Find active subglacial lake points using unsupervised clustering

.github/workflows/python-app.yml

@@ -2,9 +2,11 @@ name: Test DeepIceDrain
 
 on:
   push:
-    branches: [ master ]
+    branches:
+      - master
   pull_request:
-    branches: [ master ]
+    branches:
+      - "**"
 
 jobs:
   test:

README.md

@@ -11,7 +11,7 @@ in Antarctica using remote sensing and machine learning.
 
 ![ICESat-2 ATL11 rate of height change over time in Antarctica 2018-10-14 to 2020-05-13](https://user-images.githubusercontent.com/23487320/90118294-2601ff80-ddac-11ea-8b93-7bc9b15f2be0.png)
 
-![DeepIceDrain Pipeline](https://yuml.me/diagram/scruffy;dir:LR/class/[Land-Ice-Elevation|atl06_play.ipynb]->[Convert|atl06_to_atl11.ipynb],[Convert]->[Ice-Sheet-H(t)-Series|atl11_play.ipynb],[Ice-Sheet-H(t)-Series]->[Height-Change-over-Time-(dhdt)|atlxi_dhdt.ipynb])
+![DeepIceDrain Pipeline](https://yuml.me/diagram/scruffy;dir:LR/class/[Land-Ice-Elevation|atl06_play.ipynb]->[Convert|atl06_to_atl11.ipynb],[Convert]->[Ice-Sheet-H(t)-Series|atl11_play.ipynb],[Ice-Sheet-H(t)-Series]->[Height-Change-over-Time-(dhdt)|atlxi_dhdt.ipynb],[Height-Change-over-Time-(dhdt)]->[Subglacial-Lake-Finder|atlxi_lake.ipynb])
 
 # Getting started
 
@@ -57,7 +57,7 @@ To just try out the scripts, download the `environment.yml` file from the reposi
     conda env create --name deepicedrain --file environment.yml
     pip install git+https://github.com/weiji14/deepicedrain.git
 
-### Advanced
+### Intermediate
 
 To help out with development, start by cloning this [repo-url](/../../)
 
@@ -77,11 +77,6 @@ Then install the python libraries listed in the `pyproject.toml`/`poetry.lock` f
 
     poetry install
 
-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
-
 Finally, double-check that the libraries have been installed.
 
     poetry show
@@ -93,6 +88,30 @@ Finally, double-check that the libraries have been installed.
 
     jupyter labextension list  # ensure that extensions are installed
 
+
+### Advanced
+
+This is for those who want full reproducibility of the conda environment,
+and more computing power by using Graphical Processing Units (GPU).
+
+Making an explicit conda-lock file
+(only needed if creating a new conda environment/refreshing an existing one).
+
+    conda env create -f environment.yml
+    conda 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.
+
+    conda create --name deepicedrain --file environment-linux-64.lock
+    conda 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
 
     conda activate deepicedrain

atl11_play.ipynb

@@ -303,7 +303,7 @@
     "# Do the actual computation to find data points within region of interest\n",
     "placename: str = \"kamb\"  # Select Kamb Ice Stream region\n",
     "region: deepicedrain.Region = regions[placename]\n",
-    "ds_subset: xr.Dataset = region.subset(ds=ds)\n",
+    "ds_subset: xr.Dataset = region.subset(data=ds)\n",
     "ds_subset = ds_subset.unify_chunks()\n",
     "ds_subset = ds_subset.compute()"
    ]

atl11_play.py

@@ -181,7 +181,7 @@
 # Do the actual computation to find data points within region of interest
 placename: str = "kamb"  # Select Kamb Ice Stream region
 region: deepicedrain.Region = regions[placename]
-ds_subset: xr.Dataset = region.subset(ds=ds)
+ds_subset: xr.Dataset = region.subset(data=ds)
 ds_subset = ds_subset.unify_chunks()
 ds_subset = ds_subset.compute()