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xreds

XArray Environmental Data Services

Running Locally

Installing dependencies

System Dependencies

macOS

brew install netcdf4 h5 geos proj eccodes

Python Dependencies

Then install with uv in a virtualenv:

uv venv
source venv/bin/activate
uv pip install -r requirements.txt

Or install with pip in a virtualenv:

virtualenv -p python3 env/
source env/bin/activate
pip install -r requirements.txt

Running the server

Build the react app

cd viewer/
npm run install
npm run build

Run the following in the activated virtualenv:

DATASETS_MAPPING_FILE=./test.json python app.py

Where DATASETS_MAPPING_FILE is the path to the dataset key value store as described here. You can now navigate to http://localhost:8090/docs to see the supported operations

Running With Docker

Building and Running manually

The docker container for the app can be built with:

docker build -t xreds:latest .

There are aso build arguments available when building the docker image:

  • ROOT_PATH: The root path the app will be served from. Defaults to /xreds/.
  • WORKERS: The number of gunicorn workers to run. Defaults to 1.

Once built, it requires a few things to be run: The 8090 port to be exposed, and a volume for the datasets to live in, and the environment variable pointing to the dateset json file.

docker run -p 8090:8090 -e "DATASETS_MAPPING_FILE=/path/to/datasets.json" -v "/path/to/datasets:/opt/xreds/datasets" xreds:latest

Running with docker compose

There are a few docker compose examples to get started with:

Vanilla

docker compose -d

With Redis

docker compose -f docker-compose-redis.yml up -d

With NGINX Proxy

docker compose -f docker-compose-nginx.yml up -d

Specifying Datasets

Datasets are specified in a key value manner, where the keys are the dataset ids and the values are objects with the path and access control info for the datasets:

{
    "gfswave_global": {
        "path": "s3://nextgen-dmac/kerchunk/gfswave_global_kerchunk.json",
        "type": "kerchunk",
        "chunks": {},
        "drop_variables": ["orderedSequenceData"],
        "target_protocol": "s3",
        "target_options": {
            "anon": false,
            "key": "my aws key"
            "secret": "my aws secret"
        }
    },
    "dbofs": {
        "path": "s3://nextgen-dmac/nos/nos.dbofs.fields.best.nc.zarr",
        "type": "kerchunk",
        "chunks": {
            "ocean_time": 1
        },
        "drop_variables": ["dstart"]

    }
}

Equivalent yaml is also supported:

---

gfswave_global:
  path: s3://nextgen-dmac/kerchunk/gfswave_global_kerchunk.json
  type: kerchunk
  chunks: {}
  drop_variables:
    - orderedSequenceData

Currently zarr, netcdf, and kerchunk dataset types are supported. This information should be saved in a file and specified when running via environment variable DATASETS_MAPPING_FILE.

Dataset Type Schema

kerchunk

{
    "path": "s3://nextgen-dmac/kerchunk/gfswave_global_kerchunk.json",
    "type": "kerchunk",
    "chunks": {},
    "drop_variables": ["orderedSequenceData"],
    "remote_protocol": "s3", // default is s3
    "remote_options": {
        "anon": true, // default is True
    },
    "target_protocol": "s3", // defualt is s3
    "target_options": {
        "anon": false, // default is True
    },
    "extensions": { // optional
      "vdatum": {
        "path": "s3://nextgen-dmac-cloud-ingest/nos/vdatums/ngofs2_vdatums.nc.zarr", // fsspec path to vdatum dataset
        "water_level_var": "zeta", // variable to use for water level
        "vdatum_var": "mllwtomsl", // variable mapping to vdatum transformation
        "vdatum_name": "mllw" // name of the vdatum transformation
      }
    }
}

netcdf

{
    "path": "http://www.smast.umassd.edu:8080/thredds/dodsC/models/fvcom/NECOFS/Forecasts/NECOFS_GOM7_FORECAST.nc",
    "type": "netcdf",
    "engine": "netCDF4", // default is netCDF4
    "chunks": {},
    "drop_variables": ["Itime", "Itime2"],
    "additional_coords": ["lat", "lon", "latc", "lonc", "xc", "yc"],
    "extensions": { // optional
        ... // Same as kerchunk options
    }
}

Configuration Options

The following environment variables can be set to configure the app:

  • DATASETS_MAPPING_FILE: The fsspec compatible path to the dataset key value store as described here
  • PORT: The port the app should run on. Defaults to 8090.
  • WORKERS: The number of worker threads handling requests. Defaults to 1
  • ROOT_PATH: The root path the app will be served from. Defaults to be served from the root.
  • DATASET_CACHE_TIMEOUT: The time in seconds to cache the dataset metadata. Defaults to 600 (10 minutes).
  • EXPORT_THRESHOLD: The maximum size file to allow to be exported. Defaults to 500 mb
  • USE_REDIS_CACHE: Whether to use a redis cache for the app. Defaults to False
  • REDIS_HOST: [Optional] The host of the redis cache. Defaults to localhost
  • REDIS_PORT: [Optional] The port of the redis cache. Defaults to 6379

Building and Deploying Public Docker Image

First follow instructions above to build the docker image tagged xreds:latest. Then thexreds:latest image needs to be tagged and deployed to the relevant docker registry.

# Auth with ECR
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/m2c5k9c1

# Tag the image
docker tag xreds:latest public.ecr.aws/m2c5k9c1/nextgen-dmac/xreds:latest

# Push the image
docker push public.ecr.aws/m2c5k9c1/nextgen-dmac/xreds:latest