Skip to main content

A set of helpers to manipulate Colander data.

Project description

Colander Data Converter

A set of helpers to manipulate Colander data.

Website | Documentation | GitHub | Support

⚠️ This project is currently under active development and is not suitable for production use. Breaking changes may occur without notice. A stable release will be published to PyPI once development stabilizes.

Colander Data Converter is part of the PiRogue Tool Suite (PTS) ecosystem, created to assist investigators, researchers, and civil society organizations in performing mobile forensics and digital investigations.

colander_data_converter is a Python library that enables interoperability between cyber threat intelligence (CTI) platforms by converting structured threat data between different formats — notably MISP, STIX 2.1, and Colander. Colander data format is an opinionated data format focused on usability and interoperability.

It's designed for developers, CTI analysts, and investigators who need to normalize, migrate, or integrate threat data across systems that use different schemas.

Key features

  • 🔄 Convert between MISP, STIX 2.1, and Colander
  • 📦 Preserve relationships, metadata, and object references
  • 🧩 Easily integrated into existing pipelines and CTI platforms
  • ⚙️ CLI and programmatic usage
  • 📖 Open-source and extensible

Who is this for?

  • CTI developers integrating systems or building bridges across tools
  • Threat analysts converting incoming feeds for unified analysis
  • Security researchers working with mixed-format CTI datasets
  • Organizations using Colander for collaborative investigations

Installation

colander_data_converter requires Python 3.12 or higher.

Once released, install with:

pip install colander_data_converter

Usage examples

Stix 2.1 to Colander

import json
from colander_data_converter.converters.stix2.converter import Stix2Converter
from colander_data_converter.converters.stix2.models import Stix2Bundle

with open("path/to/stix2_bundle.json", "r") as f:
    raw = json.load(f)
stix2_bundle = Stix2Bundle.load(raw)
colander_feed = Stix2Converter.stix2_to_colander(stix2_bundle)

Generate Graphviz DOT file

import json

from colander_data_converter.base.models import ColanderFeed
from colander_data_converter.exporters.graphviz import GraphvizExporter

# Load the feed
with open("path/to/colander_feed.json", "r") as f:
    raw = json.load(f)
colander_feed = ColanderFeed.load(raw)

# Export the feed as a graph
exporter = GraphvizExporter(colander_feed)
with open("path/to/colander_feed.dot", "w") as f:
    exporter.export(f)

Contributing

We welcome community contributions! You can:

  • Report bugs or suggest improvements via Issues
  • Submit pull requests for format support or enhancements on GitHub
  • Help document conversion edge cases or gaps
  • Join our Discord server

Development setup

  1. Install Python 3.12 or higher.
  2. Install uv.
  3. Clone the project repository:
git clone https://github.com/PiRogueToolSuite/colander-data-converter
cd colander-data-converter
uv sync

Before submitting a PR, execute run the test suite and the pre-commit checks:

tox run -e fix,3.12,docs

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

colander_data_converter-1.0.10.tar.gz (95.1 kB view details)

Uploaded Source

Built Distribution

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

colander_data_converter-1.0.10-py3-none-any.whl (123.0 kB view details)

Uploaded Python 3

File details

Details for the file colander_data_converter-1.0.10.tar.gz.

File metadata

File hashes

Hashes for colander_data_converter-1.0.10.tar.gz
Algorithm Hash digest
SHA256 ce77f5d53f4e4055cdb623dbf4f1194d0c5eaf250c5714546763176d5ef62b60
MD5 724ea0f5f890def92b18901ce1ca57d2
BLAKE2b-256 968e38c1cff115210d7a61387374e50b990428c09eaadfbdc9f98cfd6f5d63d8

See more details on using hashes here.

File details

Details for the file colander_data_converter-1.0.10-py3-none-any.whl.

File metadata

File hashes

Hashes for colander_data_converter-1.0.10-py3-none-any.whl
Algorithm Hash digest
SHA256 8ff61bf02cbf621dd389991c7c9db986d83f9b24a45ab60d6fb7a5b197c5e353
MD5 49c31f42afed6cd7c2aafc3c1b83cf8d
BLAKE2b-256 3ffff318268a88655394c022241fb450203ed96a5e0fa8290daf9e5f90763140

See more details on using hashes here.

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