hakai-ctd-qc

hakai-ctd-qc is the main package used to handle the QCing of the CTD Datasets maintained by the Hakai Institute. Please refer to the test description manual for a full description of the different tests applied within this package. Examples of tes

Installation

The present package can be installed locally or through a docker container. In all cases, it is best to clone locally the package and apply the appropriate configuration.

git clone [email protected]:HakaiInstitute/hakai-ctd-qc.git

Locally for development only

Clone locally the repository and create the conda environment:

pyenv install 3.11.2
pyenv local 3.11.2
pip install poetry
poetry install

Copy the sample.env file as .env and replace the different values accordingly.

Methods

Command Line Interface

Once installed the package hakai_profile_qc can be run via the command line. See the help menu for a complete description of the different options:

python hakai_profile_qc --help

Usage: hakai_ctd_qc [OPTIONS]

Options:
  --hakai_ids TEXT            Comma delimited list of hakai_ids to qc
  --processing-stages TEXT    Comma list of processing_stage profiles to
                              review [env=QC_PROCESSING_STAGES]  [default:
                              8_binAvg,8_rbr_processed]
  --test-suite                Run Test suite [env=RUN_TEST_SUITE]
  --api-root TEXT             Hakai API root to use [env=HAKAI_API_ROOT]
                              [default: https://goose.hakai.org/api]
  --upload-flag               Update database flags
                              [env=UPDATE_SERVER_DATABASE]
  --chunksize INTEGER         Process profiles by chunk
                              [env=CTD_CAST_CHUNKSIZE]  [default: 100]
  --sentry-minimum-date TEXT  Minimum date to use to generate sentry warnings
                              [env=SENTRY_MINIMUM_DATE]
  --profile PATH              Run cProfile
  --help                      Show this message and exit.

API

Run the following command:

poetry run python hakai_ctd_qc/api.py

And within a browser to go: http://127.0.0.1:8000

With vscode you can also run the debug configuration Run API which helps debug the interface in realtime.

Important

To protect the api from unpexted calls, you can set a list of accepted tokens as a list of comma separated list. Any post calls to the api will then require a field token within the header of the post command and an accepted value.

Deployments

The hakai_ctd_qc tool is deployed via a Docker container (see Dockerfile) on two caprover instances: related to the development and production branches.

• development: http://hakai-ctd-qc.server.hak4i.org/ -> qc hakaidev database
• main: http://hakai-ctd-qc.server.hakai.app/ -> qc hakai database
  • a cron job is applied to this instance to qc latest data submitted.

Each been associated to their respective hakai database:

Continuous Integration

1. Testing: Any changes to the package are tested via a GitHub workflow that qc hakai_id test suite.
2. Docker Build Testing: Docker container build is tested via a GitHub worflow
3. Changes to the main and development versions are directely deployed to the different caprover instances via the deploy action each respective github environments.
4. Errors and monitoring: Sentry is use to monitor the different errors and cron jobs. Only the main deployment is required to run a cron job to make sure any newly submitted data is qced. See the following links for any issues and cron issues encountered.

Tests parametrization

The different tests applied are defined within the respective configurations:

• Hakai tests
• QARTOD Tests

A subset of hakai_ids is used to test the qc tool and is maintained here

Manual flags can also be implemented on any instrument-specific variables via the grey-list, which overwrites any automatically generated flags.

Testing

To make sure the tests are working appropriately a a series of pytests are available. Some of the tests are specific to the hakai tests, others to the hakai test suite.

The test suite is made available locally via the parquet file, or retrieved from the development or production database.

To run all the tests locally:

poetry run pytest .

To run all the tests with the production data (hecate) or development data (goose). Use the --test-suite-from option. Here's an example for goose:

poetry run pytest . --test-suite-from goose

Once to test the results on any of the databases without rerunning the tests on the data, you can use the --test-suite-qc False option.

poetry run pytest . --test-suite-form goose -k test_source_expected_results