iri20py 0.0.2

Python bindings for IRI-2020 model

IRI-2020 Python Wrapper

iri20py is a wrapper around the IRI-2020 empirical model.

This repository includes a version of the IRI-2020 model where the call signatures have been modified for ease of integration with Python. The integration is achieved by means of a FORTRAN shim (irishim.f90) that is compiled into a module using F2PY. Data files associated with IRI-2020 are included in the data folder and are available at runtime. The wrapper automatically retrieves the latest available ig_rz.dat and apf107.dat files on import.

Installation

A Fortran compiler is REQUIRED to build the FORTRAN extension module.

From PyPI

pip install iri20py

From GitHub

pip install iri20py@git+https://github.com/sunipkm/iri20py

Usage

from iri20py import Iri2020, alt_grid
from datetime import datetime, UTC
import matplotlib.pyplot as plt

# Instantiate the model
iri = Iri2020()
# Note: iri is a singleton (thread safety with FORTRAN)
# Evaluate the model
_, ds = iri.evaluate(
    datetime(2022, 3, 12, 0, 0, 0, tzinfo=UTC),
    40, -70,
    alt_grid()
)

# ds is an xarray Dataset
# Plot electron density profile
ds.Ne.plot(y='alt_km')
plt.show()

Output Dataset Format

• Coordinates
  • Altitude (alt_km): Altitude in km
• Data Variables (as a function of altitude)
  • Electron density (Ne) in cm^-3
  • Electron temperature (Te) in K
  • Ion temperature (Ti) in K
  • O⁺, H⁺, He⁺, O₂⁺, NO⁺, N⁺ and cluster ion densities (cm^-3)
• Attributes
  • settings: JSON string of settings (iri20py.Settings) used to evaluate the model.
  • date: ISO formatted date and time for which the model was evaluated.
  • lat and lon: Latitude and longitude for where the model was evaluated.
  • Additional attributes as returned in the OARR struct (refer to IRI-2020 documentation). These additional attributes are provided as JSON dictionaries containing a value, its unit, a longer name (long_name) and an associated description, if available.

The dataset is NetCDF4 compatible.

Example Plot

An example script to generate the following plot is available in the tests/test_iri2020.py file.

Citation

If you use this code in your work, please cite the repository:

@software{sunipkm_iri20py_2025,
  author       = {Sunip K. Mukherjee},
  title        = {{iri20py}: A Python Wrapper for the IRI-2020 Empirical Model},
  month        = nov,
  year         = 2025,
  publisher    = {GitHub},
  version      = {v0.0.2},
  doi          = {https://zenodo.org/badge/latestdoi/1089134543},
  url          = {https://github.com/sunipkm/iri20py},
}