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Benchmarking library with Space and Time Complexity estimation

Project description

Example complexity graph

Bigot

Benchmarking library with Space and Time Complexity estimation.
Pull requests are welcome !

Installation

pip install bigot

Usage

Provide a benchmark function with a single dimension parameter

def on(n):
    x = 10000000*"-"*int(n)
    sleep(0.001*n)

import bigot
print("Function has a space complexity of", bigot.Space(on2),
      "and a time complexity of", bigot.Time(on2))
Function has a space complexity of O(n^2) and a time complexity of O(n^2)

You can test our fancy options. See docstrings for reference.

bench = bigot.Time(
    on2,
    plot=True,
    duration=1,
    verbose=True,
    name="My fancy function"
)

And check the number of iterations, useful when comparing functions

print(bench.iterations, "iterations in", bench.duration, "seconds")
8 iterations in 8 seconds

You can also compare multiple functions

def on2(n):
    x = 10000000*"-"*int(n**2)
    sleep(0.001*n**2)

print(bigot.Compare([on, on2]).space())
  Name  Duration  Iterations Space complexity
0   On       1.0        49.0             O(n)
1  On2       1.0         8.0           O(n^2)

Testing

pytest .

Project details


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