https://github.com/opendatacube/datacube-explorer
Web-based exploration of Open Data Cube collections
https://github.com/opendatacube/datacube-explorer
flask gis hacktoberfest opendatacube
Last synced: 3 months ago
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Web-based exploration of Open Data Cube collections
- Host: GitHub
- URL: https://github.com/opendatacube/datacube-explorer
- Owner: opendatacube
- License: apache-2.0
- Created: 2016-07-29T00:16:06.000Z (about 9 years ago)
- Default Branch: develop
- Last Pushed: 2025-07-03T00:01:10.000Z (3 months ago)
- Last Synced: 2025-07-03T00:29:47.946Z (3 months ago)
- Topics: flask, gis, hacktoberfest, opendatacube
- Language: JavaScript
- Homepage:
- Size: 36.4 MB
- Stars: 58
- Watchers: 12
- Forks: 33
- Open Issues: 39
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: code-of-conduct.md
Awesome Lists containing this project
README
# Data Cube Explorer
[](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3ALinting)
[](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3ATests)
[](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3ADocker)
[](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3AScan)
[](https://codecov.io/gh/opendatacube/datacube-explorer)
[](http://datacube-explorer.readthedocs.org/en/latest/)
## Usage (quick-start)
Assuming you already have an Open Data Cube instance, Explorer will use
its existing settings.
Install Explorer:
pip install datacube-explorer
Generate summaries for all of your products:
cubedash-gen --init --all
Run Explorer locally:
cubedash-run
It will now be viewable on [http://localhost:8090](https://localhost:8090)
## Developer Setup
These directions are for running from a local folder in development. But it will run from any typical Python WSGI server.
Firstly, install the Open Data Cube. Use of a [Data Cube conda environment](https://datacube-core.readthedocs.io/en/latest/installation/setup/common_install.html)
is recommended. You may need to also `conda install -c conda-forge postgis`
Test that you can run `datacube system check`, and that it's connecting
to the correct datacube instance.
### Dependencies
Now install the explorer dependencies:
# These two should come from conda if you're using it, not pypi
conda install fiona shapely
pip install -e .
### Summary generation
Initialise and create product summaries:
cubedash-gen --init --all
(This can take a long time the first time, depending on your datacube size.)
Other available options can be seen by running `cubedash-gen --help`.
### Run
A `cubedash-run` command is available to run Explorer locally:
$ cubedash-run
* Running on http://localhost:8080/ (Press CTRL+C to quit)
(see `cubedash-run --help` for list of options)
But Explorer can be run using any typical Python WSGI server, for example [gunicorn](https://gunicorn.org/):
pip install gunicorn
gunicorn -b '127.0.0.1:8080' -w 4 cubedash:create_app()
Products will begin appearing one-by-one as the summaries are generated in the
background. If impatient, you can manually navigate to a product using
`/` and other tags at the bottom of every page.
cubedash/templates/include-global.env.html
- Footer text include. For human text such as Copyright statements.
echo "Server staging-1.test" > cubedash/templates/include-footer.env.html
(`*.env.html` is the naming convention used for environment-specific templates: they are ignored by
Git)
### How can I configure the deployment?
refer to [deployment instructions](https://datacube-explorer.readthedocs.io/en/latest/deploying.html) and [app-config](https://datacube-explorer.readthedocs.io/en/latest/config.html)
### How do I modify the CSS/Javascript?
The CSS is compiled from [Sass](https://sass-lang.com/), and the Javascript is compiled from
[Typescript](https://www.typescriptlang.org/).
Install [npm](https://www.npmjs.com/get-npm), and then install them both:
npm install -g sass typescript
You can now run `make static` to rebuild all the static files, or
individually with `make style` or `make js`.
Alternatively, if using [PyCharm](https://www.jetbrains.com/pycharm), open a
Sass file and you will be prompted to enable a `File Watcher` to
compile automatically.
PyCharm will also compile the Typescript automatically by ticking
the "Recompile on changes" option in `Languages & Frameworks ->
Typescript`.
### How do I run the integration tests?
The integration tests run against a real PostgreSQL database, which is
automatically started and stopped using Docker. This requires Docker to
be available, but no further database setup is required.
Install the test dependencies: `pip install -e .[test]`
The run the tests with: `pytest integration_tests`
### How do I add test data for the automated tests?
Most of the automated tests for Datacube Explorer require sample data to run. This comprises
definitions of ODC *Metadata Types*, *Products* and *Datasets*.
These are contained within YAML files in the [`integration_tests/data`](https://github.com/opendatacube/datacube-explorer/tree/develop/integration_tests/data) directory.
Test data is loaded using a pytest fixture called `auto_odc_db`, which is activated per
test module, and will automatically populate the database using files referenced in module
global variables. Activate and use it similar to the following example:
pytestmark = pytest.mark.usefixtures("auto_odc_db")
METADATA_TYPES = ["metadata/qga_eo.yaml"]
PRODUCTS = ["products/ga_s2_ard.odc-product.yaml"]
DATASETS = ["s2a_ard_granule.yaml.gz"]
To add sample datasets required for the test case, create a `.yaml` file
with the product name and place all the sample datasets split by `---` in the yaml.
If the sample datasets file is large, compress it with `gzip .yaml` and reference
that file instead.
## Roles for production deployments
The [roles](cubedash/summary/roles) directory contains sql files for creating
Postgres roles for Explorer. These are suitable for running each Explorer
task with minimum needed security permissions.
Three roles are created:
- **explorer-viewer**: A read-only user of datacube and Explorer. Suitable for the web interface and cli (`cubedash-view`) commands.
- **explorer-generator**: Suitable for generating and updating summaries (ie. Running `cubedash-gen`)
- **explorer-owner**: For creating and updating the schema. (ie. Running `cubedash-gen --init`)
Note that these roles extend the built-in datacube role `agdc_user` (using postgres) or `odc_user` (using postgis).
If you created your datacube without permissions, a stand-alone creator of the appropriate
role is available as a prerequisite in the same [roles](cubedash/summary/roles)
directory.
## Docker for Development and running tests
You need to have Docker and Docker Compose installed on your system.
To create your environment, run `make up` or `docker-compose up`.
You need an ODC database, so you'll need to refer to the [ODC docs](https://datacube-core.readthedocs.io/en/latest/) for help on indexing, but you can create the database by running `make initdb` or `docker-compose exec explorer datacube system init`. (This is not enough, you still need to add a product and index datasets.)
When you have some ODC data indexed, you can run `make index` to create the Explorer indexes.
Once Explorer indexes have been created, you can browse the running application at [http://localhost:5000](http://localhost:5000).
You can run tests by first creating a test database `make create-test-db-docker` and then running tests with `make test-docker`.
And you can run a single test in Docker using a command like this: `docker-compose --file docker-compose.yml run explorer pytest integration_tests/test_dataset_listing.py`
## Docker-compose for Development and running tests
### Testing with app.config
edit `.docker/settings_docker.py` and setup application config. Then `docker-compose -f docker-compose.yml -f docker-compose.override.yml up` to bring up explorer docker with database, explorer with settings
## STAC API Extensions
The STAC endpoint implements the [filter](https://github.com/stac-api-extensions/filter), [fields](https://github.com/stac-api-extensions/fields), and [sort](https://github.com/stac-api-extensions/sort) extensions, all of which are bound to the STAC API - Item Search (`/search`) endpoint. All support both GET and POST request syntax.
Fields contained in the item properties must be prefixed with `properties.`, ex `properties.dea:dataset_maturity`.
The implementation of `fields` differs somewhat from the suggested include/exclude semantics in that it does not permit for invalid STAC entities, so the `id`, `type`, `geometry`, `bbox`, `links`, `assets`, `properties.datetime`, `collection`, and `stac_version` fields will always be included, regardless of user input.
The `sort` and `filter` implementations will recognise any syntactically valid version of a property name, which is the say, the STAC, eo3, and search field (as defined by the metadata type) variants of the name, with or without the `item.` or `properties.` prefixes. If a property does not exist for an item, `sort` will ignore it while `filter` will treat it as `NULL`.
The `filter` extension supports both `cql2-text` and `cql2-json` for both GET and POST requesets, and uses [pygeofilter](https://github.com/geopython/pygeofilter) to parse the cql and convert it to a sqlalchemy filter expression. `filter-crs` only accepts http://www.opengis.net/def/crs/OGC/1.3/CRS84 as a valid value.