Advent of Code 2021

setup

Codes were written using python 3.9. The only non-official modules used are numpy, pandas, z3 and Cython

To compile the cython files run:

python setup.py build_ext --inplace

this command will generate several module_c.so.cpython-39-your-archi.so files in the required folders

code organization

in each dir, you can find :

• input.txt the input of the exercise
• a file c.py that compute both answers at the same time, or 2 files c_part1.py and c_part2.py when both parts are very different

sometimes there are also module files. They can be python file or cython file that needs to be compiled.

day per day

• day 1: pure python
• day 2: pandas
• day 3: numpy
• day 4: numpy
• day 5: numpy
• day 6: pure python
• day 7: numpy
• day 8: 2 solutions, one in pure python and one in z3
• day 9: numpy
• day 10: pure python
• day 11: numpy
• day 12: pure python
• day 13: numpy
• day 14: pure python
• day 15: numpy + cython. To use the cython version change the import in the file c.py.
  • python: part 1: 0.1005 seconds, part 2: 2.4491117000579834 seconds
  • cython: part 1: 0.008 seconds, part 2: 0.247 seconds
• day 16: pure python
• day 17: 3 versions are available, one in pure python, one in cython using python annotation and one in cython using pyx file
  • python run in 0.0457 s
  • python with cython annotation run in 0.0137 s
  • cython pyx file run in 0.0003 s (yes, 100 times faster, mainly because we use c++ std::vector instead of python list )
• day 18: pure python and cython
• day 19: cython
• day 20: numpy + cython
• day 21: python
• day 22: numpy for part1, pure python for part 2
• day 23: pure python or cython
  • pure python runs in 54 s
  • cython runs in 4 s
• day 24: z3
• day 25:
  • numpy runs in 0.07903742790222168 s
  • pure python runs in 9.99944257736206 s
  • cython runs in 0.06466269493103027 s

things learned:

• heapq is really powerful when working on sorted list. See day 15 for an application
• z3 is a great tool for constraints solving. See days 8 and 23
• Counter are basically the same as defaultdict(lambda: 0)
• profiling: as simple as a python -m cProfile -o out.prof script.py then snakeviz out.prof
• cython is fast. It also provide a great visualization to understand the c code generated from python.
  • annotating python file to gain some speed work well. See day 17
  • pyx files are a bit more complex to use. Need to compile to do tests, but allow to easily use c and cpp tools, which provide more speed-up (std::vector are insane compare to python list). But beware complex data structure and strings. For these case a mix of python and cython may be best