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

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 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

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