Skip to main content

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bigot-1.0.2.tar.gz (3.7 kB view hashes)

Uploaded Source

Built Distribution

bigot-1.0.2-py2.py3-none-any.whl (4.8 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page