hybig-py 2.0.0

Python package designed to produce browse imagery compatible with NASA's Global Image Browse Services (GIBS).

Harmony Browse Image Generator (HyBIG).

This repository contains code designed to produce browse imagery. Its default behaviour produces images compatible with the NASA Global Image Browse Services (GIBS).

This means that default parameters for images are selected to match the visualization generation requirements and recommendations put forth in the GIBS Interface Control Document (ICD), which can be found on Earthdata Wiki along with additional GIBS documentation.

HyBIG creates paletted PNG images and associated metadata from GeoTIFF input images. Scientific parameter raster data as well as RGB[A] raster images can be converted to browse PNGs. These browse images undergo transformation by reprojection, tiling and coloring to seamlessly integrate with GIBS.

The repository contains code and infrastructure to support both the HyBIG Service as well as hybig-py. The HyBIG Service is packaged as a Docker container that is deployed to NASA's Harmony system. The business logic is contained in the hybig-py library which exposes functions to generate browse images in python scripts.

hybig-py

The browse image generation logic is packaged in the hybig-py library. Currently, a single function, create_browse is exposed to the user.

def create_browse(
    source_tiff: str,
    params: dict = None,
    palette: str | ColorPalette | None = None,
    logger: Logger = None,
) -> list[tuple[Path, Path, Path]]:
    """Create browse imagery from an input geotiff.

    This is the exposed library function to allow users to create browse images
    from the hybig-py library. It parses the input params and constructs the
    correct Harmony input structure [Message.Format] to call the service's
    entry point create_browse_imagery.

    Output images are created and deposited into the input GeoTIFF's directory.

    Args:
        source_tiff: str, location of the input geotiff to process.

        params: [dict | None], A dictionary with the following keys:

            mime: [str], MIME type of the output image (default: 'image/png').
                  any string that contains 'jpeg' will return a jpeg image,
                  otherwise create a png.

            crs: [dict | None], Target image's Coordinate Reference System.
                 A dictionary with 'epsg', 'proj4' or 'wkt' key.

            scale_extent: [dict | None], Scale Extents for the image. This dictionary
                contains "x" and "y" keys each whose value which is a dictionary
                of "min", "max" values in the same units as the crs.
                e.g.: { "x": { "min": 0.5, "max": 125 },
                        "y": { "min": 52, "max": 75.22 } }

            scale_size: [dict | None], Scale sizes for the image.  The dictionary
                contains "x" and "y" keys with the horizontal and veritcal
                resolution in the same units as the crs.
                e.g.: { "x": 10, "y": 10 }

            height: [int | None], height of the output image in gridcells.

            width: [int | none], width of the output image in gridcells.

        palette: [str | ColorPalette | none], either a URL to a remote color palette
             that is fetched and loaded or a ColorPalette object used to color
             the output browse image. If not provided, a grayscale image is
             generated.

        logger: [Logger | None], a configured Logger object. If None a default
             logger will be used.

    Note:
      if supplied, scale_size, scale_extent, height and width must be
      internally consistent.  To define a valid output grid:
            * Specify scale_extent and 1 of:
              * height and width
              * scale_sizes (in the x and y horizontal spatial dimensions)
            * Specify all three of the above, but ensure values are consistent
              with one another, noting that:
              scale_size.x = (scale_extent.x.max - scale_extent.x.min) / width
              scale_size.y = (scale_extent.y.max - scale_extent.y.min) / height

    Returns:
        List of 3-element tuples. These are the file paths of:
        - The output browse image
        - Its associated ESRI world file (containing georeferencing information)
        - The auxiliary XML file (containing duplicative georeferencing information)


    Example Usage:
        results = create_browse(
            "/path/to/geotiff",
            {
                "mime": "image/png",
                "crs": {"epsg": "EPSG:4326"},
                "scale_extent": {
                    "x": {"min": -180, "max": 180},
                    "y": {"min": -90, "max": 90},
                },
                "scale_size": {"x": 10, "y": 10},
            },
            "https://remote-colortable",
            logger,
        )

    """

library installation

The hybig-py library can be installed from PyPI but has a prerequisite dependency requirement on the GDAL libraries. Ensure you have an environment with the libraries available. You can check on Linux/macOS:

gdal-config --version

on windows (if GDAL is in your PATH):

gdalinfo --version

Once verified, you can simply install the libary:

pip install hybig-py

Reprojection

GIBS expects to receive images in one of three Coordinate Reference System (CRS) projections.

Region	Code	Name
north polar	EPSG:3413	WGS 84 / NSIDC Sea Ice Polar Stereographic North
south polar	EPSG:3031	WGS 84 / Antarctic Polar Stereographic
global	EPSG:4326	WGS 84 -- WGS84 - World Geodetic System 1984, used in GPS

HyBIG processing will attempt to choose a GIBS-suitable target CRS from the input image or read it from the inputs. Reprojection is done by resampling via nearest neighbor. It is important to note that HyBig outputs are not scientific data, but browse imagery and should not be used for scientific analysis.

Tiling

Large output images are divided into smaller, more manageable tiles for efficient handling and processing, as per agreement with GIBS. The maximum untiled image size generated by HyBIG is 67,108,864 cells (8,192 x 8,192). If the output image exceeds this threshold, HyBIG automatically tiles the output into multiple 4,096 x 4,096 cell images.

Tiled images are labeled with the zero-based column and row numbers inserted into the output filename before its extension. For example, VCF5KYR_1991001_001_2018224205008.r01c02.png represents the second row and third column of the output tiles. The tiles at the edges are truncated to fit the overall image dimensions. Currently, you cannot override this behavior.

Coloring

HyBIG images are colored in several ways. A palette can be included in the input STAC Item. If an Item's asset contains a value with the role of palette, it is assumed to be a reference to a remote color table, which is fetched from the asset's href and parsed as a GDAL color table.

If the STAC Item lacks color information, the Harmony message source is searched for a related URL with a "content type" of VisualizationURL and a "type" of Color Map. If found, it is presumed to be a remote color table and fetched from that location.

In the absence of remote color information, the input image itself is searched for a color map, which is used if present.

If no color information can be found, grayscale is used.

Defaults

HyBIG tries to provide GIBS-appropriate default values for the browse image outputs. When a user does not provide a target values for the output, HyBIG will try to pick an appropriate default.

Coordinate Reference System (CRS)

HyBIG selects a default CRS from the list of GIBS preferred projections. The steps followed are simple but effective:

1. If the proj is lonlat use global (EPSG:4326)
2. If the projection latitude of origin is above 80° N use northern (EPSG:3413)
3. If the projection latitude of origin is below -80° N use southern (EPSG:3031)
4. Otherwise use global (EPGS:4326)

Scale Extent (Image Bounds)

The default scale extent for an output image is computed by reprojecting the input data boundary into the target CRS. It densifies the edges by adding 21 points (rasterio's default) to each edge before reprojection to account for non-linear edges produced by the transformation ensuring inclusion of all data in the output image.

Dimensions / Scale Sizes

Output image dimensions can be explicitly included as width and height in the harmony message or computed based on the scale extent and scale size (resolution).

The dimension computations from the scale extent and scale size:

height = round((scale_extent['ymax'] - scale_extent['ymin']) / scale_size.y)
width = round((scale_extent['xmax'] - scale_extent['xmin']) / scale_size.x)

When a Harmony message contains neither dimensions nor scaleSizes a default set of dimensions is computed.

For coarse input data, the resolution (scale size) is used with the scale extent to compute the output dimensions. For high resolution data, finer than 2km per gridcell, the input resolution is used to lookup the closest GIBS preferred resolution (Table 4.1.8-1 and -2 from the ICD) and the preferred resolution along with the scale extent is used to compute the output image dimensions.

Customizations

Users can request customizations to the output images such as crs, scale_extents, or scale_sizes and dimensions (height & width) in the harmony request. However, the generated outputs may not be compatible with GIBS.

When a user customizes scale_extent or scale_size, they must also include a crs in the request. The units of the cusomized values must match the target CRS. For example, specifying a bounding box in degrees requires a target CRS also with units of degrees.

Repository structure:

|- 📂 bin
|- 📂 docker
|- 📂 docs
|- 📂 hybig
|- 📂 harmony_service
|- 📂 tests
|- CHANGELOG.md
|- CONTRIBUTING.md
|- LICENSE
|- README.md
|- dev-requirements.txt
|- legacy-CHANGELOG.md
|- pip_requirements.txt
|- pip_requirements_skip_snyk.txt
|- pyproject.toml

• bin - A directory containing utility scripts to build the service and test images. A script to extract the release notes for the most recent version, as contained in CHANGELOG.md is also in this directory.

• docker - A directory containing the Dockerfiles for the service and test images. It also contains service_version.txt, which contains the semantic version number of the library and service image. Update this file with a new version to trigger a release.

• docs - A directory with example usage notebooks.

• hybig - A directory containing Python source code for the HyBIG library. This directory contains the business logic for generating GIBS compatible browse images.

• harmony_service - A directory containing the Harmony Service specific python code. adapter.py contains the BrowseImageGeneratorAdapter class that is invoked by calls to the Harmony service.

• tests - A directory containing the service unit test suite.

• CHANGELOG.md - This file contains a record of changes applied to each new release of HyBIG. Any release of a new version should have a record of what was changed in this file.

• CONTRIBUTING.md - This file contains guidance for making contributions to HyBIG, including recommended git best practices.

• LICENSE - Required for distribution under NASA open-source approval. Details conditions for use, reproduction and distribution.

• README.md - This file, containing guidance on developing the library and service.

• dev-requirements.txt - list of packages required for library and service development.

• legacy-CHANGELOG.md - Notes for each version that was previously released internally to EOSDIS, prior to open-source publication of the code and Docker image.

• pip_requirements.txt - A list of service Python package dependencies.

• pip_requirements_skip_snyk.txt - A list of service Python package dependencies that are not scanned by snyk for vulnerabilities. This file contains only the GDAL package. It is separated because snyk's scanning is naive and cannot pre-install required libraries so that pip install GDAL fails and we have no work around.

• pyproject.toml - Configuration file used by packaging tools, as well as other tools such as linters, type checkers, etc.

Local development:

Local testing of service functionality can be achieved via a local instance of Harmony. Please see instructions there regarding creation of a local Harmony instance.

For local development and testing of library modifications or small functions independent of the main Harmony application:

1. Create a Python virtual environment
2. Ensure GDAL libraries are accessable in the virtual environment.
3. Install the dependencies in pip_requirements.txt, pip_requirements_skip_snyk.txt and dev-requirements.txt
4. Install the pre-commit hooks.

> conda create --name hybig-env python==3.11
> pip install -r pip_requirements.txt -r pip_requirements_skip_snyk.txt
> pip install -r dev-requirements.txt

> pre-commit install

Tests:

This service utilises the Python unittest package to perform unit tests on classes and functions in the service. After local development is complete, and test have been updated, they can be run in Docker via:

$ ./bin/build-image
$ ./bin/build-test
$ ./bin/run-test

The tests/run_tests.sh script will also generate a coverage report, rendered in HTML, and scan the code with pylint.

Currently, the unittest suite is run automatically within a GitHub workflow as part of a CI/CD pipeline. These tests are run for all changes made in a PR against the main branch. The tests must pass in order to merge the PR.

Unit tests are executed automatically by github actions on each Pull Request.

Versioning:

Docker service images and the hybig-py package library adhere to semantic version numbers: major.minor.patch.

• Major increments: These are non-backwards compatible API changes.
• Minor increments: These are backwards compatible API changes.
• Patch increments: These updates do not affect the API to the service.

CI/CD:

The CI/CD for HyBIG is run on github actions with the workflows in the .github/workflows directory:

• run_lib_tests.yml - A reusable workflow that tests the library functions against the supported python versions.
• run_service_tests.yml - A reusable workflow that builds the service and test Docker images, then runs the Python unit test suite in an instance of the test Docker container.
• run_tests_on_pull_requests.yml - Triggered for all PRs against the main branch. It runs the workflow in run_service_tests.yml and run_lib_tests.yml to ensure all tests pass for the new code.
• publish_docker_image.yml - Triggered either manually or for commits to the main branch that contain changes to the docker/service_version.txt file.
• publish_to_pypi.yml - Triggered either manually or for commits to the main branch that contain changes to the docker/service_version.txtfile.
• publish_release.yml - workflow runs automatically when there is a change to the docker/service_version.txt file on the main branch. This workflow will:
  • Run the full unit test suite, to prevent publication of broken code.
  • Extract the semantic version number from docker/service_version.txt.
  • Extract the released notes for the most recent version from CHANGELOG.md.
  • Build and deploy a this service's docker image to ghcr.io.
  • Build the library package to be published to PyPI.
  • Publish the package to PyPI.
  • Publish a GitHub release under the semantic version number, with associated git tag.

Releasing

A release consists of a new version hybig-py library published to PyPI and a new Docker service image published to github's container repository.

A release is made automatically when a commit to the main branch contains a changes in the docker/service_version.txt file, see the publish_release workflow in the CI/CD section above.

Before merging a PR that will trigger a release, ensure these two files are updated:

• CHANGELOG.md - Notes should be added to capture the changes to the service.
• docker/service_version.txt - The semantic version number should be updated.

The CHANGELOG.md file requires a specific format for a new release, as it looks for the following string to define the newest release of the code (starting at the top of the file).

## [vX.Y.Z] - YYYY-MM-DD

Where the markdown reference needs to be updated at the bottom of the file following the existing pattern.

[unreleased]:https://github.com/nasa/harmony-browse-image-generator/compare/X.Y.Z..HEAD
[vX.Y.Z]:https://github.com/nasa/harmony-browse-image-generator/compare/X.Y.Y..X.Y.Z

pre-commit hooks:

This repository uses pre-commit to enable pre-commit checking the repository for some coding standard best practices. These include:

• Removing trailing whitespaces.
• Removing blank lines at the end of a file.
• JSON files have valid formats.
• ruff Python linting checks.
• black Python code formatting checks.

To enable these checks locally:

# Install pre-commit Python package as part of test requirements:
pip install -r tests/pip_test_requirements.txt

# Install the git hook scripts:
pre-commit install

# (Optional) Run against all files:
pre-commit run --all-files

When you try to make a new commit locally, pre-commit will automatically run. If any of the hooks detect non-compliance (e.g., trailing whitespace), that hook will state it failed, and also try to fix the issue. You will need to review and git add the changes before you can make a commit.

It is planned to implement additional hooks, possibly including tools such as mypy.

pre-commit.ci is configured such that these same hooks will be automatically run for every pull request.

Releasing a new version of the service:

Once a new Docker image has been published with a new semantic version tag, that service version can be released to a Harmony environment by following the directions in the Harmony Managing Existing Services Guide.

Get in touch:

You can reach out to the maintainers of this repository via email:

• [e-mail]
• [e-mail]
• [e-mail]