Skip to main content

Webviz config plugins for subsurface data

Project description

PyPI version Build Status Total alerts Language grade: Python Python 3.8 | 3.9 | 3.10 Code style: black

Webviz subsurface

:sparkles::eyeglasses: Live demo application

Introduction

This repository contains subsurface specific standard webviz containers, which are used as plugins in webviz-config.

Installation

The easiest way of installing this package is to run

pip install webviz-subsurface

Add --upgrade if you have installed earlier, but want to upgrade to a newer version.

If you want to install the latest, unreleased, code you can instead run

pip install git+https://github.com/equinor/webviz-subsurface

Usage and documentation

For general usage, see the documentation on webviz-config. End-user documentation for the subsurface containers are automatically built and hosted on the github pages for this repository.

There is also a live demo application showing how a created application can look like, using the master branch of this repository.

Example webviz configuration files

Example webviz configuration files, and corresponding test data, is available at https://github.com/equinor/webviz-subsurface-testdata.

See that repository for instructions on how to download and run the examples.

Creating new elements

If you are interested in creating new elements which can be configured through the configuration file, take a look at the webviz-config contribution guide.

You can do automatic linting of your code changes by running

black --check webviz_subsurface tests # Check code style
pylint webviz_subsurface tests # Check code quality
bandit -r -c ./bandit.yml webviz_subsurface tests  # Check Python security best practice

Review of contributions

When doing review of contributions, it is usually useful to also see the resulting application live, and not only the code changes. In order to facilitate this, this repository is using GitHub actions.

When on a feature branch, and a commit message including the substring [deploy test] arrives, the GitHub action workflow will try to build and deploy a test Docker image for you (which you then can link to a web app with e.g. automatic reload on new images). All you need to do in your own fork is to add GitHub secrets with the following names:

  • review_docker_registry_url: The registry to push to (e.g. myregistry.azurecr.io)
  • review_docker_registry_username: Registry login username.
  • review_docker_registry_token: Registry login token (or password).
  • review_container_name: What you want to call the container pushed to the registry.

You are encouraged to rebase and squash/fixup unnecessary commits before pull request is merged to master.

Disclaimer

This is a tool under heavy development. The current configuration file layout, also for subsurface containers, will therefore see large changes.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

webviz-subsurface-0.2.17.tar.gz (788.3 kB view details)

Uploaded Source

Built Distribution

webviz_subsurface-0.2.17-py3-none-any.whl (971.9 kB view details)

Uploaded Python 3

File details

Details for the file webviz-subsurface-0.2.17.tar.gz.

File metadata

  • Download URL: webviz-subsurface-0.2.17.tar.gz
  • Upload date:
  • Size: 788.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.15

File hashes

Hashes for webviz-subsurface-0.2.17.tar.gz
Algorithm Hash digest
SHA256 235b79779d3c6d5f7c7f9535c424dcc77999561d7a488f7dbfeba208564aa11c
MD5 1f55865339cde1ba5db812e33a312550
BLAKE2b-256 7c520d886fe4799f970d579090427320e1a4c75ba4f73e3f7baa4cc436a946d6

See more details on using hashes here.

File details

Details for the file webviz_subsurface-0.2.17-py3-none-any.whl.

File metadata

File hashes

Hashes for webviz_subsurface-0.2.17-py3-none-any.whl
Algorithm Hash digest
SHA256 2768b0369b8e01d140f96516f6929a6c78963a97fcadcbea98b83520187093b0
MD5 780077f97d4cff0ada91babcbe38f8f0
BLAKE2b-256 173d2ddc4ca5060b9121ba226e632c34ec2f5297e4f264d25ce7363279d600ca

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page