stac-sample-maker 0.0.1

Generate synthetic STAC (SpatioTemporal Asset Catalog) items with realistic metadata

STAC Sample Maker

Generate synthetic STAC (SpatioTemporal Asset Catalog) items with realistic metadata for testing, development, and demonstration purposes.

Features

• 🌍 Complete STAC compliance: Generates valid STAC v1.1.0 items
• 🔧 All stable extensions: Supports EO, File, Projection, View, SAR, Scientific, and Landsat extensions
• 📝 Template-based generation: Create items matching existing STAC item structures
• ✅ Schema validation: Optional validation using stac-validator
• 🎯 Realistic data: Uses Faker to generate believable synthetic metadata
• 📊 Common metadata: Includes STAC common metadata fields (license, providers, etc.)
• 🔄 Flexible output: NDJSON format with stdout or file output
• 🎲 Reproducible: Seed support for consistent outputs

Installation

Basic Installation

pip install stac-sample-maker

With Validation Support

pip install stac-sample-maker[validation]

Development Installation

git clone https://github.com/username/stac-sample-maker.git
cd stac-sample-maker
pip install -e ".[dev,validation]"

Quick Start

Command Line Usage

Generate 5 synthetic STAC items:

stac-sample-maker --num-items 5

Generate items with validation:

stac-sample-maker --num-items 3 --validate

Generate items from a template:

stac-sample-maker --template my-stac-item.json --num-items 10

Save to a file:

stac-sample-maker --num-items 5 --output samples.ndjson

Library Usage

from stac_sample_maker import generate_stac_items

# Generate basic items
items = generate_stac_items(num_items=5)

# Generate with specific parameters
items = generate_stac_items(
    num_items=10,
    start_date="2020-01-01T00:00:00Z",
    end_date="2023-12-31T23:59:59Z",
    bbox=[-122.5, 37.7, -122.3, 37.8],  # San Francisco area
    seed=42,  # For reproducible results
    validate=True  # Requires stac-validator
)

# Generate from template
from stac_sample_maker import generate_stac_items_from_template

items = generate_stac_items_from_template(
    template_path="example-item.json",
    num_items=5
)

Command Line Interface

stac-sample-maker [OPTIONS]

Options

Option	Description	Default
--num-items N	Number of STAC items to generate	1
--template PATH	Path to template STAC item JSON file	None
--output PATH	Output file path (NDJSON format)	stdout
--start DATE	Start of datetime range (ISO 8601)	None
--end DATE	End of datetime range (ISO 8601)	None
--interval-percent FLOAT	Fraction of items using intervals (0-1)	0.2
--bbox MINX MINY MAXX MAXY	Bounding box for geometry	None
--seed INT	Random seed for reproducibility	None
--validate	Validate items against STAC schema	False

Examples

# Generate 100 items for 2023 with 50% using time intervals
stac-sample-maker --num-items 100 \
  --start "2023-01-01T00:00:00Z" \
  --end "2023-12-31T23:59:59Z" \
  --interval-percent 0.5

# Generate items within a bounding box (San Francisco)
stac-sample-maker --num-items 20 \
  --bbox -122.5 37.7 -122.3 37.8

# Generate reproducible items with validation
stac-sample-maker --num-items 10 \
  --seed 42 \
  --validate \
  --output validated-items.ndjson

Library API

Core Functions

generate_stac_items()

Generate synthetic STAC items with all extensions.

def generate_stac_items(
    num_items: int,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    interval_percent: float = 0.2,
    bbox: Optional[Tuple[float, float, float, float]] = None,
    seed: Optional[int] = None,
    extensions: Optional[List[str]] = None,
    validate: bool = False,
) -> List[dict]:

generate_stac_items_from_template()

Generate STAC items matching a template structure.

def generate_stac_items_from_template(
    template_path: str,
    num_items: int,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    bbox: Optional[Tuple[float, float, float, float]] = None,
    seed: Optional[int] = None,
    validate: bool = False,
) -> List[Dict[str, Any]]:

validate_stac_item()

Validate a STAC item against JSON schema.

def validate_stac_item(
    item: Dict[str, Any],
    strict: bool = True
) -> bool:

Supported Extensions

• EO (Electro-Optical): Cloud cover, snow cover, spectral bands
• File: File size, checksums, header information
• Projection: EPSG codes, transforms, shapes, bounding boxes
• View: Viewing angles, sun position, off-nadir angles
• SAR: Radar-specific metadata (frequency, polarization, etc.)
• Scientific: DOI citations, publications
• Landsat: Landsat-specific metadata (WRS path/row, etc.)

Template Mode

Template mode analyzes an existing STAC item and generates new items with the same structure:

1. Extensions: Matches the exact extensions used
2. Properties: Generates new values for the same property fields
3. Assets: Creates assets with the same keys and structure
4. Temporal: Preserves instant vs. interval temporal patterns

Example template workflow:

# Save one generated item as a template
items = generate_stac_items(num_items=1)
with open("template.json", "w") as f:
    json.dump(items[0], f)

# Generate more items matching the template
similar_items = generate_stac_items_from_template(
    template_path="template.json",
    num_items=100
)

STAC Compliance

Generated items are fully compliant with:

• STAC Specification v1.1.0
• All stable STAC extensions
• STAC Common Metadata (license, providers, platform, etc.)
• GeoJSON standards for geometry
• ISO 8601 for datetime formatting

Development

Setup

git clone https://github.com/username/stac-sample-maker.git
cd stac-sample-maker
pip install -e ".[dev,validation]"
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=stac_sample_maker --cov-report=html

# Run specific tests
pytest tests/test_generator.py -v

Code Quality

# Format code
ruff format

# Lint code
ruff check

# Type checking
mypy stac_sample_maker

Pre-commit Hooks

The project uses pre-commit hooks for code quality:

pre-commit run --all-files

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests and linting (pytest && ruff check && mypy stac_sample_maker)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• STAC Specification for the standard
• Faker for synthetic data generation
• stac-validator for validation support

Related Projects

• PySTAC - Python library for working with STAC
• STAC Validator - Validation tools
• STAC Browser - Browse STAC catalogs
• STAC Index - Discover STAC resources