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 Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

txtfold-0.2.8-cp314-cp314-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

txtfold-0.2.8-cp312-cp312-win_amd64.whl (1.0 MB view details)

Uploaded CPython 3.12Windows x86-64

txtfold-0.2.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

File details

Details for the file txtfold-0.2.8-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for txtfold-0.2.8-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0547847a6964a4af99e2294757537aafcc87d2e13d04741fb914b73590ef89d4
MD5 95bccce5b4e1c882a85501d19bea8e3f
BLAKE2b-256 934c66e37f5dc8346d26de1e79bb9b791f02550e9e1b94a23d6fee3896ceeb67

See more details on using hashes here.

Provenance

The following attestation bundles were made for txtfold-0.2.8-cp314-cp314-macosx_11_0_arm64.whl:

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.

File details

Details for the file txtfold-0.2.8-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: txtfold-0.2.8-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for txtfold-0.2.8-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 a732da361ea63803d0bff3c34531699ac74dc1af6d362736fd519fc643ee5d44
MD5 83cd46b2fb1534190e5d135aca2318b1
BLAKE2b-256 99b576bf0a67050a3efa1e5f5a280391bf65629aafb8e10f7088b90d48d76cfd

See more details on using hashes here.

Provenance

The following attestation bundles were made for txtfold-0.2.8-cp312-cp312-win_amd64.whl:

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.

File details

Details for the file txtfold-0.2.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for txtfold-0.2.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8f5d8db8a083ec86af2bda674bdc0b290d7831dafbd271ad8711dbe16f443902
MD5 1d54ed7403a5f1701314be296a4365fd
BLAKE2b-256 5a4efa9d2188d95423cf6220b4ea084cc9317a0e658826bd62c69f08b3811b7f

See more details on using hashes here.

Provenance

The following attestation bundles were made for txtfold-0.2.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

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