Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/weiji14/deepicedrain
Mapping and monitoring deep subglacial water activity in Antarctica using remote sensing and machine learning, with ICESat-2!
https://github.com/weiji14/deepicedrain
analysis-ready-data antarctica big-data binder datashader hdf5 ice-sheet icesat-2 intake jupyter-lab open-science pygmt python3 zarr
Last synced: 17 days ago
JSON representation
Mapping and monitoring deep subglacial water activity in Antarctica using remote sensing and machine learning, with ICESat-2!
- Host: GitHub
- URL: https://github.com/weiji14/deepicedrain
- Owner: weiji14
- License: lgpl-3.0
- Created: 2019-10-09T02:02:05.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2022-10-18T21:10:22.000Z (about 2 years ago)
- Last Synced: 2024-05-01T15:10:50.836Z (6 months ago)
- Topics: analysis-ready-data, antarctica, big-data, binder, datashader, hdf5, ice-sheet, icesat-2, intake, jupyter-lab, open-science, pygmt, python3, zarr
- Language: Jupyter Notebook
- Homepage:
- Size: 29.6 MB
- Stars: 28
- Watchers: 2
- Forks: 8
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- open-sustainable-technology - DeepIceDrain - Mapping and monitoring deep subglacial water activity in Antarctica using remote sensing and machine learning. (Cryosphere / Glacier and Ice Sheets)
README
# DeepIceDrain [[poster]](https://github.com/weiji14/nzasc2021)
Mapping and monitoring deep subglacial water activity
in Antarctica using remote sensing and machine learning.
[](https://doi.org/10.5281/zenodo.4071235)
[](https://github.com/ambv/black)
[](https://github.com/weiji14/deepicedrain/actions/workflows/python-app.yml)
[](https://dependabot.com)
| Ice Surface Elevation trends over Antactica | Active Subglacial Lake fill-drain event |
|---|---|
|  |  |
|atlxi_dhdt.ipynb],[Height-Change-over-Time-(dhdt)]->[Subglacial-Lake-Finder|atlxi_lake.ipynb],[Subglacial-Lake-Finder]->[Crossover-Analysis|atlxi_xover.ipynb])
| Along track view of an ATL11 Ground Track | Elevation time-series at Crossover Points |
|---|---|
|  |  |
# Getting started
## Quickstart
Launch in [Binder](https://mybinder.readthedocs.io) (Interactive jupyter lab environment in the cloud).
[](https://mybinder.org/v2/gh/weiji14/deepicedrain/main)
Alternative [Pangeo BinderHub](https://pangeo-binder.readthedocs.io) link.
Requires a GitHub account and you'll have to install your own computing environment,
but it runs on AWS uswest2 which allows for
[cloud access to ICESat-2](https://nsidc.org/data/user-resources/data-announcements/data-set-updates-new-earthdata-cloud-access-option-icesat-2-and-icesat-data-sets)!
[](https://hub.aws-uswest2-binder.pangeo.io/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fweiji14%2Fdeepicedrain&urlpath=lab%2Ftree%2Fdeepicedrain%2F&branch=main)
## Usage
Once you've properly installed the [`deepicedrain` package](deepicedrain)
(see installation instructions further below), you'll have access to a
[wide range of tools](https://github.com/weiji14/deepicedrain/tree/main/deepicedrain)
for downloading and performing quick calculations on ICESat-2 datasets.
The example below shows how to calculate ice surface elevation change
on a sample ATL11 dataset between ICESat's Cycle 3 and Cycle 4.
import deepicedrain
import xarray as xr
# Loads a sample ATL11 file from the intake catalog into xarray
atl11_dataset: xr.Dataset = deepicedrain.catalog.test_data.atl11_test_case.read()
# Calculate elevation change in metres from ICESat-2 Cycle 3 to Cycle 4
delta_height: xr.DataArray = deepicedrain.calculate_delta(
dataset=atl11_dataset, oldcyclenum=3, newcyclenum=4, variable="h_corr"
)
# Quick plot of delta_height along the ICESat-2 track
delta_height.plot()
## Installation
### Basic
To just try out the scripts, download the `environment.yml` file from the repository and run the commands below:
cd deepicedrain
mamba env create --name deepicedrain --file environment.yml
pip install git+https://github.com/weiji14/deepicedrain.git
### Intermediate
To help out with development, start by cloning this [repo-url](/../../)
git clone
Then I recommend [using mamba](https://mamba.readthedocs.io/en/latest/installation.html)
to install the non-python binaries.
A virtual environment will also be created with Python and
[poetry](https://github.com/python-poetry/poetry) installed.
cd deepicedrain
mamba env create --file environment.yml
Activate the virtual environment first.
mamba activate deepicedrain
Then install the python libraries listed in the `pyproject.toml`/`poetry.lock` file.
poetry install
Finally, double-check that the libraries have been installed.
poetry show
### Advanced
This is for those who want full reproducibility of the virtual environment,
and more computing power by using Graphical Processing Units (GPU).
Making an explicit [conda-lock](https://github.com/conda-incubator/conda-lock) file
(only needed if creating a new virtual environment/refreshing an existing one).
mamba env create -f environment.yml
mamba list --explicit > environment-linux-64.lock
Creating/Installing a virtual environment from a conda lock file.
See also https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#building-identical-conda-environments.
mamba create --name deepicedrain --file environment-linux-64.lock
mamba install --name deepicedrain --file environment-linux-64.lock
If you have a [CUDA](https://en.wikipedia.org/wiki/CUDA)-capable GPU,
you can also install the optional "cuda" packages to accelerate some calculations.
poetry install --extras cuda
## Running jupyter lab
mamba activate deepicedrain
python -m ipykernel install --user --name deepicedrain # to install virtual env properly
jupyter kernelspec list --json # see if kernel is installed
jupyter lab &
## Related Projects
This work would not be possible without inspiration
from the following cool open source projects!
Go check them out if you have time.
- [ATL11](https://github.com/suzanne64/ATL11)
- [ICESAT-2 HackWeek](https://github.com/ICESAT-2HackWeek)
- [icepyx](https://github.com/icesat2py/icepyx)
## Citing
The work in this repository has not been peer-reviewed, but if you do want to
cite it for some reason, use the following BibLaTeX code from this conference
proceedings ([poster presentation](https://github.com/weiji14/nzasc2021)):
@inproceedings{LeongSpatiotemporalvariabilityactive2021,
title = {{Spatiotemporal Variability of Active Subglacial Lakes in Antarctica from 2018-2020 Using ICESat-2 Laser Altimetry}},
author = {Leong, W. J. and Horgan, H. J.},
date = {2021-02-10},
publisher = {{Unpublished}},
location = {{Christchurch, New Zealand}},
doi = {10.13140/RG.2.2.27952.07680},
eventtitle = {{New Zealand Antarctic Science Conference}}},
langid = {english}
}
Python code for the DeepIceDrain package here on Github is also mirrored on Zenodo at https://doi.org/10.5281/zenodo.4071235.