Physics 212, 2019: Lecture 3

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Back to Physics 212, 2019: Computational Modeling.

Most important thing to note about these lectures:

You won't learn Python by reading. You have to try coding. If you have a question how a certain expression works, type it in Python console, and test it!

Introduction to Python

• Algorithmic thinking
  • Example: opening a door
  • Different levels of algorithms
• Basic parts needed to design an algorithm
  • State (or memory)
  • Rules for transforming states (operations, functions, procedures)
  • Rules for making decisions
• Assignment operation (vs. testing for equality)
  • Variable is a pointer to a container (object) in memory where a state is stored. (This is not quite correct; we will return to this later).
• Anaconda distribution / Spyder development environment
  • How to launch it
  • Console vs. editor plus additional tools
  • Syntax highlighting / code analyzer
• Basic syntaxes
  • Resetting the state of the system
  • Asking for help with ? and with Google
  • Built-in functions, like print
  • Numbers; note that j is the imaginary unit, not i
    • Are numbers real or integer?
  • Arithmetic operations, +, -, *, /, **.
  • Parentheses
  • Importing modules and functions from modules
    • numpy as np and matplotlib.pyplot as plt
• Python Modules
  • Importing and reloading
  • pyplot and numpy

Exercise

We will solve a quadratic equation a*x**2+b*x+c=0 for a=1, b=2, c=-3. Do this by

1. Resetting the environment
2. Importing sqrt function from numpy
3. Assigning values to a, b, c
4. Evaluating both solutions using the standard formula you learned in middle school
5. Printing both solutions

• More syntaxes
  • Functions have arguments; keyword arguments
  • Functions return values
  • Functions can otherwise change Python state

Exercise

Evaluate a binary log (log base 2) of a number 42 using at least three different sets of commands. Verify your result by taking 2 to the appropriate power and seeing if you get 42 back.

• Objects -- mutable (arrays and lists) vs. immutable (numbers)
  • Object attributes and object methods, using dir()
  • Overloading methods
• Variables vs. objects (again!)
• Lists vs. Numpy arrays
  • Why np.zeros((2,4)) and not np.zeros(2,4)?
  • A list of lists, and and an array of arrays
• Creation, concatenation (stacking).
• Slicing -- doesn't create new arrays (we will come back to this)
• Flatten copies data, ravel and reshape does not
• Strings

Exercise

Create a rectangular 3x4 arrays of numbers. Flatten and ravel it. Which of the commands creates new arrays and which does not? Reshape it as a 4x3 array. Did this create a new array? Check in the variable explorer. Now create a slice that corresponds to the first and the second rows (remember that row numbering starts with 0) and zeroth through second columns. Did this create a new array?

Don't forget to submit your work at the end of the class using Canvas.