README.md

Syntrend Synthetic Data Generation

Syntrend is a Python Package and Command Line tool for generating synthetic data to express very specific behaviours and trends across multiple inputs.

A simple Project may only contain a few lines of YAML

$ cat 5_numbers.yaml
output:
  count: 5
type: integer

$ syntrend generate 5_numbers.yaml
-178
430
-192
-114
-125

---

Specific objectives for this project is to:

• Be Lightweight: Make a tool that can easily run from a local workstation, from a CI Pipeline, or embedded into an application.
• Be Easy to Use: All configurations use YAML, intended as an extendable markup format that allows re-use within and across projects.
• Be Environment Agnostic: Everyone has preferences of how they want to work so providing formatted outputs that can be easily consumed by target sources is necessary.
• Support As Many Data Types As Possible: Projects have different expectations of how they consume data: exchange formats, structured, streaming, or a combination of all with references between them.
• Be Expressive: Data can have a personality, and we need this data to express that personality so we have something consistent to work with.

Quickstart

1. Install Syntrend

   For a local Python project, use the project release to PyPI

   pip install syntrend

   or pull the Docker image

   docker pull ghcr.io/wsidl/syntrend:latest

2. Create a Project File

   Create a text file with the YAML content defined in the Project File structure

   type: string

3. Run the Project File

   syntrend generate project_file.yaml

   if using Docker:

   docker run -v $(pwd):/project -w /project ghcr.io/wsidl/syntrend:latest generate project_file.yaml

4. Handle the data

   The data can be produced into a number of different locations. This can be handled after the command is generated or piped from the outputs.

Next Steps

• Become familiar with Project File structure
• Review the types of data generators that can create and parse values for the dataset
• Understand how to use Expressions to define trends in specific properties across a project.
• Apply controlled randomness within your data using Value Distributions
• Understand how data can be formatted or provide custom outputs
• Read the FAQ's for any un-answered questions

Contributing

see CONTRIBUTING documentation