visualize runtime execution times for arbitrary functions against other functions
Ever wanted to compare function execution speeds in Python? With this tool, albeit with a bit of time to set it up, you can easily contrast differences and decide whether one function is more optimal (in terms of efficiency/speed) than another.
For (a bad) example, comparing two completely random functions I found on the internet which have no textual relation, besides the context which is prime generation.
An obvious detail to notice is that when it does an exponential curve like that, it means that the function is, obviously, working harder on the dataset with the ever-growing iteration cycle; which is a bit of a misleading name because it really just is the x in f(x), just a value that's substituted into the functions using the special replacement string so that the graph can actually be usable.
A completely useless debugging note is that when both functions produce a line scoring across the top, middle or bottom; it probably means you forgot to put the special replacement strings inside. Here's the full output & configuration I had set up to produce that graph:
{*} How many functions would you like to compare? 2
{*} What is the unique replacement string? %%%
{*} What should be the starting integer? 1
{*} What should be the ending integer? 100
{*} How many times should it run? 100
{*} How many times should each function run individiually? 10
Setup (run once) {0}
Code {0} r=range(2,%%%);[x for x in r if sum(x%d<1 for d in r)<2]
Color {0} red
Setup (run once) {1}
Code {1} r=range(2,%%%);m=[x*y for x in r for y in r];[x for x in r if not x in m]
Color {1} green
NOTE:
the usage has been changed however the core functions have remained, simply some aliasing for functions and multiline setups/repeated code clauses.