Running one of the scripts in this project generates primitive meshes in Blender, wich are animated to visualize various sorting algorithms.
The three folders (sort_color, sort_combined, sort_scale) contain three different types of visualisation.

sort_color: 2D array of planes arragned into a square sorted based on color
sort_combined: multiple 2D arrays of planes arragned into a cube sorted based on color
sort_scale: array of cuboids sorted based on height + array access and comparison counter

Getting Started

Download, install and start Blender.
Open the .py file in the Text Editor.
Click the play button to run the script.

Possible Updates

Below I compiled a list of features that could be implemented in the future.

increase efficienty of the setup_array() function, to allow greater object count
adding audio “Audibilization”
adding more sorting algorithms
adding more types of visualisations e.g. Sphere Agitation, Cube Amalgam, Dynamic Hoops
auto generate camera with correct transforms based on the count of sorted objects
create panel were you can choose different options like colors, sorting algorithms and count of objects
improve merge sort visualisation so there are no gaps and overlapping objects

Contributions to this project with either ideas from the list or your own are welcome.

Sorting Algorithms

Bubble Sort

Bubble sort is one of the most straightforward sorting algorithms, it makes multiple passes through a list.

Starting with the first element, compare the current element with the next element of the array.
If the current element is greater than the next element of the array, swap them.
If the current element is less than the next element, move to the next element.

In essence, each item “bubbles” up to the location where it belongs.

bubble_sort_scale.py	bubble_sort_color.py

Insertion Sort

Like bubble sort, the insertion sort algorithm is straightforward to implement and understand.

Iterate from arr[1] to arr[n] over the array.
Compare the current element (key) to its predecessor.
If the key element is smaller than its predecessor, compare its elements before.
Move the greater elements one position up to make space for the swapped element.

It splits the given array into sorted and unsorted parts, then the values from the unsorted parts are picked and placed at the correct position in the sorted part.

insertion_sort_scale.py	insertion_sort_color.py

Selection Sort

The selection sort algorithm sorts an array by repeatedly finding the minimum element (considering ascending order) from unsorted part and putting it at the beginning.
The algorithm maintains two subarrays in a given array.

The subarray which is already sorted.
Remaining subarray which is unsorted.

In every iteration of selection sort, the minimum element (considering ascending order) from the unsorted subarray is picked and moved to the sorted subarray.

selection_sort_scale.py	selection_sort_color.py

Heap Sort

heap_sort_scale.py	heap_sort_color.py

Shell Sort

The shell sort algorithm extends the insertion sort algorithm and is very efficient in sorting widely unsorted arrays.
The array is divided into sub-arrays and then insertion sort is applied.
The algorithm is:

Calculate the value of the gap.
Divide the array into these sub-arrays.
Apply the insertion sort.
Repeat this process until the complete list is sorted.

This sorting technique works by sorting elements in pairs, far away from each other and subsequently reduces their gap.
The gap is known as the interval. We can calculate this gap/interval with the help of Knuth’s formula.

shell_sort_scale.py	shell_sort_color.py

Merge Sort

Merge sort uses the divide and conquer approach to sort the elements. It is one of the most popular and efficient sorting algorithm.
It divides the given list into two equal halves, calls itself for the two halves and then merges the two sorted halves.
We have to define the merge() function to perform the merging.

The sub-lists are divided again and again into halves until the list cannot be divided further.
Then we combine the pair of one element lists into two-element lists, sorting them in the process.
The sorted two-element pairs is merged into the four-element lists, and so on until we get the sorted list.

merge_sort_scale.py	merge_sort_color.py

Quick Sort

Like Merge Sort, QuickSort is a Divide and Conquer algorithm. It picks an element as pivot and partitions the given array around the picked pivot. There are many different versions of quickSort that pick pivot in different ways.

Always pick first element as pivot.
Always pick last element as pivot.
Pick a random element as pivot.
Pick median as pivot. (implemented below)

The key process in quickSort is partition(). Target of partitions is, given an array and an element x of array as pivot, put x at its correct position in sorted array and put all smaller elements (smaller than x) before x, and put all greater elements (greater than x) after x.
All this should be done in linear time.

quick_sort_scale.py	quick_sort_color.py

Big O

What is Big O Notation?

Big O notation is the language we use for talking about how long an algorithm takes to run.
It’s how we compare the efficiency of different approaches to a problem.
With Big O notation we express the runtime in terms of how quickly it grows relative to the input, as the input gets arbitrarily large.

Let’s break that down:

how quickly the runtime grows
It’s hard to pin down the exact runtime of an algorithm.
It depends on the speed of the processor, what else the computer is running, etc. So instead of talking about the runtime directly, we use big O notation to talk about how quickly the runtime grows.
relative to the input
If we were measuring our runtime directly, we could express our speed in seconds.
Since we’re measuring how quickly our runtime grows, we need to express our speed in terms of…something else.
With Big O notation, we use the size of the input, which we call “n”.
So we can say things like the runtime grows “on the order of the size of the input” (O(n)) or “on the order of the square of the size of the input” (O(n^2)).
as the input gets arbitrarily
Our algorithm may have steps that seem expensive when “n” is small but are eclipsed eventually by other steps as “n” gets huge.
For big O analysis, we care most about the stuff that grows fastest as the input grows, because everything else is quickly eclipsed as “n” gets very large.

Time Complexity Notations

These are the asymptotic notations that are used for calculating the time complexity of the sorting algorithms:

Big O Notation, O:

It measures the upper limit of an algorithm’s running time or the worst-case time complexity. It is known by O(n) for input size ‘n’.

Omega Notation, Ω:

It measures the minimum time taken by algorithms to execute, and calculates the best case time complexity in sorting. It is known by Ω(n) for input size ‘n’.

Theta Notation, θ:

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Table of Sorting Algorithms

Algorithm	Time Complexity			Space Complexity
	Best Case	Average Case	Worst Case	Worst Case
Quick Sort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n^2)`	`O(log(n))`
Merge Sort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n log(n))`	`O(n)`
Tim Sort	`Ω(n)`	`Θ(n log(n))`	`O(n log(n))`	`O(n)`
Heap Sort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n log(n))`	`O(1)`
Bubble Sort	`Ω(n)`	`Θ(n^2)`	`O(n^2)`	`O(1)`
Insertion Sort	`Ω(n)`	`Θ(n^2)`	`O(n^2)`	`O(1)`
Selection Sort	`Ω(n^2)`	`Θ(n^2)`	`O(n^2)`	`O(1)`
Tree Sort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n^2)`	`O(n)`
Shell Sort	`Ω(n log(n))`	`Θ(n(log(n))^2)`	`O(n(log(n))^2)`	`O(1)`
Bucket Sort	`Ω(n+k)`	`Θ(n+k)`	`O(n^2)`	`O(n)`
Radix Sort	`Ω(nk)`	`Θ(nk)`	`O(nk)`	`O(n+k)`
Counting Sort	`Ω(n+k)`	`Θ(n+k)`	`O(n+k)`	`O(k)`
Cube Sort	`Ω(n)`	`Θ(n log(n))`	`O(n log(n))`	`O(n)`

README.md Unescape Escape

Sorting-Algorithms-Blender

Table of contents

Introduction

Description