facpy 0.1.0

Field-Aligned Current Python toolkit for SWARM satellite analysis

facpy: Field-Aligned Current Python Toolkit

facpy is a research-grade Python package designed for standardized, fast, and reproducible analysis of Swarm satellite Field-Aligned Currents (FAC). It is optimized for quiet-time studies, regional analysis (e.g., Africa), and interhemispheric comparisons.

🚀 Key Features

• Data Loading: Efficient loading of Swarm Level 2 FAC data (CDF and NetCDF formats) into Polars DataFrames.
• Quiet Time Selection: Automatic selection of quietest days based on Kp (sum/max) or Dst (min) geomagnetic indices.
• Geospacial Tools:
  • Region-based filtering (presets for Africa, Europe, Polar Caps, etc.).
  • Solar Local Time (SLT) calculation.
  • Hemisphere separation.
• Gridding: Fast aggregation of point data into regular 2D (Lat/Lon) or 3D (Lat/Lon/LT) grids using vectorization.
• Interhemispheric Analysis (IHFAC): Tools to compare Northern and Southern hemisphere currents (Difference, Ratio) with automatic coordinate alignment.
• Visualization: Publication-ready map generation using cartopy.

📦 Installation

facpy requires Python 3.9+.

# Clone the repository
git clone https://github.com/madvirus-ops/facpy
cd facpy

# Install in editable mode
pip install -e .

# Install optional development dependencies
pip install -e ".[dev]"

⚡ Quick Start

Here is a complete workflow example demonstrating loading, filtering, gridding, and mapping.

import facpy
from facpy import io, quiet, geo, grid, plot
import polars as pl

# 1. Load Data
# Supports single file or list of files (CDF/NetCDF)
df = io.load_swarm_fac("SW_OPER_FAC_A_20210101.cdf")

# 2. Select Quiet Days
# Get the 5 quietest days in Jan 2021 based on Kp index
quiet_dates = quiet.quiet_days(
    start_date="2021-01-01", 
    end_date="2021-01-31", 
    method="kp", 
    top_n=5,
    index_file="kp_index.txt" # Path to your index file
)

# Filter dataframe
df = df.filter(pl.col("timestamp").dt.date().is_in(quiet_dates))

# 3. Filter Region & Add Local Time
# Focus on Africa and calculate Solar Local Time
df_africa = geo.filter_region(df, region="africa")
df_africa = geo.add_local_time(df_africa)

# 4. Grid the Data
# Create a 2°x2° grid of Mean FAC values
ds_grid = grid.grid_fac(
    df_africa, 
    resolution=(2.0, 2.0), 
    statistic="mean"
)

# 5. Plot
# Generate a map using built-in Cartopy plotter
plot.fac_map(
    ds_grid, 
    title="Quiet Time Mean FAC - Africa", 
    projection="platecarree"
)

📚 Module Overview

facpy.io

Handles file I/O.

• load_swarm_fac(): Reads data, handles fill values, and normalizes column names.

facpy.quiet

Geomagnetic activity selection.

• quiet_days(): Returns dates of low activity defined by Kp or Dst.

facpy.geo

Coordinate and spatial tools.

• filter_region(): Spatial subsetting.
• add_local_time(): Computes SLT from UTC and Longitude.

facpy.grid

Aggregation logic.

• grid_fac(): Converts track data to xarray.Dataset grids. Supports multiple statistics (mean, median, std, count).

facpy.ihfac

Interhemispheric analysis.

• compare(): Aligns South hemisphere data to North coordinates and computes difference or ratio maps.

facpy.plot

Visualization.

• fac_map(): Wrapper around Cartopy for quick, consistent FAC maps.

🧪 Testing

Run the test suite to ensure everything is working correctly:

pytest tests/

🤝 Contributing

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