Metadata-Version: 2.1
Name: itipy
Version: 0.1.1
Summary: Package for translation between image domains of different astrophysical instruments.
Home-page: https://github.com/RobertJaro/InstrumentToInstrument
Author: Robert Jarolim
License: GNU GENERAL PUBLIC LICENSE
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch >=1.8
Requires-Dist: sunpy >=2.0
Requires-Dist: scikit-image
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: astropy
Requires-Dist: aiapy
Requires-Dist: drms
Requires-Dist: jupyter
Requires-Dist: sunpy-soar
Requires-Dist: lightning
Requires-Dist: google
Requires-Dist: google-cloud-storage
Requires-Dist: wandb
Requires-Dist: pytorch-fid

![](images/HinodeEnhanced_v2.jpg)
# Instrument to Instrument Translation for Solar Observations

[![Documentation Status](https://readthedocs.org/projects/iti-documentation/badge/?version=latest)](https://iti-documentation.readthedocs.io/en/latest/?badge=latest)

The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not profit from this trend. In solar physics, the study of long-term evolution typically exceeds the lifetime of single instruments and data driven approaches are strongly limited in terms of coherent long-term data samples.

We demonstrate that the available data sets can directly profit from the most recent instrumental improvements and provide a so far unused resource to foster novel research and accelerate data driven studies.

`ITI` (Instrument-to-Instrument) is a method that translates between image domains of different instruments, in order to inter-calibrate data sets, enhance physically relevant features which are otherwise beyond the diffraction limit of the telescope, mitigate atmospheric degradation effects and can estimate observables that are not covered by the instrument.

We demonstrate that our method can provide unified long-term data sets at the highest quality, by applying it to ground- and space-based solar observations.

## Installation
To install the `ITI` tool we recommend to use the [installation](https://iti-documentation.readthedocs.io/en/latest/installation/index.html) guide provided in the documentation.

## Usage
We provide an [example gallery](https://iti-documentation.readthedocs.io/en/latest/generated/gallery/index.html) that can be used as a starting point. A complete notebook with all the example steps (download &rarr; preprocessing &rarr; translation &rarr; visualization) can be found in the ``examples`` folder.

## Information
For more information and a detailed description about the `ITI` tool please visit the [documentation](https://iti-documentation.readthedocs.io/en/latest/index.html) page.

## Acknowledgements
This work is the research product of the 22-MDRAIT22-0018, ITI (Instrument to Instrument) tool - unlocking
collaborative sensor webs research. This has been funded and supported by NASA through a Grant and we
would like to express our sincere gratitude. The research and its outputs have been designed, managed and
delivered by [Trillium Technologies Inc](https://trillium.tech/.) Trillium is a research and development Trillium
with a focus on intelligent systems and collaborative communities for planetary stewardship, space
exploration and human health.

The material is based upon work supported by NASA under Grant award No 80NSSC23K1045. Any
opinions, findings, and conclusions or recommendations expressed in this material are those of the authors
and do not necessarily reflect the views of the National Aeronautics and Space Administration (NASA).
Trillium aspires to ensure that the latest tools and techniques in Artificial Intelligence (AI) and Machine
Learning (ML) are applied to developing open science for all Humankind.
