if the python process was kept alive, then we only are saving 25 milliseconds from ~250 to ~235! However, in real world testing, it seems that the profiler is not really a proper enough test! likely because the profiler is adding some overhead to each line of code.

notice here, if I add this line of code:

transform_cache = {}
BASE_DIR = dirname(realpath(__file__))
if isfile(join(BASE_DIR, r'transform_cache')):
    with open('transform_cache', 'r') as f:
        transform_cache = literal_eval(f.read().replace('\r','').replace('\n',''))
transform_cache = {} # this makes the code faster???

Notice I load from file a transform_cache from a previous run. However because of the "if stone in transform_cache" check, the loaded transform_cache is for some reason slower than allowing it to be filled up again. however, loading it and clearing it, is faster because the cpu/ram/OS is probably doing their own caching, too. if we remove the "if stone in transform_cache" check and keep the transform_cache fully loaded, then it is faster by ~1 millisecond, down to 29 milliseconds! these are the niche problems with caching things and squeezing all the performance out of code.