
+
     def pointcloud_callback(self, msg):
         self.current_pointcloud = msg
         self.process_pointcloud()
-    
+
     def map_callback(self, msg):
         self.current_map = msg
         self.update_navigation_plan()
-    
+
     def object_detection_callback(self, msg):
         self.detected_objects = msg.objects
         self.process_detected_objects()
-    
+
     def process_image(self):
         if self.current_image is not None:
             # Perform visual navigation tasks
@@ -395,54 +395,54 @@ class MyCobotNavigator:
                 for line in lines:
                     x1, y1, x2, y2 = line[0]
                     cv2.line(self.current_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            
+
             cv2.imshow("Navigation View", self.current_image)
             cv2.waitKey(1)
-    
+
     def process_pointcloud(self):
         if self.current_pointcloud is not None:
             # Process pointcloud for obstacle detection
             # This is a placeholder for actual pointcloud processing
             rospy.loginfo("Processing pointcloud for obstacle detection")
-    
+
     def process_detected_objects(self):
         for obj in self.detected_objects:
             rospy.loginfo(f"Detected object: {obj.label} at position: {obj.pose.position}")
             # Implement logic to interact with detected objects
             if obj.label == "target_object":
                 self.plan_path_to_object(obj.pose)
-    
+
     def plan_path_to_object(self, object_pose):
         goal = MoveBaseGoal()
         goal.target_pose.header.frame_id = "map"
         goal.target_pose.header.stamp = rospy.Time.now()
         goal.target_pose.pose = object_pose
-        
+
         self.move_base_client.send_goal(goal)
         rospy.loginfo("Sent goal to move_base")
-    
+
     def update_navigation_plan(self):
         if self.navigation_goal is not None:
             self.plan_path_to_goal(self.navigation_goal)
-    
+
     def plan_path_to_goal(self, goal_pose):
         start_pose = PoseStamped()
         start_pose.header.frame_id = "base_link"
         start_pose.header.stamp = rospy.Time.now()
-        
+
         try:
             transformed_pose = self.tf_listener.transformPose("map", start_pose)
             # Here you would typically call a path planning service
             # For this example, we'll just log the intent
             rospy.loginfo(f"Planning path from {transformed_pose.pose.position} to {goal_pose.pose.position}")
         except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException) as e:
             rospy.logerr(f"TF Error: {e}")
-    
+
     def move_arm_to_target(self, x, y, z):
         # Move MyCobot arm to target position
         self.mycobot.send_coords([x, y, z, 0, 0, 0], 50, 1)  # Adjust speed and mode as needed
         rospy.loginfo(f"Moving arm to coordinates: ({x}, {y}, {z})")
-    
+
     def run(self):
         rate = rospy.Rate(10)  # 10 Hz
         while not rospy.is_shutdown():
@@ -485,25 +485,29 @@ from prefect import task, Flow
 import time
 import random
 
+
 @task
 def control_robot():
     for _ in range(10):
         print("Moving the robot forward...")
         time.sleep(1)
 
+
 @task
 def monitor_sensors():
     for _ in range(10):
         sensor_value = random.uniform(0.0, 2.0)  # Simulating sensor data
         print(f"LIDAR sensor reading: {sensor_value}")
         time.sleep(1)
 
+
 @task
 def analyze_data():
     for _ in range(10):
         print("Analyzing real-time data...")
         time.sleep(1)
 
+
 with Flow("Asynchronous Robotics Control") as flow:
     control_robot()
     monitor_sensors()
@@ -532,7 +536,7 @@ To use this script you need to:
 
 ### Define Asynchrony in the Context of Hardware Control
 
-Asynchrony in robotics refers to executing tasks independently and concurrently. In the context of hardware control, this is critical for managing complex processes in an autonomous laboratory, where sensors must continually gather data, robots must execute movement commands, and the system needs to react in real-time. 
+Asynchrony in robotics refers to executing tasks independently and concurrently. In the context of hardware control, this is critical for managing complex processes in an autonomous laboratory, where sensors must continually gather data, robots must execute movement commands, and the system needs to react in real-time.
 
 For instance, while a robot is moving, it must be able to continuously monitor its surroundings (e.g., using LIDAR or cameras) and adjust its trajectory without pausing or waiting for other tasks to complete.
 
@@ -593,14 +597,14 @@ bridge = CvBridge()
 def image_callback(msg):
 # Convert ROS image messages to OpenCV images
 img = bridge.imgmsg_to_cv2(msg, "bgr8")
-    
+
 # Use OpenCV to detect pill bottles (e.g. by color or shape)
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
 
 # Use OpenCV contour detection for medicine bottles
 contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    
+
 for contour in contours:
 x, y, w, h = cv2.boundingRect(contour)
 if w > 30 and h > 30: # Assume the size of the bottle is within this range
@@ -650,11 +654,11 @@ def move_robot():
 rospy.init_node('robot_control_node')
 pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
 rate = rospy.Rate(10)
-    
+
 twist = Twist()
 twist.linear.x = 1.0 # Move forward
 twist.angular.z = 0.5 # Rotation
-    
+
 while not rospy.is_shutdown():
 pub.publish(twist)
 rate.sleep()