Algorithm Analysis Figures

The following Python script (figures.py) was written to generate graphs and benchmark figures for a Data Structures paper on Algorithm Analysis.

The script requires Python 3.9+ and the following packages:

• numpy
• matplotlib

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Usage

• Clone the AlgoAnalysisFigures/ repo into any folder.
• Install packages. Run:
  • pip install -r requirements.txt
• Run the script: python figures.py

Note

This will create a figures/ folder with all the PNG files (and CSVs if you keep benchmarks on).

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Expectation

$ python figures.py
Saved figures\fig_growth_rates.png
Saved figures\fig_bubble_sort.png
Saved figures\fig_search.png
Saved figures\fig_sorting_sim.png
Saved figures\fig_binary_halving.png

Running benchmarks on this machine (this may take a bit)...
[sort-bench] n=256  bubble=0.001999s  merge=0.000227s  timsort=0.000010s
[sort-bench] n=512  bubble=0.006741s  merge=0.000514s  timsort=0.000021s
[sort-bench] n=1024  bubble=0.032419s  merge=0.001078s  timsort=0.000042s
[sort-bench] n=2048  bubble=nans  merge=0.002309s  timsort=0.000101s
Saved figures\bench_sort.csv
Saved figures\fig_sorting_bars.png
[search-bench] n=10000  linear=0.000020s  binary=0.000002s
[search-bench] n=100000  linear=0.000429s  binary=0.000003s
[search-bench] n=1000000  linear=0.029770s  binary=0.000004s
Saved figures\bench_search.csv
Saved figures\fig_searching_bars.png

Done. All figures are in the ./figures directory.

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Glossary

Main

• Big-O family overview → fig_growth_rates.png

• Little-o vs Big-O intuition → also fig_growth_rates.png

• Binary search intuition → fig_binary_halving.png

Efficiency Analysis

• Bubble Sort quadratic proof → fig_bubble_sort.png

• Growth-rate separation → fig_growth_rates.png

Benchmarking / Results

• Sorting results (real machine) → fig_sorting_bars.png

• Searching results (real machine) → fig_searching_bars.png

• You can optionally include the simulated curve figure fig_sorting_sim.png next to the bar chart.

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