Skip to main content

Identifies patterns and outliers in large log files and structured data

Project description

txtfold

Identifies patterns and outliers in large log files and structured data. No ML, no fuzzy logic — same input always produces the same output.

Installation

pip install txtfold

Quick start

import txtfold

# Returns a structured dict
result = txtfold.process(text)

# Or get a markdown summary directly
md = txtfold.process_markdown(text)

By default, the algorithm and input format are auto-detected. Options can be passed as keyword arguments:

result = txtfold.process(text, algorithm="clustering", threshold=0.9)

Documentation

Full documentation — algorithms, parameters, and output schema — is at https://kristiandupont.github.io/txtfold/.

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

txtfold-0.1.0.tar.gz (80.9 kB view details)

Uploaded Source

File details

Details for the file txtfold-0.1.0.tar.gz.

File metadata

  • Download URL: txtfold-0.1.0.tar.gz
  • Upload date:
  • Size: 80.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for txtfold-0.1.0.tar.gz
Algorithm Hash digest
SHA256 631b00b33f8a787eb0691e0c825134d4cd32ecfeb8d02f2abc02b23265b7c42d
MD5 88635f4c65d63f9434d9f39f12669075
BLAKE2b-256 5fb2da712cf11ed812ab41489b2af49453c592266b76e9e284d13a7345a3a226

See more details on using hashes here.

Provenance

The following attestation bundles were made for txtfold-0.1.0.tar.gz:

Publisher: publish-pypi.yml on kristiandupont/txtfold

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

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