pywatershed 0.1.2

pywatershed is a Python package for hydrologic modeling

pywatershed

Purpose

The purpose of this repository is to refactor and redesign the PRMS modeling system while maintaining its functionality. Code modernization is a step towards unification with MODFLOW 6 (MF6).

The following motivations are taken from our AGU poster from December 2022 which provides additional details on motivations, project status, and current directions of this project as of approximately January 2023.

Goals of the USGS Enterprise Capacity (EC) project include:

• A sustainable integrated, hydrologic modeling framework for the U.S. Geological Survey (USGS)
• Interoperable modeling across the USGS, partner agencies, and academia

Goals for EC Watershed Modeling:

• Couple the Precipitation-Runoff Modeling System (PRMS, e.g. Regan et al, 2018)  with MODFLOW 6 (MF6, e.g. Langevin et al, 2017) in a sustainable way
• Redesign PRMS to be more modern and flexible
• Prioritize process representations in the current National Hydrological Model (NHM) based on PRMS 5.2.1

Prototype an EC watershed model: "pywatershed"

• Redesign PRMS quickly in python
• Couple to MF6 via BMI/XMI interface (Hughes et al, 2021; Hutton et al, 2020)
• Establish a prototyping ground for EC codes that couples to the compiled framework: low cost proof of concepts (at the price of potentially less computational performance)
• Enable process representation hypothesis testing
• Use cutting-edge techniques and technologies to improve models
• Machine learning, automatic differentiation
• Address challenges of modeling across space and time scales
• Transition prototype watershed model to compiled EC code

Installation and Python Environments

This installation assumes you do not need to run automated tests locally. See the following "Developer Installation" section for complete installation instructions.

To install the software you will need Python >= 3.8. We recommend installing the python package dependencies using anaconda or miniconda. Most users will likely want to create the pyws_nb conda environment by running

conda env create -f examples/examples_env.yml.

One could also do

pip install -r examples/exampes_env.txt

but this is not guaranteed.

Once the environment is established, activate the environment and install pywatershed

conda activate pyws_nb; cd pywatershed; pip install .

If you would like to compile the fortran computational kernels for certain physical process representations (not required), you'll need a fortran compiler and you will run

export PYWS_FORTRAN=true; cd pywatershed; pip install .

See Developer Requirements below for more details.

Developer Installation

Git is required if you plan to contribute code back to the repository.

C and Fortran compilers are required. We are currently using gnu (gcc, gfortran) 11 and 12 as well as intel (icc, ifort) 2021 on Windows, Linux, and MacOS (including Apple Silicon). Both of these are freely obtainable but the installation process varies widely. We are looking for a conda-based approach to obtaining compilers, but currently do not have a solution. Compilers are needed for two applications:

1. Compiling and running C/Fortran PRMS code to generate testing/verification data
2. Compiling (installing) and running fortran backends/kernels for some hydrological process representations in pywatershed

On Apple Silicon, the PRMS source code is only currently known to compile with intel while the fortran kernels in pywatershed only compile with gnu.

Python >= 3.8 is required. Three different python environments are specified within the repository. These are:

• Minimal (for developing/testing), 'pyws': ci/requirements/environment.yml
• Notebooks (~= minimal + jupyter), 'pyws_nb': examples/examples_env.yml
• Documentation (only if you want to build the documentation), 'pyws-docs': ci/requirements/doc.yml

We recommend (because we test it in CI) using anacoda or miniconda to establish these environments with the following commands

conda env create -f path/to/env_of_choice.yml

which will create the environment with "name" specified on the first line of the file, given before the path to the file above.

More detailed python environment installation instructions using conda can be found in examples/00_python_virtual_env.ipynb.

There are also .txt equivalents that can be used for installing from pip, like so:

pip install -r env_of_choice.txt

though these are not comprehensive installs as with conda and not tested.

Once the python environment and dependencies are established and activated (conda activate env_of_choice), pywatershed is installed for development into that environment with the following command

cd pywatershed; pip install -e .

The numpy extension F2PY is used to provide fortran compiled kernels of core calculations to boost performance. F2PY is documented within numpy. This repository is configured NOT to compile on install by default. Currently, we have not established this compilation procedure for Windows. On linux and MacOS, compilation of fortran kernels on package installation is achieved by the following code:

export SETUPTOOLS_ENABLE_FEATURES="legacy-editable"
export CC=path/to/gcc  # for example
export FC=path/to/gfortran  # for example
export PYWS_FORTRAN=true
cd path/to/pywatershed
pip install -e .

Once the dependencies are available, we want to verify the software by running its test suite. The following testing procedures are also covered in the notebook examples/01_automated_testing.ipynb. To run the tests, we first need to generate the test data. This consists of running PRMS and then converting the output to netcdf:

cd path/to/pywatershed/test_data/scripts
pytest -v -n=4 test_run_domains.py
pytest -v -n=8 test_nc_domains.py

Finally, run the tests themselves,

cd path/to/pywatershed/autotest
pytest -v -n=8

All tests should pass, XPASS, or XFAIL. XFAIL is an expected failure.

Contributing

We welcome community development! Please file Issues and/or Pull Requests in the appropriate places on github. The continuous integration (CI) procedure is the first gate keeper for new code contribution. The CI procedure is defined by .github/workflows/ci.yaml. This includes running the formatting and linting packages isort, black, and flake8 in addition to generating the test data and running the tests in autotest/. New codes need new tests so they can be verified moving ahead in time.

Example Notebooks

Jupyter notebooks containing examples are found in the examples/ directory. Numbered notebooks are tested. Notebooks 00 and 01 walk the user through the setting the python environment and running the software tests. Notebook 02 demonstrates modeling with pywatershed. Non-numbered notebooks cover additional topics. These notebooks are note yet covered by testing and so may be expected to have some issues until they are added to testing.

Overview of Repository Contents

The contents of directories at this level is described. Therein you may discover another README.md for more information.

.github/    Github actions for deploying continuous integration (CI)
autotest/   pywatershed package testing using pytest
bin/        PRMS executables distributed
ci/         Python environments for CI
doc/        Package/code documentation source code
examples/   How to use the package, mostly jupyter notebooks
prms_src/   PRMS source used for generating executables in bin/
pywatershed/      Package source
reference/  Ancillary materials for development
resources/  Static stuff like images
test_data/  Data used for automated testing

Disclaimer

This information is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The information has not received final approval by the U.S. Geological Survey (USGS) and is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the information.

From: https://www2.usgs.gov/fsp/fsp_disclaimers.asp#5

This software is in the public domain because it contains materials that originally came from the U.S. Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy

Although this software program has been used by the USGS, no warranty, expressed or implied, is made by the USGS or the U.S. Government as to the accuracy and functioning of the program and related program material nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by the USGS in connection therewith. This software is provided "AS IS."