This repo contains a python package src and a collection of notebooks for downloading and stacking ECOSTRESS L2,L3,L4 and ERA5 Land reanalysis data. Scripts were used to download these data over the rhone, resample all ecostress scenes to the same downsampled grid as ERA5 Land, and to organize these data products into a single xarray dataset with a date axis for calculating potential ET (from ERA and ECOSTRESS L3) and actual ET (from ECOSTRESS) where such data was available. The end goal was to subset these products to riparian river corridors and examine patterns of water use during times of high and low streamflow, but project was put on hold for a while due to other responsibilities and quality issues with the ECOSTRESS data.
Geospatial python dependencies are most reliably installed via conda forge in a fresh environment that uses Python 3.6 (not 3.7). installing from yaml files often doesn't work so set it up with
conda create -n geo
conda activate geo
conda install -c conda-forge dask rioxarray geocube xarray rasterio scikit-image scipy numpy pandas descartes mapclassify rtree ipykernel
python setup.py develop
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience