Skip to main content

Imaging Spectrometer Optimal FITting

Project description

Welcome to ISOFIT 3x. This is a major update to the ISOFIT codebase, and is not backwards compatible with ISOFIT 2x. To view the previous version of ISOFIT, please see dev_2x. Updates and performance enhancements are still underway, but testing and feedback are encouraged! A list of new 3x features is compiled below.

ISOFIT contains a set of routines and utilities for fitting surface, atmosphere and instrument models to imaging spectrometer data. It is written primarily in Python, with JSON format configuration files and some dependencies on widely-available numerical and scientific libraries such as scipy, numpy, and scikit-learn. It is designed for maximum flexibility, so that users can swap in and evaluate model components based on different radiative transfer models (RTMs) and various statistical descriptions of surface, instrument, and atmosphere. It can run on individual radiance spectra in text format, or imaging spectrometer data cubes.

  • Please check the documentation for installation and usage instructions and in depth information.

  • There are three main branches:

  • main (in-line with the current release)

  • dev (for activate development of ISOFIT 3x)

  • dev_2x (archived version of ISOFIT 2x)

  • Information on how to cite the ISOFIT Python package can be found in the CITATION file.

License

Free software: Apache License v2

All images contained in any (sub-)directory of this repository are licensed under the CC0 license which can be found here.

Major ISOFIT 3x features

  • new handling of look-up-tables (LUTs), including the option to provide custom prebuilt LUTs

  • centralized radiative transfer physics for more flexible development and experimentation

  • test coverage for major functionality

  • click command line utilities, including download of external data and example files

  • a more flexible isofit.ini file used to discover various paths such as tests, data, and examples

  • instructions for dev environment setup and a collection of setup scripts

  • numpy implementation of the sRTMnet emulator (removes tensorflow dependency)

Basic features

  • utilities for fitting surface, atmosphere and instrument models to imaging spectrometer data

  • a selection of radiative transfer models (RTMs) incl. MODTRAN and 6S

  • sRTMnet emulator for MODTRAN 6 by coupling a neural network with a surrogate RTM (6S v2.1)

  • various statistical descriptions of surface, instrument, and atmosphere

  • application to both individual radiance spectra and imaging spectrometer data cubes

  • custom instrument models to handle new sensors

  • observation uncertainties to account for model discrepancy errors

  • prior distribution based on background knowledge of the state vector

Status

badge1 badge2 badge3 badge4 badge5 badge6 badge7 badge8

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

isofit-3.4.3.tar.gz (870.3 kB view details)

Uploaded Source

Built Distribution

isofit-3.4.3-py3-none-any.whl (268.5 kB view details)

Uploaded Python 3

File details

Details for the file isofit-3.4.3.tar.gz.

File metadata

  • Download URL: isofit-3.4.3.tar.gz
  • Upload date:
  • Size: 870.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for isofit-3.4.3.tar.gz
Algorithm Hash digest
SHA256 d0fee7e15869d47719aff0e8441e803c12d3f94f2788bee2602db59373937eaa
MD5 812eca8c784db44086e6af2139aebd32
BLAKE2b-256 f0255bd14bf3f589b8cd054e69ff49d7cbe4dcffee4299d5cada73e6e600bb27

See more details on using hashes here.

File details

Details for the file isofit-3.4.3-py3-none-any.whl.

File metadata

  • Download URL: isofit-3.4.3-py3-none-any.whl
  • Upload date:
  • Size: 268.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for isofit-3.4.3-py3-none-any.whl
Algorithm Hash digest
SHA256 c24dc222424d5e675631a3f623c13330db6da688132dc3ca77d0756e9cc34bbd
MD5 5d478dda327444fd9b223a5066b36629
BLAKE2b-256 ba47fdde21460a365e30c62517a88384003bcd99e600bfb82aefedbfb1446ef5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page