Sorting algorithms
==================
[](https://github.com/egor-tensin/sorting-algorithms/actions/workflows/ci.yml)
Getting the hang out of (sorting) algorithms.
See also https://tensin.name/sorting-algorithms/.
Prerequisites
-------------
* Python 3.4 or higher
* [matplotlib]
* [numpy] (required by [matplotlib])
[matplotlib]: http://matplotlib.org/
[numpy]: http://www.numpy.org/
Algorithms
----------
Each of the implemented sorting algorithms resides in a separate Python module
(in the `algorithms.impl` package).
The implemented algorithms are listed below.
| Module name | Description
| ---------------- | --------------
| `bubble_sort` | Bubble sort
| `heapsort` | Heapsort
| `insertion_sort` | Insertion sort
| `median` | Median value
| `merge_sort` | Merge sort
| `quicksort` | Quicksort
| `selection_sort` | Selection sort
Some algorithms actually come in different variants.
For example, the implementation of quicksort includes a number of versions
depending on how the pivot element is chosen, be it the first, the second, the
middle, the last or a random element of the sequence.
Testing
-------
You can test each of the algorithms above by passing a sequence of integer
numbers to the corresponding script.
```
> python -m algorithms.impl.heapsort 5 3 4 1 2
[1, 2, 3, 4, 5]
```
```
> python -m algorithms.impl.quicksort 5 3 4 1 2
[1, 2, 3, 4, 5]
[1, 2, 3, 4, 5]
[1, 2, 3, 4, 5]
[1, 2, 3, 4, 5]
[1, 2, 3, 4, 5]
```
You can use "test.py" to quickly generate an input list of some kind and
display the result of executing one of the implemented algorithms.
Consult the output of `test.py --help` to learn how to use the script.
```
> ./test.py --input best --length 1000 median_heaps
499.5
```
```
> ./test.py --input worst --length 10 quicksort_random
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
```
Plotting
--------
You can generate similar plots you might've seen at
https://tensin.name/sorting-algorithms/ using "plot.py".
Consult the output of `plot.py --help` to learn how to use the script.
```
> ./plot.py merge_sort --min 0 --max 200 --input best --iterations 1000
```
```
> ./plot.py median_sorting --min 0 --max 200 --input average --iterations 100 --output median_sorting.png
```
If you're having problems using the script (like having excessive noise in the
measurement results), try minimizing background activity of your OS and
applications.
For example, on Windows 8.1 I got very reasonable plots after booting into Safe
Mode and running the script with a higher priority while also setting its CPU
affinity:
```
> start /affinity 1 /realtime plot.py ...
```
License
-------
Distributed under the MIT License.
See [LICENSE.txt] for details.
[LICENSE.txt]: LICENSE.txt
