HyperCoast

HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments - Published in JOSS (2024)

https://github.com/opengeos/hypercoast

Science Score: 98.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
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DOI references
Found 6 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: science.org, joss.theoj.org
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Committers with academic emails
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Institutional organization owner
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords

aviris coastal emit geospatial hyperspectral ipyleaflet ipywidgets leafmap nasa neon pace python

Keywords from Contributors

hydrological exoplanet energy-system mesh ode bids pypi annotation eeg ipympl

Last synced: 4 months ago · JSON representation ·

Repository

A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Basic Info

Host: GitHub
Owner: opengeos
License: mit
Language: Python
Default Branch: main
Homepage: https://hypercoast.org
Size: 58 MB

Statistics

Stars: 213
Watchers: 8
Forks: 40
Open Issues: 4
Releases: 44

Topics

aviris coastal emit geospatial hyperspectral ipyleaflet ipywidgets leafmap nasa neon pace python

Created over 1 year ago · Last pushed 4 months ago

Metadata Files

Readme Contributing License Citation

HyperCoast

A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Free software: MIT License
Documentation: https://hypercoast.org

Introduction

HyperCoast is a Python package designed to provide an accessible and comprehensive set of tools for visualizing and analyzing hyperspectral data in coastal environments. Hyperspectral data refers to the information collected by sensors that capture light across a wide range of wavelengths, beyond what the human eye can see. This data allows scientists to detect and analyze various materials and conditions on the Earth's surface with great detail. Unlike multispectral data, which captures light in a limited number of broad wavelength bands (typically 3 to 10), hyperspectral data captures light in many narrow, contiguous wavelength bands, often numbering in the hundreds. This provides much more detailed spectral information. Leveraging the capabilities of popular packages like Leafmap and PyVista, HyperCoast streamlines the exploration and interpretation of complex hyperspectral remote sensing data from existing spaceborne and airborne missions. It is also poised to support future hyperspectral missions, such as NASA's SBG and GLIMR. It enables researchers and environmental managers to gain deeper insights into the dynamic processes occurring in aquatic environments.

HyperCoast supports the reading and visualization of hyperspectral data from various missions, including AVIRIS, NEON, PACE, EMIT, DESIS, PRISMA and ENMAP along with other datasets like ECOSTRESS. Users can interactively explore hyperspectral data, extract spectral signatures, change band combinations and colormaps, visualize data in 3D, and perform interactive slicing and thresholding operations (see Figure 1). Additionally, by leveraging the earthaccess package, HyperCoast provides tools for interactively searching NASA's hyperspectral data. This makes HyperCoast a versatile and powerful tool for working with hyperspectral data globally, with a particular focus on coastal regions.

EMIT Figure 1. An example of visualizing NASA EMIT hyperspectral data using HyperCoast.

Citations

If you find HyperCoast useful in your research, please consider citing the following papers to support us. Thank you!

Liu, B., & Wu, Q. (2024). HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments. Journal of Open Source Software, 9(100), 7025. https://doi.org/10.21105/joss.07025.

Features

Searching for NASA hyperspectral data interactively
Performing atmospheric correction using Acolite
Interactive visualization and analysis of hyperspectral data, such as AVIRIS, DESIS, EMIT, PACE, NEON AOP, Tanager, PRISMA and ENMAP
Interactive visualization of NASA ECOSTRESS data
Interactive visualization of PACE chlorophyll-a data
Interactive extraction and visualization of spectral signatures
Changing band combinations and colormaps interactively
Visualizing hyperspectral data in 3D
Visualizing ERA5 temperature data in 3D
Interactive slicing and thresholding of hyperspectral data in 3D
Saving spectral signatures as CSV files

Demos

Visualizing hyperspectral data in 3D (notebook)

Cube

Interactive slicing of hyperspectral data in 3D (notebook)

Slicing

Interactive thresholding of hyperspectral data in 3D (notebook)

Slicing

Visualizing ERA5 temperature data in 3D (notebook)

ERA5

Changing band combinations and colormaps interactively (notebook)

colormap

Visualizing NASA AVIRIS hyperspectral data interactively (notebook)

AVIRIS

Visualizing DESIS hyperspectral data interactively (notebook)

DESIS

Visualizing NASA EMIT hyperspectral data interactively (notebook)

EMIT

Visualizing NASA PACE hyperspectral data interactively (notebook)

PACE

Visualizing NEON AOP hyperspectral data interactively (notebook)

NEON

Interactive visualization of PACE chlorophyll-a data (notebook)

Chla

Acknowledgement

The HyperCoast project draws inspiration from the nasa/EMIT-Data-Resources repository. Credits to the original authors. We also acknowledge the NASA EMIT program support through grant no. 80NSSC24K0865.

License

HyperCoast is released under the MIT License. However, some of the modules in HyperCoast adapt code from other open-source projects, which may have different licenses. Please refer to the license notice in each module for more information. Credits to the original authors.

emit.py: Part of the code is adapted from the nasa/EMIT-Data-Resources repository, which is released under the Apache License 2.0.
aviris.py: Part of the code is adapted from the jjmcnelis/aviris-ng-notebooks, which is released under the MIT License.

Contributors

_{Bingqing Liu}
💻 🎨 🤔

_{Qiusheng Wu}
💻 🎨 🚧

_{Alex Leith}
💻 👀

_{arfy slowy}
💻 🚧

_{Guillermo E. Ponce-Campos}
💻 🐛

_{Carsten Lemmen}
👀

_{Advait Dhamorikar}
💻

Owner

Name: Open Geospatial Solutions
Login: opengeos
Kind: organization
Email: opengeos@outlook.com

Website: https://opengeos.github.io
Repositories: 1
Profile: https://github.com/opengeos

A collection of open-source software packages for the geospatial community

JOSS Publication

HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Published

August 26, 2024

DOI

10.21105/joss.07025

Volume 9, Issue 100, Page 7025

Authors

Bingqing Liu

School of Geosciences, University of Louisiana at Lafayette, Lafayette, LA 70504, United States

Qiusheng Wu

Department of Geography & Sustainability, University of Tennessee, Knoxville, TN 37996, United States

Editor

Taher Chegini

Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
    - family-names: Liu
      given-names: Bingqing
      orcid: "https://orcid.org/0000-0003-4651-6996"
    - family-names: Wu
      given-names: Qiusheng
      orcid: "https://orcid.org/0000-0001-5437-4073"
doi: 10.5281/zenodo.13368024
message: If you use this software, please cite our article in the
    Journal of Open Source Software.
preferred-citation:
    authors:
        - family-names: Liu
          given-names: Bingqing
          orcid: "https://orcid.org/0000-0003-4651-6996"
        - family-names: Wu
          given-names: Qiusheng
          orcid: "https://orcid.org/0000-0001-5437-4073"
    date-published: 2024-08-26
    doi: 10.21105/joss.07025
    issn: 2475-9066
    issue: 100
    journal: Journal of Open Source Software
    publisher:
        name: Open Journals
    start: 7025
    title: "HyperCoast: A Python Package for Visualizing and Analyzing
        Hyperspectral Data in Coastal Environments"
    type: article
    url: "https://joss.theoj.org/papers/10.21105/joss.07025"
    volume: 9
title: "HyperCoast: A Python Package for Visualizing and Analyzing
    Hyperspectral Data in Coastal Environments"

GitHub Events

Total

Create event: 21
Issues event: 6
Release event: 4
Watch event: 49
Delete event: 15
Issue comment event: 27
Push event: 60
Pull request review event: 1
Pull request event: 32
Fork event: 12

Last Year

Create event: 21
Issues event: 6
Release event: 4
Watch event: 49
Delete event: 15
Issue comment event: 27
Push event: 60
Pull request review event: 1
Pull request event: 32
Fork event: 12

Committers

Last synced: 5 months ago

All Time

Total Commits: 170
Total Committers: 9
Avg Commits per committer: 18.889
Development Distribution Score (DDS): 0.159

Past Year

Commits: 51
Committers: 6
Avg Commits per committer: 8.5
Development Distribution Score (DDS): 0.294

Top Committers

Name	Email	Commits
Qiusheng Wu	g**s@g**m	143
pre-commit-ci[bot]	6****]	7
allcontributors[bot]	4****]	6
Bingqing Liu	1****u	5
dependabot[bot]	4****]	4
arfy slowy	s**y@p**e	2
Guillermo E. Ponce-Campos	g**e@a**t	1
Bingqing Liu	1****7	1
Alex Leith	a**h@g**m	1

Committer Domains (Top 20 + Academic)

arstucson.net: 1 proton.me: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 28
Total pull requests: 124
Average time to close issues: 4 days
Average time to close pull requests: 1 day
Total issue authors: 10
Total pull request authors: 9
Average comments per issue: 2.64
Average comments per pull request: 1.23
Merged pull requests: 115
Bot issues: 0
Bot pull requests: 24

Past Year

Issues: 4
Pull requests: 31
Average time to close issues: about 2 hours
Average time to close pull requests: 6 days
Issue authors: 3
Pull request authors: 5
Average comments per issue: 0.25
Average comments per pull request: 1.32
Merged pull requests: 25
Bot issues: 0
Bot pull requests: 11

View more stats

Top Authors

Issue Authors

giswqs (14)
platipodium (6)
ohadshapira (2)
smcclatchie (1)
gponce-ars (1)
sonicviz (1)
jessjaco (1)
marklit (1)
Tartomas (1)
iacallejas (1)

Pull Request Authors

giswqs (175)
dependabot[bot] (17)
pre-commit-ci[bot] (12)
allcontributors[bot] (12)
bingqing-liu (6)
slowy07 (6)
alexgleith (2)
advait-0 (2)
gponce-ars (2)
Bingqing9027 (1)

Top Labels

Issue Labels

bug (12)

Pull Request Labels

dependencies (17) github_actions (10) ready-to-merge (7) python (7) already reviewed (3)

Packages

Total packages: 3
Total downloads:
- pypi 5,247 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 132
Total maintainers: 2

proxy.golang.org: github.com/opengeos/HyperCoast

Documentation: https://pkg.go.dev/github.com/opengeos/HyperCoast#section-documentation
License: mit
Latest release: v0.14.1
published 4 months ago

Versions: 44
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 4 months ago

proxy.golang.org: github.com/opengeos/hypercoast

Documentation: https://pkg.go.dev/github.com/opengeos/hypercoast#section-documentation
License: mit
Latest release: v0.14.1
published 4 months ago

Versions: 44
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 4 months ago

pypi.org: hypercoast

A Python package for processing hyperspectral data in coastal regions

Homepage: https://github.com/opengeos/HyperCoast
Documentation: https://hypercoast.readthedocs.io/
License: MIT License
Latest release: 0.14.1
published 4 months ago

Versions: 44
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 5,247 Last month

Rankings

Dependent packages count: 9.6%

Average: 36.4%

Dependent repos count: 63.2%

Maintainers (2)

giswqs bingqingliu

Last synced: 4 months ago

Dependencies

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pyproject.toml pypi

requirements.txt pypi

numpy *

requirements_dev.txt pypi

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nbconvert * development
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pymdown-extensions * development
pytest * development
pytest-runner * development
sphinx * development
twine * development
watchdog * development
wheel * development

HyperCoast

Science Score: 98.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

HyperCoast

Introduction

Citations

Features

Demos

Acknowledgement

License

Contributors

Owner

JOSS Publication

HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Authors

Editor

Tags

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/opengeos/HyperCoast

Rankings

proxy.golang.org: github.com/opengeos/hypercoast

Rankings

pypi.org: hypercoast

Rankings

Maintainers (2)

Dependencies