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Add notebook showing how to use hawkins & sutton algo to partition uncertainties #181

Add notebook showing how to use hawkins & sutton algo to partition uncertainties

Add notebook showing how to use hawkins & sutton algo to partition uncertainties #181

name: First Pull Request
on:
pull_request_target:
types:
- opened
jobs:
welcome:
name: Welcome
runs-on: ubuntu-latest
steps:
- uses: actions/[email protected]
with:
script: |
// Get a list of all issues created by the PR opener
// See: https://octokit.github.io/rest.js/#pagination
const creator = context.payload.sender.login
const opts = github.rest.issues.listForRepo.endpoint.merge({
...context.issue,
creator,
state: 'all'
})
const issues = await github.paginate(opts)
for (const issue of issues) {
if (issue.number === context.issue.number) {
continue
}
if (issue.pull_request) {
return // Creator is already a contributor.
}
}
await github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: `**Welcome**, new contributor!
It appears that this is your first Pull Request. To give credit where it's due, we ask that you add your information to the \`AUTHORS.rst\` and \`.zenodo.json\`:
- [ ] The relevant author information has been added to \`AUTHORS.rst\` and \`.zenodo.json\`
Please make sure you've read our [contributing guide](CONTRIBUTING.rst). We look forward to reviewing your Pull Request shortly ✨`
})