MISA-pySLIME 2.0.9

Millstone Hill Incoherent Scatter Radar Spatial-Linear Ionospheric Modeling Engine

MISA pySLIME: Ionospheric Background Modeling Toolkit

Millstone Hill Incoherent Scatter Radar Spatial-Linear Ionospheric Modeling Engine

---

MISA pySLIME is a Python library for building and querying binned‑regression ionospheric background models for ion and electron temperature (Ti/Te) and density (Ne) from Millstone Hill Incoherent Scatter Radar (MISA) data. It supports querying predictions for both lat&lon and alt&azimuth, day of year (DOY), and solar local time (SLT) or universal time (UT), while leveraging pre-computed geophysical indices. The library also supports efficient vectorized predictions for multiple inputs.

Features

• Predict electron density (Ne), electron temperature (Te), or ion temperature (Ti) for specific geospatial and temporal conditions.
• Flexibility between querying with lat/lon or UT/SLT
• Efficient batch predictions using vectorized input arrays for higher performance.
• Supports querying geophysical indices from provided datasets.

---

🔧 Installation

Prerequisites

• Python 3.8+
• NumPy
• requests
• SciKit-Learn
• Pandas
• Xarray
• tqdm
• SciPy

Installation

You can install this package via pip:

pip install MISA_pySLIME

The model downloads missing dependencies on first import (e.g., the geophysical dataset):

Downloading MISA dataset from https://www.dropbox.com/...
MISA dataset downloaded and saved to ancillary/processed_ncs/...
Downloading Geophysical Dataset from https://www.dropbox.com/...
Geophysical Dataset downloaded and saved to ancillary/processed_ncs/...

If the files already exist locally, the library will skip the download step and use the existing files.

---

Usage

🚀 Quickstart

Here's how you can use the model to predict electron density over 24 hours:

from MISA_pySLIME import predict_ne, predict_ti, predict_te

# Example: predict electron density (Ne) at azimuth=-80°, altitude=350 km,
# DOY=99, UT=12.5 in 2024:
ne = predict_ne(
  doy=99,
  time=12.5,
  year=2024,
  coords=(-80, 350),
  input_coords='az_alt',  # or 'lat_lon'
  time_ref='ut'  # 'slt' or 'ut'
)
print(f"Predicted Ne: {ne:.3e} m⁻³")

Batch Predictions

To predict multiple inputs efficiently, you can pass arrays to predict_ne,predict_te, or predict_ti:

placeholder

---

Inputs and Parameters

Clamping and Wrapping Input Values

Set argument verbose=True to enable progress bar.

placeholder

---

All functions are defined in pySLIME.py.

• get_lat_lon(az, alt) Convert radar beam geometry (azimuth, altitude) to geographic (latitude, longitude).

• get_az_alt(lat, lon) Inverse mapping: geographic coords → beam (azimuth, altitude).

• query_model(az, alt, doy, slt, indices, bin_models, feature_order) Low‑level engine that selects the correct bin, scales & transforms features, and runs the Ridge regression.

• predict_ne(...), predict_ti(...), predict_te(...) High‑level convenience wrappers loading pre‑trained models for electron density, ion temperature, and electron temperature.

---

📁 Project Structure

pySLIME/
├── ancillary/             # Geophysical & grid datasets (netCDF, CSV)
├── model/                 # Pre‑trained model artifacts (.npy)
├── notebooks/             # Example & tutorial Jupyter notebooks
├── pySLIME.py             # Core library functions
├── requirements.txt       # Python dependencies (pip)
├── environment.yml        # Optional: conda environment spec
├── README.md              # This file
├── LICENSE                # Open‑source license
└── .gitignore             # Ignore patterns for git