trailpack 0.1.0

Contains all the python boilerplate you need to create a Brightway ecosystem package.

trailpack

Installation

You can install trailpack via [pip] from [PyPI]:

$ pip install trailpack

Usage

Web UI

Trailpack includes a Streamlit-based web UI for mapping Excel columns to PyST concepts:

# Run the UI
streamlit run trailpack/ui/streamlit_app.py

Or using the run script:

python trailpack/ui/run_streamlit.py

The UI provides a 3-step workflow with smooth transitions:

1. Upload File & Select Language: Upload an Excel file and select language for PyST mapping
2. Select Sheet: Choose which sheet to process with data preview
3. Map Columns: Map each column to PyST concepts with automatic suggestions and dataframe preview

The view object is stored internally for further processing.

For more details, see trailpack/ui/README.md.

Deploying to Streamlit Cloud? See STREAMLIT_DEPLOYMENT.md for complete deployment instructions.

Python API

You can also use trailpack programmatically:

from trailpack.excel import ExcelReader
from trailpack.pyst.api.client import get_suggest_client

# Read Excel structure
reader = ExcelReader("data.xlsx")
sheets = reader.sheets()
columns = reader.columns("Sheet1")

# Get PyST suggestions
client = get_suggest_client()
suggestions = await client.suggest("carbon footprint", "en")

✨ New: UI-Ready DataPackage Schema Classes

This project now includes comprehensive schema classes for building data package metadata through user interfaces:

Key Features

• DataPackageSchema: Defines field types, validation rules, and UI configuration
• MetaDataBuilder: Fluent interface for creating metadata programmatically
• Field validation: Built-in validation for package names, versions, URLs
• UI integration ready: Field definitions include labels, placeholders, patterns
• Standards compliant: Follows Frictionless Data Package specification

Quick Example

from trailpack.datapackage_schema import MetaDataBuilder, Resource, Field

# Create metadata with fluent interface
metadata = (MetaDataBuilder()
    .set_basic_info(name="my-dataset", title="My Dataset")
    .add_license("CC-BY-4.0")
    .add_contributor("Your Name", "author")
    .add_resource(Resource(name="data", path="data.parquet"))
    .build())

# Use with existing Packing class
from trailpack.packing import Packing
packer = Packing(df, metadata)
packer.write_parquet("output.parquet")

UI Integration

The schema classes provide everything needed for UI frameworks:

• Field definitions with types, labels, validation patterns
• Enumerated options for dropdowns (licenses, profiles, etc.)
• Built-in validation methods
• Error messages for invalid input

See examples/datapackage_ui_demo.py for detailed usage examples.

Contributing

Contributions are very welcome. To learn more, see the Contributor Guide.

License

Distributed under the terms of the MIT license, trailpack is free and open source software.

Issues

If you encounter any problems, please file an issue along with a detailed description.

Building the Documentation

You can build the documentation locally by installing the documentation Conda environment:

conda env create -f docs/environment.yml

activating the environment

conda activate sphinx_trailpaack

and running the build command:

sphinx-build docs _build/html --builder=html --jobs=auto --write-all; open _build/html/index.html