STanalysis 0.1.0

Air pollution data extraction and comparison with different sources - HealthyPlanet Project - AWI

STanalysis

A Python package for extracting, analyzing, and comparing environmental data from multiple sources.

Developed by the Data Science Support group (DSS) at AWI
For the HealthyPlanet Project – BIPS, under the DataNord initiative

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Features

• Extract point values from NetCDF datasets with spatial and temporal dimensions
• Support for multiple input formats (CSV, Shapefile, GeoJSON)
• Temporal aggregation with configurable window sizes
• Efficient nearest-neighbor interpolation
• Comprehensive error handling and input validation

Installation

You can install STanalysis directly from PyPI:

pip install STanalysis

Or install from source for development:

git clone https://github.com/MuhammadShafeeque/dss-environment-analysis.git
cd dss-environment-analysis
uv pip install -e .[dev]

Quick Start

Here's a simple example of extracting temperature values at specific points:

from STanalysis import extract_point_values

# Extract values from a NetCDF file at specified points
result_df = extract_point_values(
    netcdf_path="temperature.nc",
    points_path="measurement_points.csv",
    variable="temperature",
    days_back=2,  # Average over the last 2 days
    date_col="date"
)

print(result_df[["name", "lat", "lon", "value"]])

Input CSV format example:

name,lat,lon,date
"Bremen City",53.0793,8.8017,2024-06-08
"Bremen North",53.1680,8.6317,2024-06-15

Documentation

Main Functions

extract_point_values

def extract_point_values(
    netcdf_path: str | Path,
    points_path: str | Path,
    variable: str,
    *,
    days_back: int = 7,
    date_col: Optional[str] = None,
    output_path: Optional[str | Path] = None,
) -> pd.DataFrame

Parameters:

• netcdf_path: Path to the input NetCDF file
• points_path: Path to point data (CSV, shapefile or GeoJSON)
• variable: Name of the variable to extract from the NetCDF dataset
• days_back: Number of days to average backwards from the provided date
• date_col: Optional column in the point file containing the date
• output_path: Optional path to write results (CSV or JSON)

Returns:

• DataFrame containing the extracted values and point metadata

Development Setup

Prerequisites

Before you begin, ensure you have the following installed:

• Docker
• Visual Studio Code
• Dev Containers extension for VS Code

Using Dev Container

1. Clone the Repository:

git clone https://github.com/MuhammadShafeeque/dss-environment-analysis.git
cd dss-environment-analysis

2. Open in Dev Container:
   • Open VS Code
   • Press Ctrl+Shift+P (or Cmd+Shift+P on macOS)
   • Type "Dev Containers: Open Folder in Container"
   • Select the cloned repository folder

The dev container provides:

• Python 3.13+ with uv package manager
• Pre-configured VS Code extensions
• All development dependencies

Development Tools

The development environment includes:

• pytest for testing
• mypy for type checking
• ruff for code formatting and linting

Run tests:

pytest

Run type checking:

mypy STanalysis

Format code:

ruff format .

Project Structure

dss-environment-analysis/          # Repository root
├── STanalysis/                   # Main package source
│   ├── __init__.py              # Package initialization
│   └── point_extraction.py       # Core functionality
├── examples/                     # Example scripts
│   └── point_extraction_example.py
├── tests/                        # Test files
├── docs/                         # Documentation
├── pyproject.toml               # Project configuration
└── README.md                    # This file

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.

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Note: This repository is under active development. Features and APIs may change.