Python and Numpy

Things to keep in mind as a beginner programmer

  • Aim to write re-usable code

  • Write clean and documented code

  • Write code with no bugs

  • make it work, then make it right, then make it fast

We will be programming in Python for this course, this is a rapid fire primer to get started working in Python. Here is a list of resources to get started and understand the where python exists within the larger scope of programming languages and programming.

Python Origin - https://www.geeksforgeeks.org/history-of-python/

Data Types - https://www.w3schools.com/python/python_datatypes.asp

OOP: https://www.geeksforgeeks.org/python-oops-concepts/

Libraries in Python: https://www.geeksforgeeks.org/libraries-in-python/

Numpy Tutorial: https://www.w3schools.com/python/numpy/numpy_array_search.asp

Variable Naming: https://www.w3schools.com/python/python_variables_names.asp

Python is an interpreted language, all you need to know about within the scope of this course is that - “interpreted languages are easier to test and modify changes in code quickly, but may run more slower compared to compiled languages”

More on that: https://www.geeksforgeeks.org/difference-between-compiled-and-interpreted-language/

Writing Code For This Class

For your assignments and exercises you will be asked to write and submit Python scripts. You will be evaluated on how well the code runs and correctness, but a good percentage of your grade is dedicated to clean coding practices and proper documentation.

To ensure a smooth experience, we want you to work in virtual environments. We walk you through setting up a virtual environment here:

https://ringbuffer.org/dsp/Introduction/python-setup/

More info on environments and how they work can be found here: https://csguide.cs.princeton.edu/software/virtualenv#:~:text=A Python virtual environment

Python Tutorial

Activate your virtual environments

MAC/OS/ Linux

source dsp_venv/bin/activate

Windows

dsp_venv\Scripts\activate

There are many ways you can edit, run and read python code on your systems. We will work with VS Code and Spyder as the Integrated Development Environments to edit, navigate and debug our source code.

You can open VS code with your terminal, follow the steps here: https://www.geeksforgeeks.org/how-to-open-vs-code-using-terminal/

Coding

import numpy as np

# Create an array of integers
arr = np.array([1, 2, 3], dtype=np.int32)

# Create an array of floats
arr = np.array([1.2, 2.3, 3.4], dtype=np.float64)

# Convert an array to a different dtype
arr = arr.astype(np.int64)

# Creating an array of zeros
arrZeros = np.zeros(5)

#Creating an Array of Ones

arrOnes = np.ones(5)

# Creating mULTIDIMENSIONAL ARRAYS

arrOnesMulti = np.ones((5,2)) #5 rows 2 columns

# Creating an array with a ceratin range

rangeArray = np.arange(0, 10, 2) # Start 0 go to 10 jump in 2s

# Random array

randomArray = np.random.randint(10, size=(5, 3))

# Random array of floats (between 0 & 1)
np.random.random((5, 3))

# Set random seed to 0
np.random.seed(0)
# Make 'random' numbers
np.random.randint(10, size=(5, 3))
# Pseudo Random

# Slicing and Indexing - special array creation
x = np.arange(10)
print(x[2:4])

xLin = np.linspace(0, 100, 5) # Range, and how many in that range
print(xLin)

# Last element of the array

print(x[-1])

print(x[0:8:2]) # In steps

#Backwards Slice
print(x[4:2:-1])
print(x[::-1])

x = np.arange(5)
y = np.ones(5)
print(x+2*y)

# Sorting with Numpy

x = np.random.randn(5)
print(x)
x.sort()
print(x)

# Sorting and getting the rearranged indices

x = np.random.randn(5)
print(x)
ind = np.argsort(x)
print(ind)
print(x[ind])

# Iterating

arr = np.array([[1, 2, 3], [4, 5, 6]])

for x in arr:
  for y in x:
    print(y)

# Conditionals and search

arr = np.array([1, 2, 3, 4, 5, 4, 4])

x = np.where(arr == 4)