Releases: compgeolab/xlandsat
v0.5.0
Released on: 2024/04/10
doi: https://doi.org/10.5281/zenodo.10953496
Bug fixes:
- Fix issue with radiance conversion of Level 1 thermal bands (#73)
- Add missing Cirrus and Thermal 2 bands to IO functions (#63)
New features:
- Add function
adjust_l1_colors
to fix the colors of an RGB composite made from Level 1 data (#76) - Add an option to download Level 1 data from the Momotombo eruption (#75)
- Add an Level 1 scene for Momotombo (#74)
- Allow choosing the dtype of composites (#64)
Documentation:
Maintenance:
- Extend testing and support for Python 3.12 (#80)
- Switch to pyproject.toml for package configuration (#78)
- Use PyPI Trusted Publishers instead of API tokens (#67)
- Use dependabot to manage GitHub Actions upgrades (#66)
- Add social media card images (#62)
This release contains contributions from:
- Leonardo Uieda
v0.4.0
Released on: 2023/09/28
doi: https://doi.org/10.5281/zenodo.8348805
Bug fixes:
- Bug fix: Use np.abs in pansharpen to avoid sign issues (#34)
New features and improvements:
- Add function for histogram equalization (#40 #54)
- Add function to fill missing values by interpolation (#36)
- Faster version of cropped loading using less RAM (#28)
- Add sample data for Manaus, Brazil (#49)
- Add sample data from Mount Roraima (#39)
- Add sample data for the Momotombo eruption (#32)
Documentation:
- Tutorial about the many ways to read data (#57)
- Add section on Indices tutorial about other indices (#55)
- Add a trigger warning and update the indices tutorial (#53)
- Use the Manaus data in the Composites tutorial (#52)
- Expand the Overview page and use the Manaus data (#51)
- Fill missing and equalization in overlay tutorial (#45)
- Fix wrong mention of color in indices tutorial (#44)
- Rework and expand the composites tutorial (#37)
- Add a tutorial on how to overlay bands on RGB (#33)
- Make README preview image full width (#26)
- Add a logo for the project (#43)
- Add Community guides (#27)
Maintenance:
- Add Issue templates with a release checklist (#58)
- Add missing Scipy dependency (#56)
- Update Leo's affiliation to USP (#50)
- Fix version number for uploading to TestPyPI (#48)
- Fetch data files from GitHub instead of figshare (#47)
- Host sample data files on GitHub (#46)
- Add testing and support for Python 3.11 (#35)
- Use the tifffile plugin for IO instead of PIL (#31)
- Update jupyter-sphinx and add ipykernel (#30)
- Move the Authorship Guidelines to the lab manual (#29)
This release contains contributions from:
- Leonardo Uieda
v0.3.0
Released on: 2023/02/08
doi: https://doi.org/10.5281/zenodo.7619773
New features:
- Create a datasets module to automate downloading: #24
- Add pansharpening (Weighted Brovey Transform): #23
Documentation:
- Add a favicon image to the docs: #22
This release contains contributions from:
- Leonardo Uieda
v0.2.0
v0.1.0
Released on: 2022/12/06
doi: https://doi.org/10.5281/zenodo.7402498
New features:
Documentation:
- Add full docstrings to the 2 API functions: #3
- Use a p instead of h2 for the README tagline: #4
- Add full docstrings to the 2 API functions: #5
- Use the "all versions" Zenodo DOI in citation: #7
- Expand and populate the Overview tutorial: #8
Maintenance:
- Default to float16 for loading scenes: #15
- Add codecov configuration file to control reports: #6
- Add tests for folder reading and > 1 MTL files: #11
- Add test for missing metadata files: #12
This release contains contributions from:
- Leonardo Uieda
v0.0.1
Released on: 2022/12/04
doi: https://doi.org/10.5281/zenodo.7395474
First release of xlandsat! This first release provides:
- A function to get Landsat 8 and 9 Collection 2 Level 2 scenes downloaded from USGS EarthExplorer into an
xarray.Dataset
. - A function to generate a composite as an
xarray.DataArray
that can be easily plotted with xarray's machinery.
Here's a quick example:
import xlandsat as xls
import pooch # for downloading the sample scene
# Download the scene as a tar archive
path = pooch.retrieve(
"doi:10.6084/m9.figshare.21665630.v1/cropped-after.tar.gz",
known_hash="md5:4ae61a2d7a8b853c727c0c433680cece",
)
# Load the scene directly from the archive (no need to unpack it)
scene = xls.load_scene(path)
# Make an RGB composite and add it to the scene Dataset
scene = scene.assign(rgb=xls.composite(scene, rescale_to=[0, 0.2]))
# Plot the composite
scene.rgb.plot.imshow()
This release contains contributions from:
- Leonardo Uieda