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https://github.com/opendatacube/datacube-explorer

Web-based exploration of Open Data Cube collections
https://github.com/opendatacube/datacube-explorer

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Web-based exploration of Open Data Cube collections

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README 

        
# Data Cube Explorer

[![Linting](https://github.com/opendatacube/datacube-explorer/workflows/Linting/badge.svg)](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3ALinting)

[![Tests](https://github.com/opendatacube/datacube-explorer/workflows/Tests/badge.svg)](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3ATests)

[![Docker](https://github.com/opendatacube/datacube-explorer/workflows/Docker/badge.svg)](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3ADocker)

[![Scan](https://github.com/opendatacube/datacube-explorer/workflows/Scan/badge.svg)](https://github.com/opendatacube/datacube-explorer/actions?query=workflow%3AScan)

[![coverage](https://codecov.io/gh/opendatacube/datacube-explorer/branch/develop/graph/badge.svg)](https://codecov.io/gh/opendatacube/datacube-explorer)

[![Doc](https://readthedocs.org/projects/datacube-explorer/badge/?version=latest)](http://datacube-explorer.readthedocs.org/en/latest/)



![Explorer Screenshot](screenshot.png)



## 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: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`. If you

created your datacube without permissions, a stand-alone creator of the `agdc_user`

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 [query](https://github.com/stac-api-extensions/query), [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 `search` endpoint as used with POST requests, with fields and sort additionally bound to the features endpoint.



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`, and `stac_version` fields will always be included, regardless of user input.



The implementation of `filter` is limited, and currently only supports CQL2 JSON syntax with the following basic CQL2 operators: `AND`, `OR`, `=`, `>`, `>=`, `<`, `<=`, `<>`, `IS NULL`.