Welcome to Infobs’ documentation

The infobs Python package provides tools to efficiently study the informativity of variables on data of interest.

Context

TODO

Installation

(optional) Create a virtual environment and activate it:

`shell python -m venv .venv source .venv/bin/activate `

Note 1: to deactivate the virtual env :

`shell deactivate `

Note 2: To delete the virtual environment:

`shell rm -r .venv `

From local package

To get the source code:

`shell git clone git@github.com:einigl/iram-30m-emir-obs-info.git `

To install infobs:

`shell pip install -e . `

Contents:

• infobs
  • infobs package
    • Subpackages
      • infobs.graphics package
      • infobs.instruments package
      • infobs.model package
      • infobs.sampling package
      • infobs.util package
    • Submodules
    • infobs.informativity module
    • infobs.simulator module
    • Module contents

• Gallery of examples
  • Meudon PDR code emulator predictions
  • Instruments
  • IRAM 30m EMIR spectral bands
  • Sampling
  • Simulating observations
  • Informativity of IRAM 30m EMIR observations

Associated packages

[A&A paper repository](https://github.com/einigl/informative-obs-paper): Reproduce the results in Einig et al. (2024)

[InfoVar](<https://github.com/einigl/infovar>): Estimating informativity of features.

[Neural network-based model approximation](<https://github.com/einigl/ism-model-nn-approximation>): handle the creation and the training of neural networks to approximate interstellar medium numerical models.

References

[1] Einig, L, Palud, P. & Roueff, A. & Pety, J. & Bron, E. & Le Petit, F. & Gerin, M. & Chanussot, J. & Chainais, P. & Thouvenin, P.-A. & Languignon, D. & Bešlić, I. & Coudé, S. & Mazurek, H. & Orkisz, J. H. & G. Santa-Maria, M. & Ségal, L. & Zakardjian, A. & Bardeau, S. & Demyk, K. & de Souza Magalhães, V. & Javier R. Goicoechea & Gratier, P. & V. Guzmán, V. & Hughes, A. & Levrier, F. & Le Bourlot, J. & Darek C. Lis & Liszt, H. S. & Peretto, N. & Roueff, E & Sievers, A. (2024). Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. I. Application to model predictions. Astronomy & Astrophysics. [10.1051/0004-6361/202451588](https://doi.org/10.1051/0004-6361/202451588).

[2] Palud, P. & Einig, L. & Le Petit, F. & Bron, E. & Chainais, P. & Chanussot, J. & Pety, J. & Thouvenin, P.-A. & Languignon, D. & Beslić, I. & G. Santa-Maria, M. & Orkisz, J.H. & Ségal, L. & Zakardjian, A. & Bardeau, S. & Gerin, M. & Goicoechea, J.R. & Gratier, P. & Guzman, V. (2023). Neural network-based emulation of interstellar medium models. Astronomy & Astrophysics. [10.1051/0004-6361/202347074](https://doi.org/10.1051/0004-6361/202347074).