Skip to main content

About

This module provides a CLI tool for rough approximations according to a catalog of prescribed growth rates.

The tool currently features most well-known classes of growth rates and works by fitting each generic growth rate function to the given data.

Its primary use case is estimating time or space growth rates of various algorithms, but it can also be used on data coming from physical measurements.

Note: The computed matches only give an approximation, which depending on the number of data points and the noise in the data might be completely different from the actual growth rate(that would be determined through more rigorous analysis).

Install

To install from pypi:

pip3 install --user matchgrowth

Usage

The CLI tool expects to be passed through --infile a CSV file, and through --col1 and --col2 two column names present inside the CSV file. Currently the CSV file needs to have headers for all columns.

The --top parameter allows to limit the number of matched growth rates reported.

The --outfile parameter is optional and allows to write the generated plot to a PNG file on disk. If the --outfile parameter is not passed, the default GUI from matplotlib will be used to display the plot.

usage: match-growth.py [-h] --infile INFILE [--outfile OUTFILE] --col1 COL1
                       --col2 COL2 [--top TOP]

Tool for estimating growth rates

optional arguments:
  -h, --help         show this help message and exit
  --infile INFILE    CSV input file with measurements
  --outfile OUTFILE  PNG file to write the plot to
  --col1 COL1        data column 1 from the CSV
  --col2 COL2        data column 2 from the CSV
  --top TOP          only print top N closest growth rates

Example usage:

match-growth.py --infile ./tests/modif7_bench.txt --outfile r1.png --col1 N --col2 memory --top 2

In this example we're estimating the closest growth rate for an algorithm that has a single input N and for which we've already recorded the memory usage for different values of N.

drawing

Support

For questions or requests for paid support, please send an e-mail to business@garage-coding.com

Download files

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

Source Distribution

matchgrowth-0.0.7.tar.gz (4.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

matchgrowth-0.0.7-py3-none-any.whl (5.6 kB view details)

Uploaded Python 3

File details

Details for the file matchgrowth-0.0.7.tar.gz.

File metadata

  • Download URL: matchgrowth-0.0.7.tar.gz
  • Upload date:
  • Size: 4.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.3

File hashes

Hashes for matchgrowth-0.0.7.tar.gz
Algorithm Hash digest
SHA256 9af306cdef0fe3d066540624c9bc47907f6b0fd69462f7e6c9f972bd63761adf
MD5 3659de73a424e7ecb9d9746a95c25b6e
BLAKE2b-256 ddc1f5d7f011a3132434ff246506d925395e68190cc68305375cd0dd91f8a039

See more details on using hashes here.

File details

Details for the file matchgrowth-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: matchgrowth-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 5.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.3

File hashes

Hashes for matchgrowth-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 8b727c4f1e147a27ffac86dab2478a79c54ef84ad06c968c8f13fa9ea90d186d
MD5 4aeb9400fed3af1bebceb85df5e09ffa
BLAKE2b-256 425e4a59e8d5a3637e2dc24c369bc46daf9e341e5aec237a76f62eaaf0cb5b86

See more details on using hashes here.

Release history Release notifications | RSS feed

0.0.9

2 files

0.0.8

2 files

This release

0.0.7 This release

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page