Introduction to Programming

Introduction to Programming Course Overview

Course Introduction

  • Objective: At the end of this lecture, students will understand:

    • What the course encompasses

    • Class schedules

    • Resources for additional help

    • Importance of learning programming

    • Course teaching methodology

    • Executing a simple Python program

    • Adjusting a pendulum on a grandfather clock

Welcome and Introductions

  • Teaching Staff:

    • Prof. Kathryn Kasmarik

    • Dr. Yangyang Shu

    • Dr. Ali Ahrari

    • Wasura Wattearachchi

    • Reda Ghanem

    • Maryam Shahsavari

    • Mohammad Askarin

  • Other Contacts:

    • FLTLT Ryan Locke (Casual address with first names encouraged)

Course Timetable

  • Structure:

    • Lectures: Two per week focused on programming concepts

    • Tutorials: One per week aimed at practicing program/algorithm design and testing. Tutorials will form the basis of the exam.

    • Labs: One per week for coding practice, particularly with Sphero BOLT+ robots. Lab work correlates with Assignments 1 and 2.

Class Expectations

  • In Lectures:

    • Students encouraged to ask questions by raising hands

    • Talking amongst peers discouraged due to room acoustics

  • In Tutorials:

    • Discussion is permissible as long as it does not interrupt tutor explanations

  • In Labs:

    • Collaboration and conversation encouraged but eating is not allowed inside labs. Kitchen in B13 available for eating needs.

Resources Available

  • Moodle Platform:

    • Lecture slides, lab exercises (which are also Assignments 1 and 2), tutorial exercises, and solutions

  • Course Textbook:

    • An Introduction to Programming Using Python by Schneider

  • Supplemental Reading:

    • Systems Analysis and Design With UML by Dennis, Wixom, and Tegarden

Learning Outcomes

  • Students will learn:

    • Solving real-world problems with computers

    • Designing effective programs

    • Constructing programs in Python

    • Documenting code thoroughly

    • Testing methodologies

    • Gaining confidence to experiment with programming

    • Being self-directed in their learning

    • Following a structured approach to experimentation

Purpose of Learning Programming

  • The automation in various industries highlights:

    • The significance of understanding computer systems’ strengths and limitations, particularly in defense

    • Programming skills enhance employability across diverse job markets, including defense and beyond

    • Programming teaches:

    • Structured problem solving

    • Systematic testing approaches and troubleshooting strategies

Teaching Methodology

  • Problem-Based Learning:

    • Content framed around engineering/computer science problems

    • Labs will utilize the Sphero BOLT+ robot for practical applications

  • Learning Style:

    • Top-down approach focusing first on high-level design, followed by detail-oriented implementation

    • Students have the autonomy to select specific details to explore

    • Practical applications will contextualize theoretical discussions

    • Ongoing feedback to cater to student needs and adapt course material

Assessments in the Course

  • Total of Four Assessments:

    • Assignment 1: Individual lab work with weekly submissions and feedback

    • Mid-session Test: Practical testing during lab sessions, including a practice test in Week 5

    • Assignment 2: Collaborative lab work where students design, build, and test programs; ongoing feedback provided

    • Final Exam: Mirrors the weekly tutorial structure

Use of ChatGPT in the Course

  • ChatGPT’s capabilities highlighted:

    • Effective for generating initial Python code

    • Caution against substituting personal learning with generated code

  • Recommendations for Use:

    • Leverage ChatGPT for specific inquiries (e.g., syntax questions)

    • Consult professors or tutors for understanding issues

  • Policy on Original Work:

    • Submissions of entire codes generated by ChatGPT are prohibited to ensure authentic learning and assessment.

Example Case Study: Adjusting a Grandfather Clock

  • Problem Identification:

    • The clock runs too slowly as it inaccurately measures seconds.

  • Steps to Develop a Solution:

    • Understand the problem in layman’s terms.

    • Draft a rough solution on paper.

    • Implement the solution in Python.

    • Test the implemented solution.

  • Key Learning Outcome:

    • This cycle from problem identification to testing is vital for course success.

Pendulum Mechanics Overview

  • Force Dynamics:

    • A pendulum bob experiences a restoring force due to gravity (-mg sin(θ)). The torque affecting the pendulum can be expressed as:

    • au=−L(mgextsinheta)au = -L(mg ext{ sin } heta)

    • Where:

    • $ au$ = torque

    • $L$ = length of the pendulum string

    • $m$ = mass of the bob

    • $g$ = acceleration due to gravity

  • Equation of Motion:

    • Newton's second law gives:

    • Iracd2hetadt2=−L(mgextsinheta)I rac{d^2 heta}{dt^2} = -L(mg ext{ sin } heta)

    • For small angles (less than 15°), the approximation is:

    • extsinhetaext≈hetaext{ sin } heta ext{ ≈ } heta

    • Resulting in:

    • racd2hetadt2=−racgLhetarac{d^2 heta}{dt^2} = - rac{g}{L} heta

  • Form of Simple Harmonic Motion:

    • Determines the angular frequency as:

    • extAngularFrequency(rachetaT)=rac2extπextPeriodText{ Angular Frequency } ( rac{ heta}{T}) = rac{2 ext{π}}{ ext{Period } T}

    • Calculated using:

    • T=2extπext√racLgT = 2 ext{π} ext{√} rac{L}{g}

Developing a Rough Solution for the Pendulum

  • Measurement Steps:

    • Measure the current length of the pendulum and verify it is excessive.

    • Calculation for the required length for a 1-second tick:

    1. Rearrange period formula to solve for $L$ when $T = 1.0$ and $g = 9.8$:

      • L=racg(2extπ)2L = rac{g}{(2 ext{π})^2}

    • Adjust the physical length of the pendulum accordingly.

UML Class Design in Programming

  • Unified Modeling Language (UML):

    • A notation system for designing and illustrating code structures prior to coding.

  • Example of UML Structure:

    • SimplePendulum:

    • Attributes:

      • _length

      • _GRAVITY

    • Methods:

      • __init__(length, gravity)

      • calculate_period() → float

      • calculate_length(desired_period) → float

  • Key Learning Outcome: Understanding UML aids in organizing thoughts before coding.

Programming Implementation

  • Execution of Python Code:

    • Implementing the designed solution into Python effectively.

Assessing the Solution

  • Importance of Documentation and Comments:

    • Note on solutions generated by ChatGPT might lack thorough documentation.

    • Emphasis on adherence to Python naming conventions and variable significance.

  • Testing and Debugging:

    • An introduction to concepts will be provided in Week 2, focusing on validating the solution such as adjustments needed on a grandfather clock.

Recap of Today’s Lecture

  • Topics Covered:

    • Introduction to running a basic Python program

    • Overview of the software lifecycle

    • Recap of Chapter 1 and preview of Chapters 2 and 4 topics:

    • Variables, data types (int, float, string)

    • Assignment syntax and formatted strings

    • Mathematical operators and built-in methods $ ext{math.sqrt}$, print

    • Internal documentation through commenting, user-defined methods, and classes.

Summary of the Lecture

  • Key Takeaways:

    • Understanding the course context, expectations, resources, importance of programming, and foundational concepts such as demonstrating code in Python and the practical applications of theoretical knowledge in the context of programming.