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‎.github/workflows/ci.yml

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name: CI
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on:
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push:
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branches:
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- main
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pull_request:
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branches:
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- main
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.10"]
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3 # This is already the latest version
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- name: Set up Python
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uses: actions/setup-python@v4 # This is also the latest version
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install .[dev] # Use dev to install pytest
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- name: Run tests
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run: |
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pytest

‎.github/workflows/python-publish.yml

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# This workflow will upload a Python Package using Twine when a release is created
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# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
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# This workflow uses actions that are not certified by GitHub.
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# They are provided by a third-party and are governed by
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# separate terms of service, privacy policy, and support
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# documentation.
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name: Upload Python Package
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on:
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release:
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types: [published]
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permissions:
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contents: read
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jobs:
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deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v3
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with:
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python-version: '3.x'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install build
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- name: Build package
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run: python -m build
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- name: Publish package
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uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
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with:
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user: __token__
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password: ${{ secrets.PYPI_API_TOKEN }}

‎README.md

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# verma-net-radiation
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Net Radiation and Daily Upscaling Remote Sensing in Python
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# Net Radiation and Daily Upscaling Remote Sensing in Python
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This Python package implements the net radiation and daily upscaling methods described in Verma et al 2016.
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[Gregory H. Halverson](https://github.com/gregory-halverson-jpl) (they/them)<br>
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[gregory.h.halverson@jpl.nasa.gov](mailto:gregory.h.halverson@jpl.nasa.gov)<br>
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Lead developer<br>
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NASA Jet Propulsion Laboratory 329G
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## References
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Verma, M., Fisher, J. B., Mallick, K., Ryu, Y., Kobayashi, H., Guillaume, A., Moore, G., Ramakrishnan, L., Hendrix, V. C., Wolf, S., Sikka, M., Kiely, G., Wohlfahrt, G., Gielen, B., Roupsard, O., Toscano, P., Arain, A., & Cescatti, A. (2016). Global surface net-radiation at 5 km from MODIS Terra. *Remote Sensing, 8*, 739. [Link](https://api.semanticscholar.org/CorpusID:1517647)

‎makefile

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.PHONY: dist
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PACKAGE_NAME = verma-net-radiation
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ENVIRONMENT_NAME = $(PACKAGE_NAME)
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DOCKER_IMAGE_NAME = $(PACKAGE_NAME)
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clean:
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rm -rf *.o *.out *.log
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rm -rf build/
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rm -rf dist/
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rm -rf *.egg-info
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rm -rf .pytest_cache
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find . -type d -name "__pycache__" -exec rm -rf {} +
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test:
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pytest
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build:
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python -m build
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twine-upload:
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twine upload dist/*
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dist:
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make clean
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make build
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make twine-upload
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remove-environment:
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mamba env remove -y -n $(ENVIRONMENT_NAME)
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install:
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pip install -e .[dev]
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uninstall:
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pip uninstall $(PACKAGE_NAME)
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reinstall:
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make uninstall
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make install
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environment:
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mamba create -y -n $(ENVIRONMENT_NAME) -c conda-forge python=3.10
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colima-start:
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colima start -m 16 -a x86_64 -d 100
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docker-build:
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docker build -t $(DOCKER_IMAGE_NAME):latest .
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docker-build-environment:
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docker build --target environment -t $(DOCKER_IMAGE_NAME):latest .
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docker-build-installation:
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docker build --target installation -t $(DOCKER_IMAGE_NAME):latest .
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docker-interactive:
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docker run -it $(DOCKER_IMAGE_NAME) fish
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docker-remove:
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docker rmi -f $(DOCKER_IMAGE_NAME)

‎pyproject.toml

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[build-system]
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requires = ["setuptools", "wheel"]
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[project]
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name = "verma-net-radiation"
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version = "1.0.1"
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description = "Net Radiation and Daily Upscaling Remote Sensing in Python"
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readme = "README.md"
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authors = [
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{ name = "Gregory Halverson", email = "gregory.h.halverson@jpl.nasa.gov" }
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]
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classifiers = [
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"Programming Language :: Python :: 3",
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"Operating System :: OS Independent",
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]
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dependencies = [
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"numpy",
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"rasters"
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]
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requires-python = ">=3.10"
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[project.optional-dependencies]
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dev = [
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"build",
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"pytest>=6.0",
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"pytest-cov",
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"jupyter",
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"pytest",
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"twine"
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]
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[tool.setuptools.package-data]
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verma_net_radiation = ["*.txt", "*.tif"]
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[project.urls]
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"Homepage" = "https://github.com/JPL-Evapotranspiration-Algorithms/verma-net-radiation"

‎tests/test_import_dependencies.py

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import pytest
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# List of dependencies
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dependencies = [
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"numpy",
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"rasters"
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]
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# Generate individual test functions for each dependency
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@pytest.mark.parametrize("dependency", dependencies)
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def test_dependency_import(dependency):
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__import__(dependency)

‎tests/test_import_verma_net_radiation.py

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def test_import_verma_net_radiation():
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import verma_net_radiation

‎verma_net_radiation/__init__.py

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from .verma_net_radiation import *
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from os.path import join, abspath, dirname
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with open(join(abspath(dirname(__file__)), "version.txt")) as f:
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version = f.read()
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__version__ = version
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__author__ = "Gregory H. Halverson"

‎verma_net_radiation/verma_net_radiation.py

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from typing import Union, Dict
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import warnings
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import numpy as np
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import rasters as rt
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from rasters import Raster
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STEFAN_BOLTZMAN_CONSTANT = 5.67036713e-8 # SI units watts per square meter per kelvin to the fourth
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def daylight_from_SHA(SHA_deg: Union[Raster, np.ndarray]) -> Union[Raster, np.ndarray]:
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"""
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This function calculates daylight hours from sunrise hour angle in degrees.
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"""
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return (2.0 / 15.0) * SHA_deg
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def sunrise_from_SHA(SHA_deg: Union[Raster, np.ndarray]) -> Union[Raster, np.ndarray]:
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"""
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This function calculates sunrise hour from sunrise hour angle in degrees.
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"""
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return 12.0 - (SHA_deg / 15.0)
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def solar_dec_deg_from_day_angle_rad(day_angle_rad: Union[Raster, np.ndarray]) -> Union[Raster, np.ndarray]:
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"""
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This function calculates solar declination in degrees from day angle in radians.
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"""
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return (0.006918 - 0.399912 * np.cos(day_angle_rad) + 0.070257 * np.sin(day_angle_rad) - 0.006758 * np.cos(
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2 * day_angle_rad) + 0.000907 * np.sin(2 * day_angle_rad) - 0.002697 * np.cos(
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3 * day_angle_rad) + 0.00148 * np.sin(
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3 * day_angle_rad)) * (180 / np.pi)
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def day_angle_rad_from_doy(doy: Union[Raster, np.ndarray]) -> Union[Raster, np.ndarray]:
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"""
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This function calculates day angle in radians from day of year between 1 and 365.
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"""
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return (2 * np.pi * (doy - 1)) / 365
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def SHA_deg_from_doy_lat(doy: Union[Raster, np.ndarray, int], latitude: np.ndarray) -> Raster:
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"""
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This function calculates sunrise hour angle in degrees from latitude in degrees and day of year between 1 and 365.
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"""
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# calculate day angle in radians
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day_angle_rad = day_angle_rad_from_doy(doy)
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# calculate solar declination in degrees
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solar_dec_deg = solar_dec_deg_from_day_angle_rad(day_angle_rad)
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# convert latitude to radians
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latitude_rad = np.radians(latitude)
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# convert solar declination to radians
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solar_dec_rad = np.radians(solar_dec_deg)
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# calculate cosine of sunrise angle at latitude and solar declination
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# need to keep the cosine for polar correction
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sunrise_cos = -np.tan(latitude_rad) * np.tan(solar_dec_rad)
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# calculate sunrise angle in radians from cosine
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore")
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sunrise_rad = np.arccos(sunrise_cos)
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# convert to degrees
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sunrise_deg = np.degrees(sunrise_rad)
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# apply polar correction
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sunrise_deg = rt.where(sunrise_cos >= 1, 0, sunrise_deg)
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sunrise_deg = rt.where(sunrise_cos <= -1, 180, sunrise_deg)
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return sunrise_deg
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def process_verma_net_radiation(
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SWin: np.ndarray,
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albedo: np.ndarray,
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ST_C: np.ndarray,
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emissivity: np.ndarray,
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Ta_C: np.ndarray,
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RH: np.ndarray,
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cloud_mask: np.ndarray = None) -> Dict:
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results = {}
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# Convert surface temperature from Celsius to Kelvin
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ST_K = ST_C + 273.15
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# Convert air temperature from Celsius to Kelvin
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Ta_K = Ta_C + 273.15
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# Calculate water vapor pressure in Pascals using air temperature and relative humidity
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Ea_Pa = (RH * 0.6113 * (10 ** (7.5 * (Ta_K - 273.15) / (Ta_K - 35.85)))) * 1000
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# constrain albedo between 0 and 1
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albedo = np.clip(albedo, 0, 1)
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# calculate outgoing shortwave from incoming shortwave and albedo
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SWout = np.clip(SWin * albedo, 0, None)
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results["SWout"] = SWout
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# calculate instantaneous net radiation from components
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SWnet = np.clip(SWin - SWout, 0, None)
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# calculate atmospheric emissivity
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eta1 = 0.465 * Ea_Pa / Ta_K
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# atmospheric_emissivity = (1 - (1 + eta1) * np.exp(-(1.2 + 3 * eta1) ** 0.5))
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eta2 = -(1.2 + 3 * eta1) ** 0.5
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eta2 = eta2.astype(float)
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eta3 = np.exp(eta2)
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atmospheric_emissivity = np.where(eta2 != 0, (1 - (1 + eta1) * eta3), np.nan)
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if cloud_mask is None:
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# calculate incoming longwave for clear sky
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LWin = atmospheric_emissivity * STEFAN_BOLTZMAN_CONSTANT * Ta_K ** 4
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else:
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# calculate incoming longwave for clear sky and cloudy
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LWin = np.where(
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~cloud_mask,
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atmospheric_emissivity * STEFAN_BOLTZMAN_CONSTANT * Ta_K ** 4,
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STEFAN_BOLTZMAN_CONSTANT * Ta_K ** 4
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)
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results["LWin"] = LWin
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# constrain emissivity between 0 and 1
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emissivity = np.clip(emissivity, 0, 1)
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# calculate outgoing longwave from land surface temperature and emissivity
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LWout = emissivity * STEFAN_BOLTZMAN_CONSTANT * ST_K ** 4
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results["LWout"] = LWout
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# LWnet = np.clip(LWin - LWout, 0, None)
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LWnet = np.clip(LWin - LWout, 0, None)
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# constrain negative values of instantaneous net radiation
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Rn = np.clip(SWnet + LWnet, 0, None)
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results["Rn"] = Rn
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return results
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def daily_Rn_integration_verma(
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Rn: Union[Raster, np.ndarray],
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hour_of_day: Union[Raster, np.ndarray],
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doy: Union[Raster, np.ndarray] = None,
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lat: Union[Raster, np.ndarray] = None,
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sunrise_hour: Union[Raster, np.ndarray] = None,
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daylight_hours: Union[Raster, np.ndarray] = None) -> Raster:
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"""
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calculate daily net radiation using solar parameters
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this is the average rate of energy transfer from sunrise to sunset
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in watts per square meter
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watts are joules per second
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to get the total amount of energy transferred, factor seconds out of joules
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the number of seconds for which this average is representative is (daylight_hours * 3600)
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documented in verma et al, bisht et al, and lagouARDe et al
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:param Rn:
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:param hour_of_day:
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:param sunrise_hour:
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:param daylight_hours:
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:return:
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"""
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if daylight_hours is None or sunrise_hour is None and doy is not None and lat is not None:
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sha_deg = SHA_deg_from_doy_lat(doy, lat)
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daylight_hours = daylight_from_SHA(sha_deg)
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sunrise_hour = sunrise_from_SHA(sha_deg)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore")
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Rn_daily = 1.6 * Rn / (np.pi * np.sin(np.pi * (hour_of_day - sunrise_hour) / (daylight_hours)))
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return Rn_daily

‎verma_net_radiation/version.txt

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1.0.1

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