diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml deleted file mode 100644 index 136b0615..00000000 --- a/.github/workflows/ci.yml +++ /dev/null @@ -1,89 +0,0 @@ -name: continuous-integration - -on: [push, pull_request] - -jobs: - - pre-commit: - - runs-on: ubuntu-latest - timeout-minutes: 30 - - steps: - - uses: actions/checkout@v2 - - name: Set up Python 3.7 - uses: actions/setup-python@v1 - with: - python-version: 3.7 - - name: Install python dependencies - run: | - pip install --upgrade pip - pip install -r requirements.txt - - name: Run pre-commit - run: - pre-commit run --all-files || ( git status --short ; git diff ; exit 1 ) - - build: - - runs-on: ubuntu-latest - timeout-minutes: 30 - - strategy: - matrix: - format: ["html", "latexpdf", "transifex"] - fail-fast: false - - steps: - - uses: actions/checkout@v2 - - name: Install latexpdf dependencies - if: ${{ matrix.format == 'latexpdf' }} - run: | - sudo apt update - sudo apt install \ - texlive-base \ - texlive-plain-generic \ - texlive-fonts-recommended \ - fonts-freefont-otf \ - texlive-latex-base \ - texlive-latex-recommended \ - texlive-latex-extra \ - texlive-xetex \ - latexmk \ - dvipng \ - dvidvi - - name: Set up Python 3.7 - uses: actions/setup-python@v1 - with: - python-version: 3.7 - - name: Install python dependencies - run: | - pip install --upgrade pip - pip install -r requirements.txt - - name: "build docs: html" - if: ${{ matrix.format == 'html' }} - run: | - make -C docs ${{ matrix.format }} - env: - SPHINXOPTS: -nW --keep-going - - name: "build docs: latexpdf" - if: ${{ matrix.format == 'latexpdf' }} - run: | - make -C docs ${{ matrix.format }} - # TODO there are reference issues for latexpdf in e.g. docs/pages/2019_molsim_school_Amsterdam/screening/export.md - # I believe due to relative document references which don't work in latexpdf, since all documents are merged into one doctree - env: - SPHINXOPTS: -n - - name: "build docs: transifex" - if: ${{ matrix.format == 'transifex' }} - run: | - sphinx-build -b gettext -D gettext_compact=0 docs locale - tx init --no-interactive - sphinx-intl update-txconfig-resources --pot-dir locale --transifex-project-name ${TRANSIFEX_PROJECT_NAME} - env: - SPHINXOPTS: -nW --keep-going - TRANSIFEX_PROJECT_NAME: aiida-tutorials - - name: "publish transifex" - if: ${{ matrix.format == 'transifex' && github.ref == 'master' }} - run: | - sudo echo $'[https://www.transifex.com]\nhostname = https://www.transifex.com\nusername = '"${{ secrets.TRANSIFEX_USER }}"$'\npassword = '"${{ secrets.TRANSIFEX_PASSWORD }}"$'\n' > ~/.transifexrc - tx push -s diff --git a/.mdlrc b/.mdlrc deleted file mode 100644 index 30aa58a5..00000000 --- a/.mdlrc +++ /dev/null @@ -1 +0,0 @@ -rules "~MD013,~MD029,~MD014" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a72ae980..ec712b5a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,34 +1,22 @@ exclude: > (?x)^( - docs/pages/.*/snippets/.*| - docs/pages/2019_molsim_school_Amsterdam/assets/raspa_loading.py| - docs/pages/2019_molsim_school_Amsterdam/assets/multiply_unitcell.py| - docs/assets/2018_PRACE_MaX/latex/de-macro.py| .*xsf| .*agr| .*tex )$ repos: - repo: git://github.com/pre-commit/pre-commit-hooks - rev: v2.2.3 + rev: v4.0.0 hooks: - id: check-yaml - id: end-of-file-fixer - id: trailing-whitespace - - repo: https://github.com/pre-commit/mirrors-yapf - rev: v0.27.0 - hooks: - - id: yapf - name: yapf - types: [python] - args: ['-i'] - - - repo: https://github.com/PyCQA/pylint - rev: pylint-2.4.4 - hooks: - - id: pylint - language: system + - repo: https://github.com/ambv/black + rev: stable + hooks: + - id: black + language_version: python3.8 - repo: https://github.com/myint/rstcheck rev: 'master' @@ -36,14 +24,3 @@ repos: - id: rstcheck additional_dependencies: - sphinx==2.4.4 - - - repo: https://github.com/markdownlint/markdownlint - rev: 'v0.9.0' - hooks: - - id: markdownlint - # currently mistaking comment lines in code cells for headers!? - exclude: > - (?x)^( - docs/pages/2020_Intro_Week/notebooks/querybuilder-template.md| - docs/pages/2020_Intro_Week/notebooks/bandstructure.md - )$ diff --git a/.rstcheck.cfg b/.rstcheck.cfg index b2ff32ac..8c218cbc 100644 --- a/.rstcheck.cfg +++ b/.rstcheck.cfg @@ -1,4 +1,4 @@ [rstcheck] -ignore_directives=panels,dropdown +ignore_directives=panels,dropdown,rst-class ignore_roles=doi,nb-download ignore_messages=(.*is not referenced.) diff --git a/docs/_static/css/custom.css b/docs/_static/aiida-tutor-custom.css similarity index 55% rename from docs/_static/css/custom.css rename to docs/_static/aiida-tutor-custom.css index 8c3fe88a..8502e9fd 100644 --- a/docs/_static/css/custom.css +++ b/docs/_static/aiida-tutor-custom.css @@ -5,3 +5,15 @@ div.highlight-console div.highlight { div.highlight-bash div.highlight { background-color: aliceblue; } + +.header-text { + font-size: 18px +} + +.text-right { + text-align: right; +} + +.footer-low { + line-height: 0.5; +} diff --git a/docs/_static/logo.png b/docs/_static/logo.png new file mode 100644 index 00000000..94dc2f52 Binary files /dev/null and b/docs/_static/logo.png differ diff --git a/docs/_static/panels-bootstrap.css b/docs/_static/panels-bootstrap.css new file mode 100644 index 00000000..b30dc192 --- /dev/null +++ b/docs/_static/panels-bootstrap.css @@ -0,0 +1,1836 @@ +.badge { + border-radius: .25rem; + display: inline-block; + font-size: 75%; + font-weight: 700; + line-height: 1; + padding: .25em .4em; + text-align: center; + vertical-align: baseline; + white-space: nowrap +} + +.badge:empty { + display: none +} + +.btn .badge { + position: relative; + top: -1px +} + +.badge-pill { + border-radius: 10rem; + padding-left: .6em; + padding-right: .6em +} + +.badge-primary { + background-color: #007bff; + color: #fff +} + +.badge-primary[href]:focus, .badge-primary[href]:hover { + background-color: #0062cc; + color: #fff; + text-decoration: none +} + +.badge-secondary { + background-color: #6c757d; + color: #fff +} + +.badge-secondary[href]:focus, .badge-secondary[href]:hover { + background-color: #545b62; + color: #fff; + text-decoration: none +} + +.badge-success { + background-color: #28a745; + color: #fff +} + +.badge-success[href]:focus, .badge-success[href]:hover { + background-color: #1e7e34; + color: #fff; + text-decoration: none +} + +.badge-info { + background-color: #17a2b8; + color: #fff +} + +.badge-info[href]:focus, .badge-info[href]:hover { + background-color: #117a8b; + color: #fff; + text-decoration: none +} + +.badge-warning { + background-color: #ffc107; + color: #212529 +} + +.badge-warning[href]:focus, .badge-warning[href]:hover { + background-color: #d39e00; + color: #212529; + text-decoration: none +} + +.badge-danger { + background-color: #dc3545; + color: #fff +} + +.badge-danger[href]:focus, .badge-danger[href]:hover { + background-color: #bd2130; + color: #fff; + text-decoration: none +} + +.badge-light { + background-color: #f8f9fa; + color: #212529 +} + +.badge-light[href]:focus, .badge-light[href]:hover { + background-color: #dae0e5; + color: #212529; + text-decoration: none +} + +.badge-dark { + background-color: #343a40; + color: #fff +} + +.badge-dark[href]:focus, .badge-dark[href]:hover { + background-color: #1d2124; + color: #fff; + text-decoration: none +} + +.border-0 { + border: 0 !important +} + +.border-top-0 { + border-top: 0 !important +} + +.border-right-0 { + border-right: 0 !important +} + +.border-bottom-0 { + border-bottom: 0 !important +} + +.border-left-0 { + border-left: 0 !important +} + +.p-0 { + padding: 0 !important +} + +.pt-0, .py-0 { + padding-top: 0 !important +} + +.pr-0, .px-0 { + padding-right: 0 !important +} + +.pb-0, .py-0 { + padding-bottom: 0 !important +} + +.pl-0, .px-0 { + padding-left: 0 !important +} + +.p-1 { + padding: .25rem !important +} + +.pt-1, .py-1 { + padding-top: .25rem !important +} + +.pr-1, .px-1 { + padding-right: .25rem !important +} + +.pb-1, .py-1 { + padding-bottom: .25rem !important +} + +.pl-1, .px-1 { + padding-left: .25rem !important +} + +.p-2 { + padding: .5rem !important +} + +.pt-2, .py-2 { + padding-top: .5rem !important +} + +.pr-2, .px-2 { + padding-right: .5rem !important +} + +.pb-2, .py-2 { + padding-bottom: .5rem !important +} + +.pl-2, .px-2 { + padding-left: .5rem !important +} + +.p-3 { + padding: 1rem !important +} + +.pt-3, .py-3 { + padding-top: 1rem !important +} + +.pr-3, .px-3 { + padding-right: 1rem !important +} + +.pb-3, .py-3 { + padding-bottom: 1rem !important +} + +.pl-3, .px-3 { + padding-left: 1rem !important +} + +.p-4 { + padding: 1.5rem !important +} + +.pt-4, .py-4 { + padding-top: 1.5rem !important +} + +.pr-4, .px-4 { + padding-right: 1.5rem !important +} + +.pb-4, .py-4 { + padding-bottom: 1.5rem !important +} + +.pl-4, .px-4 { + padding-left: 1.5rem !important +} + +.p-5 { + padding: 3rem !important +} + +.pt-5, .py-5 { + padding-top: 3rem !important +} + +.pr-5, .px-5 { + padding-right: 3rem !important +} + +.pb-5, .py-5 { + padding-bottom: 3rem !important +} + +.pl-5, .px-5 { + padding-left: 3rem !important +} + +.m-0 { + margin: 0 !important +} + +.mt-0, .my-0 { + margin-top: 0 !important +} + +.mr-0, .mx-0 { + margin-right: 0 !important +} + +.mb-0, .my-0 { + margin-bottom: 0 !important +} + +.ml-0, .mx-0 { + margin-left: 0 !important +} + +.m-1 { + margin: .25rem !important +} + +.mt-1, .my-1 { + margin-top: .25rem !important +} + +.mr-1, .mx-1 { + margin-right: .25rem !important +} + +.mb-1, .my-1 { + margin-bottom: .25rem !important +} + +.ml-1, .mx-1 { + margin-left: .25rem !important +} + +.m-2 { + margin: .5rem !important +} + +.mt-2, .my-2 { + margin-top: .5rem !important +} + +.mr-2, .mx-2 { + margin-right: .5rem !important +} + +.mb-2, .my-2 { + margin-bottom: .5rem !important +} + +.ml-2, .mx-2 { + margin-left: .5rem !important +} + +.m-3 { + margin: 1rem !important +} + +.mt-3, .my-3 { + margin-top: 1rem !important +} + +.mr-3, .mx-3 { + margin-right: 1rem !important +} + +.mb-3, .my-3 { + margin-bottom: 1rem !important +} + +.ml-3, .mx-3 { + margin-left: 1rem !important +} + +.m-4 { + margin: 1.5rem !important +} + +.mt-4, .my-4 { + margin-top: 1.5rem !important +} + +.mr-4, .mx-4 { + margin-right: 1.5rem !important +} + +.mb-4, .my-4 { + margin-bottom: 1.5rem !important +} + +.ml-4, .mx-4 { + margin-left: 1.5rem !important +} + +.m-5 { + margin: 3rem !important +} + +.mt-5, .my-5 { + margin-top: 3rem !important +} + +.mr-5, .mx-5 { + margin-right: 3rem !important +} + +.mb-5, .my-5 { + margin-bottom: 3rem !important +} + +.ml-5, .mx-5 { + margin-left: 3rem !important +} + +.btn { + background-color: transparent; + border: 1px solid transparent; + border-radius: .25rem; + color: #212529; + cursor: pointer; + display: inline-block; + font-size: 1rem; + font-weight: 400; + line-height: 1.5; + padding: .375rem .75rem; + text-align: center; + transition: color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out; + -moz-user-select: none; + -ms-user-select: none; + -webkit-user-select: none; + user-select: none; + vertical-align: middle +} + +.btn:hover { + color: #212529; + text-decoration: none +} + +.btn:visited { + color: #212529 +} + +.btn.focus, .btn:focus { + box-shadow: 0 0 0 .2rem rgba(0, 123, 255, 0.25); + outline: 0 +} + +.btn.disabled, .btn:disabled { + opacity: .65 +} + +@media (prefers-reduced-motion: reduce) { + .btn { + transition: none + } +} + +a.btn.disabled, fieldset:disabled a.btn { + pointer-events: none +} + +.btn-primary { + background-color: #007bff; + border-color: #007bff; + color: #fff +} + +.btn-primary:visited { + color: #fff +} + +.btn-primary:hover { + background-color: #0069d9; + border-color: #0062cc; + color: #fff +} + +.btn-primary.focus, .btn-primary:focus { + background-color: #0069d9; + border-color: #0062cc; + box-shadow: 0 0 0 .2rem rgba(0, 123, 255, 0.5); + color: #fff +} + +.btn-primary.disabled, .btn-primary:disabled { + background-color: #007bff; + border-color: #007bff; + color: #fff +} + +.btn-primary.active:not(:disabled):not(.disabled), .btn-primary:not(:disabled):not(.disabled):active, .show>.btn-primary.dropdown-toggle { + background-color: #0062cc; + border-color: #005cbf; + color: #fff +} + +.btn-primary.active:not(:disabled):not(.disabled):focus, .btn-primary:not(:disabled):not(.disabled):active:focus, .show>.btn-primary.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(0, 123, 255, 0.5) +} + +.btn-secondary { + background-color: #6c757d; + border-color: #6c757d; + color: #fff +} + +.btn-secondary:visited { + color: #fff +} + +.btn-secondary:hover { + background-color: #5a6268; + border-color: #545b62; + color: #fff +} + +.btn-secondary.focus, .btn-secondary:focus { + background-color: #5a6268; + border-color: #545b62; + box-shadow: 0 0 0 .2rem rgba(108, 117, 125, 0.5); + color: #fff +} + +.btn-secondary.disabled, .btn-secondary:disabled { + background-color: #6c757d; + border-color: #6c757d; + color: #fff +} + +.btn-secondary.active:not(:disabled):not(.disabled), .btn-secondary:not(:disabled):not(.disabled):active, .show>.btn-secondary.dropdown-toggle { + background-color: #545b62; + border-color: #4e555b; + color: #fff +} + +.btn-secondary.active:not(:disabled):not(.disabled):focus, .btn-secondary:not(:disabled):not(.disabled):active:focus, .show>.btn-secondary.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(108, 117, 125, 0.5) +} + +.btn-success { + background-color: #28a745; + border-color: #28a745; + color: #fff +} + +.btn-success:visited { + color: #fff +} + +.btn-success:hover { + background-color: #218838; + border-color: #1e7e34; + color: #fff +} + +.btn-success.focus, .btn-success:focus { + background-color: #218838; + border-color: #1e7e34; + box-shadow: 0 0 0 .2rem rgba(40, 167, 69, 0.5); + color: #fff +} + +.btn-success.disabled, .btn-success:disabled { + background-color: #28a745; + border-color: #28a745; + color: #fff +} + +.btn-success.active:not(:disabled):not(.disabled), .btn-success:not(:disabled):not(.disabled):active, .show>.btn-success.dropdown-toggle { + background-color: #1e7e34; + border-color: #1c7430; + color: #fff +} + +.btn-success.active:not(:disabled):not(.disabled):focus, .btn-success:not(:disabled):not(.disabled):active:focus, .show>.btn-success.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(40, 167, 69, 0.5) +} + +.btn-info { + background-color: #17a2b8; + border-color: #17a2b8; + color: #fff +} + +.btn-info:visited { + color: #fff +} + +.btn-info:hover { + background-color: #138496; + border-color: #117a8b; + color: #fff +} + +.btn-info.focus, .btn-info:focus { + background-color: #138496; + border-color: #117a8b; + box-shadow: 0 0 0 .2rem rgba(23, 162, 184, 0.5); + color: #fff +} + +.btn-info.disabled, .btn-info:disabled { + background-color: #17a2b8; + border-color: #17a2b8; + color: #fff +} + +.btn-info.active:not(:disabled):not(.disabled), .btn-info:not(:disabled):not(.disabled):active, .show>.btn-info.dropdown-toggle { + background-color: #117a8b; + border-color: #10707f; + color: #fff +} + +.btn-info.active:not(:disabled):not(.disabled):focus, .btn-info:not(:disabled):not(.disabled):active:focus, .show>.btn-info.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(23, 162, 184, 0.5) +} + +.btn-warning { + background-color: #ffc107; + border-color: #ffc107; + color: #212529 +} + +.btn-warning:visited { + color: #212529 +} + +.btn-warning:hover { + background-color: #e0a800; + border-color: #d39e00; + color: #212529 +} + +.btn-warning.focus, .btn-warning:focus { + background-color: #e0a800; + border-color: #d39e00; + box-shadow: 0 0 0 .2rem rgba(255, 193, 7, 0.5); + color: #212529 +} + +.btn-warning.disabled, .btn-warning:disabled { + background-color: #ffc107; + border-color: #ffc107; + color: #212529 +} + +.btn-warning.active:not(:disabled):not(.disabled), .btn-warning:not(:disabled):not(.disabled):active, .show>.btn-warning.dropdown-toggle { + background-color: #d39e00; + border-color: #c69500; + color: #212529 +} + +.btn-warning.active:not(:disabled):not(.disabled):focus, .btn-warning:not(:disabled):not(.disabled):active:focus, .show>.btn-warning.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(255, 193, 7, 0.5) +} + +.btn-danger { + background-color: #dc3545; + border-color: #dc3545; + color: #fff +} + +.btn-danger:visited { + color: #fff +} + +.btn-danger:hover { + background-color: #c82333; + border-color: #bd2130; + color: #fff +} + +.btn-danger.focus, .btn-danger:focus { + background-color: #c82333; + border-color: #bd2130; + box-shadow: 0 0 0 .2rem rgba(220, 53, 69, 0.5); + color: #fff +} + +.btn-danger.disabled, .btn-danger:disabled { + background-color: #dc3545; + border-color: #dc3545; + color: #fff +} + +.btn-danger.active:not(:disabled):not(.disabled), .btn-danger:not(:disabled):not(.disabled):active, .show>.btn-danger.dropdown-toggle { + background-color: #bd2130; + border-color: #b21f2d; + color: #fff +} + +.btn-danger.active:not(:disabled):not(.disabled):focus, .btn-danger:not(:disabled):not(.disabled):active:focus, .show>.btn-danger.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(220, 53, 69, 0.5) +} + +.btn-light { + background-color: #f8f9fa; + border-color: #f8f9fa; + color: #212529 +} + +.btn-light:visited { + color: #212529 +} + +.btn-light:hover { + background-color: #e2e6ea; + border-color: #dae0e5; + color: #212529 +} + +.btn-light.focus, .btn-light:focus { + background-color: #e2e6ea; + border-color: #dae0e5; + box-shadow: 0 0 0 .2rem rgba(248, 249, 250, 0.5); + color: #212529 +} + +.btn-light.disabled, .btn-light:disabled { + background-color: #f8f9fa; + border-color: #f8f9fa; + color: #212529 +} + +.btn-light.active:not(:disabled):not(.disabled), .btn-light:not(:disabled):not(.disabled):active, .show>.btn-light.dropdown-toggle { + background-color: #dae0e5; + border-color: #d3d9df; + color: #212529 +} + +.btn-light.active:not(:disabled):not(.disabled):focus, .btn-light:not(:disabled):not(.disabled):active:focus, .show>.btn-light.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(248, 249, 250, 0.5) +} + +.btn-dark { + background-color: #343a40; + border-color: #343a40; + color: #fff +} + +.btn-dark:visited { + color: #fff +} + +.btn-dark:hover { + background-color: #23272b; + border-color: #1d2124; + color: #fff +} + +.btn-dark.focus, .btn-dark:focus { + background-color: #23272b; + border-color: #1d2124; + box-shadow: 0 0 0 .2rem rgba(52, 58, 64, 0.5); + color: #fff +} + +.btn-dark.disabled, .btn-dark:disabled { + background-color: #343a40; + border-color: #343a40; + color: #fff +} + +.btn-dark.active:not(:disabled):not(.disabled), .btn-dark:not(:disabled):not(.disabled):active, .show>.btn-dark.dropdown-toggle { + background-color: #1d2124; + border-color: #171a1d; + color: #fff +} + +.btn-dark.active:not(:disabled):not(.disabled):focus, .btn-dark:not(:disabled):not(.disabled):active:focus, .show>.btn-dark.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(52, 58, 64, 0.5) +} + +.btn-outline-primary { + border-color: #007bff; + color: #007bff +} + +.btn-outline-primary:visited { + color: #007bff +} + +.btn-outline-primary:hover { + background-color: #007bff; + border-color: #007bff; + color: #fff +} + +.btn-outline-primary.focus, .btn-outline-primary:focus { + box-shadow: 0 0 0 .2rem rgba(0, 123, 255, 0.5) +} + +.btn-outline-primary.disabled, .btn-outline-primary:disabled { + background-color: transparent; + color: #007bff +} + +.btn-outline-primary.active:not(:disabled):not(.disabled), .btn-outline-primary:not(:disabled):not(.disabled):active, .show>.btn-outline-primary.dropdown-toggle { + background-color: #007bff; + border-color: #007bff; + color: #fff +} + +.btn-outline-primary.active:not(:disabled):not(.disabled):focus, .btn-outline-primary:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-primary.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(0, 123, 255, 0.5) +} + +.btn-outline-secondary { + border-color: #6c757d; + color: #6c757d +} + +.btn-outline-secondary:visited { + color: #6c757d +} + +.btn-outline-secondary:hover { + background-color: #6c757d; + border-color: #6c757d; + color: #fff +} + +.btn-outline-secondary.focus, .btn-outline-secondary:focus { + box-shadow: 0 0 0 .2rem rgba(108, 117, 125, 0.5) +} + +.btn-outline-secondary.disabled, .btn-outline-secondary:disabled { + background-color: transparent; + color: #6c757d +} + +.btn-outline-secondary.active:not(:disabled):not(.disabled), .btn-outline-secondary:not(:disabled):not(.disabled):active, .show>.btn-outline-secondary.dropdown-toggle { + background-color: #6c757d; + border-color: #6c757d; + color: #fff +} + +.btn-outline-secondary.active:not(:disabled):not(.disabled):focus, .btn-outline-secondary:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-secondary.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(108, 117, 125, 0.5) +} + +.btn-outline-success { + border-color: #28a745; + color: #28a745 +} + +.btn-outline-success:visited { + color: #28a745 +} + +.btn-outline-success:hover { + background-color: #28a745; + border-color: #28a745; + color: #fff +} + +.btn-outline-success.focus, .btn-outline-success:focus { + box-shadow: 0 0 0 .2rem rgba(40, 167, 69, 0.5) +} + +.btn-outline-success.disabled, .btn-outline-success:disabled { + background-color: transparent; + color: #28a745 +} + +.btn-outline-success.active:not(:disabled):not(.disabled), .btn-outline-success:not(:disabled):not(.disabled):active, .show>.btn-outline-success.dropdown-toggle { + background-color: #28a745; + border-color: #28a745; + color: #fff +} + +.btn-outline-success.active:not(:disabled):not(.disabled):focus, .btn-outline-success:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-success.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(40, 167, 69, 0.5) +} + +.btn-outline-info { + border-color: #17a2b8; + color: #17a2b8 +} + +.btn-outline-info:visited { + color: #17a2b8 +} + +.btn-outline-info:hover { + background-color: #17a2b8; + border-color: #17a2b8; + color: #fff +} + +.btn-outline-info.focus, .btn-outline-info:focus { + box-shadow: 0 0 0 .2rem rgba(23, 162, 184, 0.5) +} + +.btn-outline-info.disabled, .btn-outline-info:disabled { + background-color: transparent; + color: #17a2b8 +} + +.btn-outline-info.active:not(:disabled):not(.disabled), .btn-outline-info:not(:disabled):not(.disabled):active, .show>.btn-outline-info.dropdown-toggle { + background-color: #17a2b8; + border-color: #17a2b8; + color: #fff +} + +.btn-outline-info.active:not(:disabled):not(.disabled):focus, .btn-outline-info:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-info.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(23, 162, 184, 0.5) +} + +.btn-outline-warning { + border-color: #ffc107; + color: #ffc107 +} + +.btn-outline-warning:visited { + color: #ffc107 +} + +.btn-outline-warning:hover { + background-color: #ffc107; + border-color: #ffc107; + color: #212529 +} + +.btn-outline-warning.focus, .btn-outline-warning:focus { + box-shadow: 0 0 0 .2rem rgba(255, 193, 7, 0.5) +} + +.btn-outline-warning.disabled, .btn-outline-warning:disabled { + background-color: transparent; + color: #ffc107 +} + +.btn-outline-warning.active:not(:disabled):not(.disabled), .btn-outline-warning:not(:disabled):not(.disabled):active, .show>.btn-outline-warning.dropdown-toggle { + background-color: #ffc107; + border-color: #ffc107; + color: #212529 +} + +.btn-outline-warning.active:not(:disabled):not(.disabled):focus, .btn-outline-warning:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-warning.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(255, 193, 7, 0.5) +} + +.btn-outline-danger { + border-color: #dc3545; + color: #dc3545 +} + +.btn-outline-danger:visited { + color: #dc3545 +} + +.btn-outline-danger:hover { + background-color: #dc3545; + border-color: #dc3545; + color: #fff +} + +.btn-outline-danger.focus, .btn-outline-danger:focus { + box-shadow: 0 0 0 .2rem rgba(220, 53, 69, 0.5) +} + +.btn-outline-danger.disabled, .btn-outline-danger:disabled { + background-color: transparent; + color: #dc3545 +} + +.btn-outline-danger.active:not(:disabled):not(.disabled), .btn-outline-danger:not(:disabled):not(.disabled):active, .show>.btn-outline-danger.dropdown-toggle { + background-color: #dc3545; + border-color: #dc3545; + color: #fff +} + +.btn-outline-danger.active:not(:disabled):not(.disabled):focus, .btn-outline-danger:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-danger.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(220, 53, 69, 0.5) +} + +.btn-outline-light { + border-color: #f8f9fa; + color: #f8f9fa +} + +.btn-outline-light:visited { + color: #f8f9fa +} + +.btn-outline-light:hover { + background-color: #f8f9fa; + border-color: #f8f9fa; + color: #212529 +} + +.btn-outline-light.focus, .btn-outline-light:focus { + box-shadow: 0 0 0 .2rem rgba(248, 249, 250, 0.5) +} + +.btn-outline-light.disabled, .btn-outline-light:disabled { + background-color: transparent; + color: #f8f9fa +} + +.btn-outline-light.active:not(:disabled):not(.disabled), .btn-outline-light:not(:disabled):not(.disabled):active, .show>.btn-outline-light.dropdown-toggle { + background-color: #f8f9fa; + border-color: #f8f9fa; + color: #212529 +} + +.btn-outline-light.active:not(:disabled):not(.disabled):focus, .btn-outline-light:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-light.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(248, 249, 250, 0.5) +} + +.btn-outline-dark { + border-color: #343a40; + color: #343a40 +} + +.btn-outline-dark:visited { + color: #343a40 +} + +.btn-outline-dark:hover { + background-color: #343a40; + border-color: #343a40; + color: #fff +} + +.btn-outline-dark.focus, .btn-outline-dark:focus { + box-shadow: 0 0 0 .2rem rgba(52, 58, 64, 0.5) +} + +.btn-outline-dark.disabled, .btn-outline-dark:disabled { + background-color: transparent; + color: #343a40 +} + +.btn-outline-dark.active:not(:disabled):not(.disabled), .btn-outline-dark:not(:disabled):not(.disabled):active, .show>.btn-outline-dark.dropdown-toggle { + background-color: #343a40; + border-color: #343a40; + color: #fff +} + +.btn-outline-dark.active:not(:disabled):not(.disabled):focus, .btn-outline-dark:not(:disabled):not(.disabled):active:focus, .show>.btn-outline-dark.dropdown-toggle:focus { + box-shadow: 0 0 0 .2rem rgba(52, 58, 64, 0.5) +} + +.btn-link { + color: #007bff; + font-weight: 400; + text-decoration: none +} + +.btn-link:hover { + color: #0056b3; + text-decoration: underline +} + +.btn-link.focus, .btn-link:focus { + box-shadow: none; + text-decoration: underline +} + +.btn-link.disabled, .btn-link:disabled { + color: #6c757d; + pointer-events: none +} + +.btn-group-lg>.btn, .btn-lg { + border-radius: .3rem; + font-size: 1.25rem; + line-height: 1.5; + padding: .5rem 1rem +} + +.btn-group-sm>.btn, .btn-sm { + border-radius: .2rem; + font-size: .875rem; + line-height: 1.5; + padding: .25rem .5rem +} + +.btn-block { + display: block; + width: 100% +} + +.btn-block+.btn-block { + margin-top: .5rem +} + +input.btn-block[type=button], input.btn-block[type=reset], input.btn-block[type=submit] { + width: 100% +} + +.stretched-link::after { + background-color: rgba(0, 0, 0, 0); + bottom: 0; + content: ''; + left: 0; + pointer-events: auto; + position: absolute; + right: 0; + top: 0; + z-index: 1 +} + +.text-wrap { + white-space: normal !important +} + +.card { + background-clip: border-box; + background-color: #fff; + border: 1px solid rgba(0, 0, 0, 0.125); + border-radius: .25rem; + display: -ms-flexbox; + display: flex; + -ms-flex-direction: column; + flex-direction: column; + min-width: 0; + position: relative; + word-wrap: break-word +} + +.card>hr { + margin-left: 0; + margin-right: 0 +} + +.card>.list-group:first-child .list-group-item:first-child { + border-top-left-radius: .25rem; + border-top-right-radius: .25rem +} + +.card>.list-group:last-child .list-group-item:last-child { + border-bottom-left-radius: .25rem; + border-bottom-right-radius: .25rem +} + +.card-body { + -ms-flex: 1 1 auto; + flex: 1 1 auto; + min-height: 1px; + padding: 1.25rem +} + +.card-title { + margin-bottom: .75rem +} + +.card-subtitle { + margin-bottom: 0; + margin-top: -.375rem +} + +.card-text:last-child { + margin-bottom: 0 +} + +.card-link:hover { + text-decoration: none +} + +.card-link+.card-link { + margin-left: 1.25rem +} + +.card-header { + background-color: rgba(0, 0, 0, 0.03); + border-bottom: 1px solid rgba(0, 0, 0, 0.125); + margin-bottom: 0; + padding: .75rem 1.25rem +} + +.card-header:first-child { + border-radius: calc(.25rem - 1px) calc(.25rem - 1px) 0 0 +} + +.card-header+.list-group .list-group-item:first-child { + border-top: 0 +} + +.card-footer { + background-color: rgba(0, 0, 0, 0.03); + border-top: 1px solid rgba(0, 0, 0, 0.125); + padding: .75rem 1.25rem +} + +.card-footer:last-child { + border-radius: 0 0 calc(.25rem - 1px) calc(.25rem - 1px) +} + +.card-header-tabs { + border-bottom: 0; + margin-bottom: -.75rem; + margin-left: -.625rem; + margin-right: -.625rem +} + +.card-header-pills { + margin-left: -.625rem; + margin-right: -.625rem +} + +.card-img-overlay { + bottom: 0; + left: 0; + padding: 1.25rem; + position: absolute; + right: 0; + top: 0 +} + +.card-img, .card-img-bottom, .card-img-top { + -ms-flex-negative: 0; + flex-shrink: 0; + width: 100% +} + +.card-img, .card-img-top { + border-top-left-radius: calc(.25rem - 1px); + border-top-right-radius: calc(.25rem - 1px) +} + +.card-img, .card-img-bottom { + border-bottom-left-radius: calc(.25rem - 1px); + border-bottom-right-radius: calc(.25rem - 1px) +} + +.w-100 { + width: 100% !important +} + +.shadow { + box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15) !important +} + +.bg-primary { + background-color: #007bff !important +} + +button.bg-primary:focus, button.bg-primary:hover { + background-color: #0062cc !important +} + +a.bg-primary:focus, a.bg-primary:hover { + background-color: #0062cc !important +} + +a.text-primary:focus, a.text-primary:hover { + color: #121416 !important +} + +.bg-secondary { + background-color: #6c757d !important +} + +button.bg-secondary:focus, button.bg-secondary:hover { + background-color: #545b62 !important +} + +a.bg-secondary:focus, a.bg-secondary:hover { + background-color: #545b62 !important +} + +a.text-secondary:focus, a.text-secondary:hover { + color: #121416 !important +} + +.bg-success { + background-color: #28a745 !important +} + +button.bg-success:focus, button.bg-success:hover { + background-color: #1e7e34 !important +} + +a.bg-success:focus, a.bg-success:hover { + background-color: #1e7e34 !important +} + +a.text-success:focus, a.text-success:hover { + color: #121416 !important +} + +.bg-info { + background-color: #17a2b8 !important +} + +button.bg-info:focus, button.bg-info:hover { + background-color: #117a8b !important +} + +a.bg-info:focus, a.bg-info:hover { + background-color: #117a8b !important +} + +a.text-info:focus, a.text-info:hover { + color: #121416 !important +} + +.bg-warning { + background-color: #ffc107 !important +} + +button.bg-warning:focus, button.bg-warning:hover { + background-color: #d39e00 !important +} + +a.bg-warning:focus, a.bg-warning:hover { + background-color: #d39e00 !important +} + +a.text-warning:focus, a.text-warning:hover { + color: #121416 !important +} + +.bg-danger { + background-color: #dc3545 !important +} + +button.bg-danger:focus, button.bg-danger:hover { + background-color: #bd2130 !important +} + +a.bg-danger:focus, a.bg-danger:hover { + background-color: #bd2130 !important +} + +a.text-danger:focus, a.text-danger:hover { + color: #121416 !important +} + +.bg-light { + background-color: #f8f9fa !important +} + +button.bg-light:focus, button.bg-light:hover { + background-color: #dae0e5 !important +} + +a.bg-light:focus, a.bg-light:hover { + background-color: #dae0e5 !important +} + +a.text-light:focus, a.text-light:hover { + color: #121416 !important +} + +.bg-dark { + background-color: #343a40 !important +} + +button.bg-dark:focus, button.bg-dark:hover { + background-color: #1d2124 !important +} + +a.bg-dark:focus, a.bg-dark:hover { + background-color: #1d2124 !important +} + +a.text-dark:focus, a.text-dark:hover { + color: #121416 !important +} + +.bg-white { + background-color: #fff !important +} + +button.bg-white:focus, button.bg-white:hover { + background-color: #e6e6e6 !important +} + +a.bg-white:focus, a.bg-white:hover { + background-color: #e6e6e6 !important +} + +a.text-white:focus, a.text-white:hover { + color: #121416 !important +} + +.text-primary { + color: #007bff !important +} + +.text-secondary { + color: #6c757d !important +} + +.text-success { + color: #28a745 !important +} + +.text-info { + color: #17a2b8 !important +} + +.text-warning { + color: #ffc107 !important +} + +.text-danger { + color: #dc3545 !important +} + +.text-light { + color: #f8f9fa !important +} + +.text-dark { + color: #343a40 !important +} + +.text-white { + color: #fff !important +} + +.text-body { + color: #212529 !important +} + +.text-muted { + color: #6c757d !important +} + +.text-black-50 { + color: rgba(0, 0, 0, 0.5) !important +} + +.text-white-50 { + color: rgba(255, 255, 255, 0.5) !important +} + +.bg-transparent { + background-color: transparent !important +} + +.text-justify { + text-align: justify !important +} + +.text-left { + text-align: left !important +} + +.text-right { + text-align: right !important +} + +.text-center { + text-align: center !important +} + +.font-weight-light { + font-weight: 300 !important +} + +.font-weight-lighter { + font-weight: lighter !important +} + +.font-weight-normal { + font-weight: 400 !important +} + +.font-weight-bold { + font-weight: 700 !important +} + +.font-weight-bolder { + font-weight: bolder !important +} + +.font-italic { + font-style: italic !important +} + +.container { + margin-left: auto; + margin-right: auto; + padding-left: 15px; + padding-right: 15px; + width: 100% +} + +@media (min-width: 576px) { + .container { + max-width: 540px + } +} + +@media (min-width: 768px) { + .container { + max-width: 720px + } +} + +@media (min-width: 992px) { + .container { + max-width: 960px + } +} + +@media (min-width: 1200px) { + .container { + max-width: 1140px + } +} + +.container-fluid, .container-lg, .container-md, .container-sm, .container-xl { + margin-left: auto; + margin-right: auto; + padding-left: 15px; + padding-right: 15px; + width: 100% +} + +@media (min-width: 576px) { + .container, .container-sm { + max-width: 540px + } +} + +@media (min-width: 768px) { + .container, .container-md, .container-sm { + max-width: 720px + } +} + +@media (min-width: 992px) { + .container, .container-lg, .container-md, .container-sm { + max-width: 960px + } +} + +@media (min-width: 1200px) { + .container, .container-lg, .container-md, .container-sm, .container-xl { + max-width: 1140px + } +} + +.row { + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + margin-left: -15px; + margin-right: -15px +} + +.col-lg, .col-lg-1, .col-lg-10, .col-lg-11, .col-lg-12, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-auto, .col-md, .col-md-1, .col-md-10, .col-md-11, .col-md-12, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-auto, .col-sm, .col-sm-1, .col-sm-10, .col-sm-11, .col-sm-12, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-auto, .col-xl, .col-xl-1, .col-xl-10, .col-xl-11, .col-xl-12, .col-xl-2, .col-xl-3, .col-xl-4, .col-xl-5, .col-xl-6, .col-xl-7, .col-xl-8, .col-xl-9, .col-xl-auto { + padding-left: 15px; + padding-right: 15px; + position: relative; + width: 100% +} + +@media (min-width: 576px) { + .col-sm { + flex-basis: 0; + flex-grow: 1; + -ms-flex-positive: 1; + -ms-flex-preferred-size: 0; + max-width: 100% + } + .col-sm-auto { + -ms-flex: 0 0 auto; + flex: 0 0 auto; + max-width: 100%; + width: auto + } + .col-sm-1 { + -ms-flex: 0 0 8.33333%; + flex: 0 0 8.33333%; + max-width: 8.33333% + } + .col-sm-2 { + -ms-flex: 0 0 16.66667%; + flex: 0 0 16.66667%; + max-width: 16.66667% + } + .col-sm-3 { + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25% + } + .col-sm-4 { + -ms-flex: 0 0 33.33333%; + flex: 0 0 33.33333%; + max-width: 33.33333% + } + .col-sm-5 { + -ms-flex: 0 0 41.66667%; + flex: 0 0 41.66667%; + max-width: 41.66667% + } + .col-sm-6 { + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50% + } + .col-sm-7 { + -ms-flex: 0 0 58.33333%; + flex: 0 0 58.33333%; + max-width: 58.33333% + } + .col-sm-8 { + -ms-flex: 0 0 66.66667%; + flex: 0 0 66.66667%; + max-width: 66.66667% + } + .col-sm-9 { + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75% + } + .col-sm-10 { + -ms-flex: 0 0 83.33333%; + flex: 0 0 83.33333%; + max-width: 83.33333% + } + .col-sm-11 { + -ms-flex: 0 0 91.66667%; + flex: 0 0 91.66667%; + max-width: 91.66667% + } + .col-sm-12 { + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100% + } +} + +@media (min-width: 768px) { + .col-md { + flex-basis: 0; + flex-grow: 1; + -ms-flex-positive: 1; + -ms-flex-preferred-size: 0; + max-width: 100% + } + .col-md-auto { + -ms-flex: 0 0 auto; + flex: 0 0 auto; + max-width: 100%; + width: auto + } + .col-md-1 { + -ms-flex: 0 0 8.33333%; + flex: 0 0 8.33333%; + max-width: 8.33333% + } + .col-md-2 { + -ms-flex: 0 0 16.66667%; + flex: 0 0 16.66667%; + max-width: 16.66667% + } + .col-md-3 { + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25% + } + .col-md-4 { + -ms-flex: 0 0 33.33333%; + flex: 0 0 33.33333%; + max-width: 33.33333% + } + .col-md-5 { + -ms-flex: 0 0 41.66667%; + flex: 0 0 41.66667%; + max-width: 41.66667% + } + .col-md-6 { + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50% + } + .col-md-7 { + -ms-flex: 0 0 58.33333%; + flex: 0 0 58.33333%; + max-width: 58.33333% + } + .col-md-8 { + -ms-flex: 0 0 66.66667%; + flex: 0 0 66.66667%; + max-width: 66.66667% + } + .col-md-9 { + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75% + } + .col-md-10 { + -ms-flex: 0 0 83.33333%; + flex: 0 0 83.33333%; + max-width: 83.33333% + } + .col-md-11 { + -ms-flex: 0 0 91.66667%; + flex: 0 0 91.66667%; + max-width: 91.66667% + } + .col-md-12 { + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100% + } +} + +@media (min-width: 992px) { + .col-lg { + flex-basis: 0; + flex-grow: 1; + -ms-flex-positive: 1; + -ms-flex-preferred-size: 0; + max-width: 100% + } + .col-lg-auto { + -ms-flex: 0 0 auto; + flex: 0 0 auto; + max-width: 100%; + width: auto + } + .col-lg-1 { + -ms-flex: 0 0 8.33333%; + flex: 0 0 8.33333%; + max-width: 8.33333% + } + .col-lg-2 { + -ms-flex: 0 0 16.66667%; + flex: 0 0 16.66667%; + max-width: 16.66667% + } + .col-lg-3 { + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25% + } + .col-lg-4 { + -ms-flex: 0 0 33.33333%; + flex: 0 0 33.33333%; + max-width: 33.33333% + } + .col-lg-5 { + -ms-flex: 0 0 41.66667%; + flex: 0 0 41.66667%; + max-width: 41.66667% + } + .col-lg-6 { + -ms-flex: 0 0 50%; 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- - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - - - - - - - retrieved - - - remote_folder - - output_parameters - ParameterData - FolderData - RemoteData - energy=-8eVforces=[...]... - aiida.outdata-file.xml... - /scratch/run/a3/2b/2d62@cluster.uni.edu - - - - - - output_parameters - Additionalparsednodes - - - - - ... - - - StructureData - ParameterData - KpointsData - Code - UpfData - - - - - - - - - - - - - - - PwCalculation - - code - - structure - - - parameters - - - kpoints - - pseudo_Ba - - pseudo_Ti - - pseudo_O - Ba-lda.UPF - Ti-lda.UPF - O-lda.UPF - 3x3x3 - BaTiO3alat=4 Å - cutoff=50Ry... - /home/.../pw.x@cluster.uni.edu - - - diff --git a/docs/assets/2018_PRACE_MaX/latex/.gitignore b/docs/assets/2018_PRACE_MaX/latex/.gitignore deleted file mode 100644 index ddfcde18..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/.gitignore +++ /dev/null @@ -1,14 +0,0 @@ -*.synctex.gz -*.aux -*.log -*.out -*.toc -*~ -*.backup -*.fls -*.fdb_latexmk -AiiDA_tutorial.pdf -AiiDA_tutorial_online.pdf -AiiDA_tutorial_addition.pdf -*-clean.tex -*.db diff --git a/docs/assets/2018_PRACE_MaX/latex/.travis.yml b/docs/assets/2018_PRACE_MaX/latex/.travis.yml deleted file mode 100644 index 8aee657d..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/.travis.yml +++ /dev/null @@ -1,27 +0,0 @@ -dist: trusty -sudo: false - -language: python - -addons: - apt: - packages: - - texlive-base - - texlive-generic-recommended - - texlive-fonts-recommended - - texlive-latex-base - - texlive-latex-recommended - - texlive-latex-extra - - latex-xcolor - - pgf - - dvipng - - dvidvi - -env: -- LATEX_INTERACTION_MODE="-interaction=nonstopmode" - -## Check that the documentation compiles correctly -script: - - cd latex - - make all - - cd .. diff --git a/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial.tex b/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial.tex deleted file mode 100644 index 3af66e05..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial.tex +++ /dev/null @@ -1,22 +0,0 @@ -\documentclass[twoside,a4paper,11pt]{extarticle} - -\usepackage{etoolbox} - -% Version for real-life tutorial (online = false) -\newtoggle{online} -\togglefalse{online} - -%% From tutorial to tutorial, the only things to change (for headers, authors, etc.) -%% Should all be only inside here -\input{sections/custom_specifications.tex} - -\input{sections/preamble.tex} -\usepackage{sections/defs-private} - -\begin{document} - -\input{sections/main_body.tex} - -\input{sections/biblio.tex} - -\end{document} diff --git a/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial_addition.tex b/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial_addition.tex deleted file mode 100644 index a6e154b1..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial_addition.tex +++ /dev/null @@ -1,28 +0,0 @@ -\documentclass[twoside,a4paper,11pt]{extarticle} - -\usepackage{etoolbox} - -% Version for real-life tutorial (online = false) -\newtoggle{online} -\togglefalse{online} - -%% From tutorial to tutorial, the only things to change (for headers, authors, etc.) -%% Should all be only inside here -\input{sections/custom_specifications.tex} - -\input{sections/preamble.tex} -\usepackage{sections/defs-private} - -\let\origtitle\thetitle -\title{\origtitle{}---Additional part} - - -\begin{document} - -\maketitle - -\tableofcontents - -\input{sections/8_computer_code_setup.tex} - -\end{document} diff --git a/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial_online.tex b/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial_online.tex deleted file mode 100644 index e60d9a64..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/AiiDA_tutorial_online.tex +++ /dev/null @@ -1,22 +0,0 @@ -\documentclass[twoside,a4paper,11pt]{extarticle} - -\usepackage{etoolbox} - -% Version for online tutorial (online = true) -\newtoggle{online} -\toggletrue{online} - -%% From tutorial to tutorial, the only things to change (for headers, authors, etc.) -%% Should all be only inside here -\input{sections/custom_specifications.tex} - -\input{sections/preamble.tex} -\usepackage{sections/defs-private} - -\begin{document} - -\input{sections/main_body.tex} - -\input{sections/biblio.tex} - -\end{document} diff --git a/docs/assets/2018_PRACE_MaX/latex/Makefile b/docs/assets/2018_PRACE_MaX/latex/Makefile deleted file mode 100644 index 4253f329..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/Makefile +++ /dev/null @@ -1,25 +0,0 @@ -deps:=$(wildcard sections/*.tex) - -temp_files = $(shell find . -name "*aux" -o -name "*log" -o -name "*toc" -o -name "*out" -o -name "*-clean.tex" -o -name "*.db" -o -name "*.md") - -pdflatex: $(patsubst %.tex, %.pdf, $(wildcard *.tex)) - -%.pdf: %.tex $(deps) - pdflatex ${LATEX_INTERACTION_MODE} $< && pdflatex $< && pdflatex $< - - -.PHONY: all clean distclean - -clean: - rm -f ${temp_files} - -distclean: clean - rm -f AiiDA_tutorial.pdf AiiDA_tutorial_online.pdf - -markdown: $(deps) - ./de-macro.py AiiDA_tutorial.tex - pandoc -s --filter pandocfilter.py AiiDA_tutorial-clean.tex -t markdown_github-fenced_code_attributes -o AiiDA_tutorial-clean.md - -rst: $(deps) - ./de-macro.py AiiDA_tutorial.tex - pandoc -s --filter pandocfilter.py AiiDA_tutorial-clean.tex -t rst -o AiiDA_tutorial-clean.rst diff --git a/docs/assets/2018_PRACE_MaX/latex/README.md b/docs/assets/2018_PRACE_MaX/latex/README.md deleted file mode 100644 index 03be2e2d..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Contents - -This folder contains the LaTeX source for the 2018 PRACE-MaX AiiDA tutorial. - -**Note:** The LaTeX source is kept only for historical reasons -and has been replaced by the markdown/rst based version. - -## Compiling LaTeX source - -``` -make -``` - -## Export to markdown/rst - -The LaTeX source can be converted as follows: - -### Prerequisites - -* pandoc -* python3 - -### Conversion - -```console -$ pip install -r requirements.txt -$ make markdown -$ make rst -``` diff --git a/docs/assets/2018_PRACE_MaX/latex/aiidatutorial.kilepr b/docs/assets/2018_PRACE_MaX/latex/aiidatutorial.kilepr deleted file mode 100644 index 814162dd..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/aiidatutorial.kilepr +++ /dev/null @@ -1,127 +0,0 @@ -[General] -bib_extensions=.bib -bibliographyBackendAutoDetected= -bibliographyBackendUserOverride= -def_graphic_ext=eps -img_extIsRegExp=false -img_extensions=.eps .jpg .jpeg .png .pdf .ps .fig .gif -kileprversion=3 -kileversion=2.9.91 -masterDocument= -name=AiiDA Tutorial -pkg_extIsRegExp=false -pkg_extensions=.cls .sty .bbx .cbx .lbx -src_extIsRegExp=false -src_extensions=.tex .ltx .latex .dtx .ins - -[Tools] -MakeIndex= -QuickBuild= - -[item:AiiDA_tutorial.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:AiiDA_tutorial_addition.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:AiiDA_tutorial_online.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:aiidatutorial.kilepr] -archive=true -encoding=UTF-8 -highlight=None -mode=Normal - -[item:sections/0_preparation.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/1_1_1_verdi_graphs.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/1_1_2_verdi_shell_and_orm.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/1_2_qe_submission.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/2_1_querybuilder.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/2_1_querybuilder_jupyter.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/2_2_workflows.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/3_computer_code_setup.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/appendix_1.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/appendix_2.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/biblio.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/custom_specifications.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/main_body.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX - -[item:sections/preamble.tex] -archive=true -encoding=UTF-8 -highlight=LaTeX -mode=LaTeX diff --git a/docs/assets/2018_PRACE_MaX/latex/de-macro.py b/docs/assets/2018_PRACE_MaX/latex/de-macro.py deleted file mode 100755 index e7daf949..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/de-macro.py +++ /dev/null @@ -1,1103 +0,0 @@ -#!/usr/bin/python -O -""" -Copyright 2005 Peter Gacs -Licensed under the Academic Free Licence version 2.1 - - DE-MACRO - -Version 1.3 - this version is much more conservative about deleting - comments and inserting or deleting blank space: tries to - leave in all comments, adds space only when necessary, and - tries not to delete space in the main text. - The motivating comments came from Daniel Webb. -Version 1.2 - a syntactical bug corrected, thanks Brian de Alwis! - - -PURPOSE - -This program can eliminate most private macros from a LaTeX file. -Applications: - - your publisher has difficulty dealing with many private macros - - you cooperate with colleagues who do not understand your macros - - preprocessing before a system like latex2html, which is somewhat - unpredictable with private macros. - -USAGE - -de-macro [--defs ] [.tex] [[.tex] ...] - -Simplest example: de-macro testament - -(As you see, the <> is used only in the notation of this documentation, -you should not type it.) - -If contains a command \usepackage{-private} -then the file -private.sty will be read, and its macros will be -replaced in with their definitions. -The result is in -clean.tex. - -Only newcommand, renewcommand, newenvironment, and renewenvironment are -understood (it does not matter, whether you write new or renew). -These can be nested but do not be too clever, since I do not -guarantee the same expansion order as in TeX. - -FILES - -.db --clean.tex --private.sty - -For speed, a macro database file called .db is created. -If such a file exists already then it is used. -If -private.sty is older than .db then it will not -be used. - -It is possible to specify another database filename via --defs . -Then .db will be used. - -For each , a file -clean.tex will be produced. -If -clean.tex is newer than .tex then it stays. - -INPUT COMMAND - -If a tex file contains a command \input{} or \input -then .tex is processed recursively, and -clean.tex -will be inserted into the final output. -For speed, if -clean.tex is newer than .tex -then .tex will not be reprocessed. - -The dependency checking is not sophisticated, so if you rewrite some macros -then remove all *-clean.tex files! - -""" - -import sys, os, re, shelve - -# Utilities - - -class No_detail: - strerror = "" - - -no_detail = No_detail() - - -class Error(Exception): - """Base class for exceptions in this module.""" - pass - - -class Empty_text_error(Error): - """Exception raised for errors in the input. - - Attributes: - data -- data that was found empty - message - """ - - def __init__(self, data, message): - self.data = data - self.message = message - - -def warn(error_message, detail=no_detail): - sys.stderr.write(error_message + "\n") - if no_detail != detail: - sys.stderr.write(detail.strerror + "\n") - - -def die(error_message, detail=no_detail): - warn(error_message, detail=no_detail) - sys.exit(1) - - -def getopt_map(one_letter_opts, long_optlist): - "Turns long options into an option map, using getopt." - import getopt - optlist, args = getopt.getopt(sys.argv[1:], one_letter_opts, long_optlist) - opt_map = {} - for pair in optlist: - opt_map[pair[0]] = pair[1] or 1 - return opt_map, args - - -def newer(file1, file2): - - if not os.path.isfile(file1): - return False - - try: - stat_return = os.lstat(file1) - except OSError, detail: - die("lstat " + file1 + " failed:", detail) - time1 = stat_return.st_mtime - - try: - stat_return = os.lstat(file2) - except OSError, detail: - die("lstat " + file2 + " failed:", detail) - time2 = stat_return.st_mtime - - return time1 > time2 - - -def cut_extension(filename, ext): - """ - If filename has extension ext (including the possible dot), - it will be cut off. - """ - file = filename - index = filename.rfind(ext) - if 0 <= index and len(file) - len(ext) == index: - file = file[:index] - return file - - -class Stream: - data = None - pos = None - item = None - - def legal(self): - return 0 <= self.pos and self.pos < len(self.data) - - def uplegal(self): - return self.pos < len(self.data) - - def __init__(self, data_v=None): - self.data = data_v - if self.data: - self.pos = 0 - self.item = self.data[self.pos] - - def next(self): - self.pos += 1 - if self.pos < len(self.data): - self.item = self.data[self.pos] - return self.item - - def reset(self): - if self.data and 0 < len(self.data): - self.pos = 0 - self.item = self.data[0] - return self.item - - -# Basic classes - -blank_re = re.compile(r"\s") -blanked_filename_re = re.compile(r"^\s+(\w*)\s+") -braced_filename_re = re.compile(r"^\s*{\s*(\w*)\s*}") -blank_or_rbrace_re = re.compile(r"[\s}]") -pos_digit_re = re.compile(r"[1-9]") - - -def isletter(c, isatletter=False): - if "@" == c: - return isatletter - else: - return c.isalpha() - - -class Token: - """Type 0 means ordinary character, type 1 means escape sequence - (without the \ ), type 2 means comment. - """ - simple_ty = 0 - esc_symb_ty = 1 - esc_str_ty = 2 - comment_ty = 3 - - type = simple_ty - val = " " - - def __init__(self, type_v=simple_ty, val_v=" "): - self.type = type_v - self.val = val_v - - def show(self, isatletter=False): - out = "" - if simple_ty == self.type or comment_ty == self.type: - out = self.val - else: - out = "\\" + self.val - return out - - -# Constants - -g_token = Token(0, " ") # generic token -simple_ty = g_token.simple_ty -comment_ty = g_token.comment_ty -esc_symb_ty = g_token.esc_symb_ty -esc_str_ty = g_token.esc_str_ty - - -def detokenize(text): - """ - Input is a list of tokens. - Output is a string. - """ - out = "" - if 0 == len(text): - return - pos = 0 - out += text[pos].show() - pos += 1 - while pos < len(text): - previtem = text[pos - 1] - item = text[pos] - """Insert a separating space after an escape sequence if it is a - string and is followed by a letter.""" - if (esc_str_ty == previtem.type and simple_ty == item.type - and isletter(item.val[0], False)): - out += " " - out += item.show() - pos += 1 - return out - - -def strip_comments(text): - """ - Input is a list of tokens. - Output is the same list except the comment tokens. - """ - out = [] - for token in text: - if not comment_ty == token.type: - out.append(token) - return out - - -class Group: - """type 0 means a token, type 1 means contents of a group within {} - """ - token_ty = 0 - group_ty = 1 - type = token_ty - val = [] # Value is a token list. - - def __init__(self, type_v, val_v): - self.type = type_v - self.val = val_v - - def show(self): - if token_ty == self.type: - return self.val.show() - else: - return "{%s}" % detokenize(self.val) - - -# Constants - -g_group = Group(0, []) -token_ty = g_group.token_ty -group_ty = g_group.group_ty - - -def tokenize(in_str): - """Returns a list of tokens. - """ - text = [] - isatletter = False - cs = Char_stream(in_str) - cs.reset() - if not cs.legal(): - raise "No string to tokenize." - while cs.uplegal(): - if "%" == cs.item: - comment = cs.scan_comment_token() - text.append(Token(comment_ty, comment)) - elif "\\" != cs.item: - text.append(Token(simple_ty, cs.item)) - cs.next() - else: - cs.next() - name = cs.scan_escape_token(isatletter) - if isletter(name[0], isatletter): - token = Token(esc_str_ty, name) - else: - token = Token(esc_symb_ty, name) - text.append(token) - if "makeatletter" == name: - isatletter = True - elif "makeatother" == name: - isatletter = False - return text - - -class Command_def: - name = "1" - numargs = 0 - body = "" - - def __init__(self, name_v, numargs_v, body_v): - self.name = name_v - self.numargs = numargs_v - self.body = body_v - - def show(self): - out = "\\newcommand{\\%s}" % (self.name) - if 0 < self.numargs: - out += "[%d]" % self.numargs - out += "{%s}" % detokenize(self.body) - return out - - -class Env_def: - name = "1" - numargs = 0 - begin = "" - end = "" - - def __init__(self, name_v, numargs_v, begin_v, end_v): - self.name = name_v - self.numargs = numargs_v - self.begin = begin_v - self.end = end_v - - def show(self): - out = "\\newenvironment{%s}" % self.name - if 0 < self.numargs: - out += "[%d]" % self.numargs - out += "{%s}" % detokenize(self.begin) - out += "{%s}" % detokenize(self.end) - return out - - -class Command_instance: - name = "1" - args = [] - - def __init__(self, name_v, args_v): - self.name = name_v - self.args = args_v - - def show(self): - out = "\\" + self.name - for arg in self.args: - out += "{%s}" % detokenize(arg) - return out - - -class Env_instance: - name = "1" - args = [] - - def __init__(self, name_v, args_v, body_v): - self.name = name_v - self.args = args_v - self.body = body_v - - def show(self): - out = "\\begin{%s}" % self.name - for arg in self.args: - out += "{%s}" % detokenize(arg) - out += detokenize(self.body) - out += "\\end{%s}" % self.name - return out - - -class Char_stream(Stream): - def scan_escape_token(self, isatletter=False): - """ - Starts after the escape sign, assumes that it is scanning a symbol. - Returns a token-string. - """ - out = self.item # Continue only if this is a letter. - item = self.next() - if isletter(out, isatletter): - while self.uplegal() and isletter(item, isatletter): - out += item - item = self.next() - return out - - def scan_comment_token(self): - """ - Starts at the comment sign %, assumes that it is scanning a comment. - Returns the whole comment string, - including the % and all empty space after it. - """ - comment = "" - while "\n" != self.item: - comment += self.item - self.next() - while self.uplegal() and blank_re.match(self.item): - comment += self.item - self.next() - return comment - - def scan_input_filename(self): - """We just read an \input token. The next group or word will be - interpreted as a filename (possibly without .tex). - Return the filename. - """ - item = self.item - while self.uplegal() and blank_re.match(self.item): - item = self.next() - if "{" == item: - item = self.next() - file = "" - while self.uplegal() and not blank_or_rbrace_re.match(item): - file += item - item = self.next() - self.next() - return file - - def scan_package_filenames(self): - """We just read a \usepackage token. The next group will be - interpreted as a list of filenames (without .sty) separated by commas. - Return the list. - """ - item = self.item - while self.uplegal() and blank_re.match(item): - item = self.next() - file = "" - if not "{" == item: - raise "\\usepackage not followed by brace." - item = self.next() - while self.uplegal() and not blank_or_rbrace_re.match(item): - file += item - item = self.next() - self.next() - return file.split(",") - - -class Tex_stream(Stream): - - defs = ({}, {}) - defs_db = "x" - defs_db_file = "x.db" - debug = False - - def smart_tokenize(self, in_str, handle_inputs=False): - """Returns a list of tokens. - It may interpret and carry out all \input commands. - """ - self.data = [] - text = self.data - isatletter = False - cs = Char_stream(in_str) - cs.reset() - if not cs.legal(): - raise "No string to tokenize." - while cs.uplegal(): - if "%" == cs.item: - comment = cs.scan_comment_token() - text.append(Token(comment_ty, comment)) - elif "\\" != cs.item: - text.append(Token(simple_ty, cs.item)) - cs.next() - else: - cs.next() - name = cs.scan_escape_token(isatletter) - if "input" == name and handle_inputs: - file = cs.scan_input_filename() - to_add = self.process_if_newer(file) - text.extend(to_add) - elif "usepackage" == name: - while cs.uplegal() and blank_re.match(cs.item): - cs.next() - if "[" == cs.item: # private packages have no options - text.extend([ - Token(esc_str_ty, "usepackage"), - Token(simple_ty, "[") - ]) - cs.next() - continue - files = cs.scan_package_filenames() - i = 0 - while i < len(files): # process private packages - file = files[i] - p = file.rfind("-private") - if p < 0 or not len(file) - len("-private") == p: - i += 1 - continue - defs_db_file = file + ".db" - self.add_defs(file) - del files[i:(i + 1)] - if files: # non-private packages left - group_content = ",".join(files) - to_add_str = "\\usepackage{%s}" % (group_content) - to_add = tokenize(to_add_str) - text.extend(to_add) - else: - if isletter(name[0], isatletter): - token = Token(esc_str_ty, name) - else: - token = Token(esc_symb_ty, name) - text.append(token) - if "makeatletter" == name: - isatletter = True - elif "makeatother" == name: - isatletter = False - self.reset() - return self.data - - def smart_detokenize(self): - """ - Output is a string. - If the list contains an \input{file} then the content of file - file-clean.tex replaces it in the output. - """ - self.reset() - if not self.legal(): - return "" - out = "" - previtem = None - while self.uplegal(): - item = self.item - """Insert a separating space after an escape sequence if it is a - string and is followed by a letter.""" - if (None != previtem and esc_str_ty == previtem.type - and simple_ty == item.type - and isletter(item.val[0], False)): - out += " " - previtem = item - if not (esc_str_ty == item.type and "input" == item.val): - out += item.show() - self.next() - else: - self.next() - group = self.scan_group() - file = detokenize(group.val) - clean_file = "%s-clean.tex" % (file) - print "Reading file %s" % (clean_file) - fp = open(clean_file, "r") - content = fp.read() - fp.close() - out += content - return out - - # Basic tex scanning - - def skip_blank_tokens(self): # we also skip comment tokens. - item = self.item - while (self.uplegal() - and (comment_ty == item.type or - (simple_ty == item.type and blank_re.match(item.val)))): - item = self.next() - return item - - def scan_group(self): - """Returns group. - """ - if not self.legal(): - raise "No group to scan." - item = self.item - if not (simple_ty == item.type and "{" == item.val): - return Group(token_ty, [self.item]) - count = 1 - group = [] - item = self.next() - while count and self.uplegal(): - if simple_ty == item.type: - if "{" == item.val: - count += 1 - elif "}" == item.val: - count -= 1 - if count != 0: - group.append(item) - item = self.next() - return Group(group_ty, group) - - # Command and environment definitions - - def scan_command_name(self): - """Returns name. - """ - if not self.legal(): - raise "No command name to scan." - item = self.item - name = "" - if item.type in [esc_symb_ty, esc_str_ty]: - name = item.val - else: - if not "{" == item.val: - raise "Command definition misses first {." - self.next() - item = self.skip_blank_tokens() - if not item.type in [esc_symb_ty, esc_str_ty]: - raise "Command definition does not begin with control sequence." - name = item.val - self.next() - item = self.skip_blank_tokens() - if not "}" == item.val: - raise ("Definition for commmand %s misses first }., %s" % - (name, item.val)) - self.next() - self.skip_blank_tokens() - return name - - def scan_numargs(self, name): - """ - name is the name of the command or environment definition being - scanned. - Starts on a nonblank token. - Returns numargs - where numargs is the number of arguments in a command or environment - definition, - """ - if not self.legal(): - raise "No numargs to scan." - item = self.item - numargs = 0 - if not simple_ty == item.type: - raise "Illegal command or environment definition: " + name - if "[" == item.val: - if not 4 < len(self.data): - raise "Command or environment definition is illegal: " + name - item = self.next() - if not simple_ty == item.type: - raise "Illegal command or environment definition: " + name - numargs = item.val - if not pos_digit_re.match(numargs): - raise "%s must be argument number after %s" % (numargs, name) - numargs = int(numargs) - self.next() - item = self.skip_blank_tokens() - if not simple_ty == item.type: - raise "Illegal command definition: " + name - if "]" != item.val: - raise "Illegal command definition: " + name - self.next() - self.skip_blank_tokens() - return numargs - - def scan_command_def(self): - """Scan a command definition. - Return command_def. - Assumes that the number of arguments is at most 9. - """ - if not self.legal(): - raise "No command definition to scan." - item = self.item - if not 2 < len(self.data): - raise "Command definition is illegal." - # newcommand or renewcommand - if not item.type in [esc_symb_ty, esc_str_ty]: - raise "Command definition should begin with control sequence: " + item.val - if item.val not in ["newcommand", "renewcommand"]: - raise "Command definition should begin with control sequence." - self.next() - self.skip_blank_tokens() - - cmd_name = self.scan_command_name() - numargs = self.scan_numargs(cmd_name) - - body_group = self.scan_group() - if group_ty != body_group.type: - raise "Command body missing: " + cmd_name - body_val = strip_comments(body_group.val) - return Command_def(cmd_name, numargs, body_val) - - def scan_env_name(self): - """Starts on a {. - Returns name. - """ - if not self.legal(): - raise "No environment name to scan." - item = self.item - if not "{" == item.val: - raise "Env. definition begins with %s, not with {" % (item.val) - self.next() - item = self.skip_blank_tokens() - name = "" - if not simple_ty == item.type: - raise ( - "1. Env. def. begins with cont. seq. %s, not with env.name." % - (item.val)) - while self.uplegal() and not blank_or_rbrace_re.match(item.val): - name += item.val - item = self.next() - if not simple_ty == item.type: - raise ( - "2. Env. def. begins with cont. seq. %s, not with env.name." - % (item.val)) - item = self.skip_blank_tokens() - if not "}" == item.val: - raise "Command definition does not begin with control sequence." - self.next() - self.skip_blank_tokens() - return name - - def scan_env_def(self): - """Scan an environment definition. - Return env_def - Assumes that the number of arguments is at most 9. - """ - if not self.legal(): - raise "No environment definition to scan." - item = self.item - if not 7 < len(self.data): - raise "Environment definition is illegal." - pos = 0 - - if not item.type in [esc_symb_ty, esc_str_ty]: - raise ("Env. definition does not begin with control sequence:" + - item.val) - if item.val not in ["newenvironment", "renewenvironment"]: - raise "Env. definition does not begin with control sequence." - self.next() - self.skip_blank_tokens() - - env_name = self.scan_env_name() - numargs = self.scan_numargs(env_name) - self.skip_blank_tokens() - - begin_group = self.scan_group() - if group_ty != begin_group.type: - raise "Begin body missing: " + env_name - begin_val = strip_comments(begin_group.val) - - self.skip_blank_tokens() - - end_group = self.scan_group() - if group_ty != end_group.type: - raise "End body missing:" + env_name - end_val = strip_comments(end_group.val) - - return Env_def(env_name, numargs, begin_val, end_val) - - def scan_defs(self): - if not self.legal(): - raise "No definitions to scan." - self.reset() - command_defs, env_defs = self.defs - while self.uplegal(): - if (esc_str_ty == self.item.type - and self.item.val in ["newcommand", "renewcommand"]): - command_def = self.scan_command_def() - command_defs[command_def.name] = command_def - elif (esc_str_ty == self.item.type - and self.item.val in ["newenvironment", "renewenvironment"]): - env_def = self.scan_env_def() - env_defs[env_def.name] = env_def - else: - self.next() - - # Instances - - def scan_args(self, command_or_env_def): - """Scan the arguments of a command or environment. - Return [args]. - """ - if not self.legal(): - raise "No arguments to scan." - numargs = command_or_env_def.numargs - name = command_or_env_def.name - - args = [] - for i in range(numargs): - arg = [] - if not (simple_ty == self.item.type and "{" == self.item.val): - arg = [self.item] - self.next() - else: - group = self.scan_group() - arg = group.val - args.append(arg) - return args - - def scan_command(self, command_def): - """Scan the arguments of a command. - Return command_instance - """ - if not self.legal(): - raise "No command to scan." - if not self.item.type in [esc_symb_ty, esc_str_ty]: - raise "Command does not begin with control sequence." - name = self.item.val - self.next() - if 0 < command_def.numargs: - self.skip_blank_tokens() - args = self.scan_args(command_def) - else: - args = [] - return Command_instance(name, args) - - def test_env_boundary(self, item): - """Check whether an environment begin or end follows. - Return 1 if \begin, -1 if \end, 0 otherwise. - """ - d = 0 - if esc_str_ty == item.type: - if "begin" == item.val: - d = 1 - elif "end" == item.val: - d = -1 - return d - - def scan_env_begin(self): - """Scan an environment name. - Return env_name. - """ - if not self.legal(): - raise "No environment begin to scan." - item = self.item - if not (esc_str_ty == item.type and "begin" == item.val): - raise "Environment does not begin with begin." - self.next() - name_group = self.scan_group() - name = detokenize(name_group.val) - return name - - def scan_env_end(self): - """Scan an environment end. - Return env_name. - """ - if not self.legal(): - raise "No environment end to scan." - item = self.item - if not (esc_str_ty == item.type and "end" == item.val): - raise "Environment does not end with end." - self.next() - name_group = self.scan_group() - name = detokenize(name_group.val) - return name - - def scan_env_rest(self, env_def): - """Scanning starts after \begin{envname}. - Returns env_instance. - """ - if not self.legal(): - raise "No environment rest to scan." - count = 1 # We are already within a boundary. - args = self.scan_args(env_def) - body = [] - while count and self.uplegal(): - old_pos = self.pos - d = self.test_env_boundary(self.item) - count += d - if 1 == d: - self.scan_env_begin() - elif -1 == d: - self.scan_env_end() - else: - self.next() - if 0 < count: - body.extend(self.data[old_pos:self.pos]) - return Env_instance(env_def.name, args, body) - - # Definitions - - def restore_defs(self): - if os.path.isfile(self.defs_db_file): - print "Using defs db %s" % (self.defs_db_file) - db_h = shelve.open(self.defs_db) - self.defs = db_h["defs"] - db_h.close() - - def save_defs(self): - db_h = shelve.open(self.defs_db) - if db_h.has_key("defs"): - del db_h["defs"] - db_h["defs"] = self.defs - db_h.close() - - def add_defs(self, defs_file): - defs_file_compl = defs_file + ".sty" - if not os.path.isfile(defs_file_compl): - raise "%s does not exist" % (defs_file_compl) - - defs_db_file = self.defs_db_file - if newer(defs_db_file, defs_file_compl): - print "Using defs db %s for %s" % (defs_db_file, defs_file) - else: - defs_fp = open(defs_file_compl, "r") - defs_str = defs_fp.read() - defs_fp.close() - ds = Tex_stream() - ds.defs = self.defs - defs_text = ds.smart_tokenize(defs_str) - # changing ds.defs will change self.defs - if self.debug: - defs_seen_file = "%s-seen.sty" % (defs_file) - defs_seen_fp = open(defs_seen_file, "w") - out = detokenize(defs_text) - defs_seen_fp.write(out) - defs_seen_fp.close() - ds.scan_defs() - if self.debug: - out = "" - command_defs, env_defs = self.defs - for def_name in command_defs.keys(): - out += command_defs[def_name].show() + "\n" - for def_name in env_defs.keys(): - out += env_defs[def_name].show() + "\n" - print "Definitions after reading %s:" % (defs_file) - print out - - # Applying definitions, recursively - # (maybe not quite in Knuth order, so avoid tricks!) - - def subst_args(self, body, args): - out = [] - pos = 0 - while pos < len(body): - item = body[pos] - if not (simple_ty == item.type and "#" == item.val): - out.append(item) - pos += 1 - continue - pos += 1 - token = body[pos] - argnum = token.val - if not pos_digit_re.match(argnum): - raise "# is not followed by number." - argnum = int(argnum) - if argnum > len(args): - raise "Too large argument number." - arg = args[argnum - 1] - out += arg - pos += 1 - return out - - def apply_command_recur(self, command_instance): - command_defs, env_defs = self.defs - name = command_instance.name - command_def = command_defs[name] - - args = command_instance.args - body = command_def.body - result = self.subst_args(body, args) - try: - result = self.apply_all_recur(result) - except Empty_text_error, e: - raise "apply_all_recur fails on command instance %s: %s, %s" % \ - (command_instance.show(), detokenize(e.data), e.message) - return result - - def apply_env_recur(self, env_instance): - command_defs, env_defs = self.defs - name = env_instance.name - env_def = env_defs[name] - - begin, end = env_def.begin, env_def.end - body, args = env_instance.body, env_instance.args - out = self.subst_args(begin, args) + body + self.subst_args(end, args) - return self.apply_all_recur(out) - - def apply_all_recur(self, data, report=False): - ts = Tex_stream(data) - ts.defs = self.defs - command_defs, env_defs = self.defs - out = [] - progress_step = 10000 - progress = progress_step - if not ts.legal(): - raise Empty_text_error(data, "No text to process.") - while ts.uplegal(): - if self.pos > progress: - if report: - print self.pos - progress += progress_step - if not ts.item.type in [esc_symb_ty, esc_str_ty]: - out.append(ts.item) - ts.next() - continue - if 1 == ts.test_env_boundary(ts.item): - old_pos = ts.pos - env_name = ts.scan_env_begin() - if not env_defs.has_key(env_name): - out.extend(ts.data[old_pos:ts.pos]) - continue - else: - env_def = env_defs[env_name] - env_instance = ts.scan_env_rest(env_def) - result = ts.apply_env_recur(env_instance) - out.extend(result) - elif not command_defs.has_key(ts.item.val): - out.append(ts.item) - ts.next() - continue - else: - command_def = command_defs[ts.item.val] - command_inst = ts.scan_command(command_def) - result = ts.apply_command_recur(command_inst) - out.extend(result) - return out - - # Processing files - - def process_file(self, file): - """Returns the new defs. - """ - file = cut_extension(file, ".tex") - source_file = "%s.tex" % (file) - print "File %s [" % (source_file) - source_fp = open(source_file, "r") - text_str = source_fp.read() - source_fp.close() - - self.smart_tokenize(text_str, handle_inputs=True) - if not self.data: - raise "Empty tokenization result." - self.reset() - - if self.debug: - source_seen_fname = "%s-seen.tex" % (file) - source_seen_fp = open(source_seen_fname, "w") - source_seen_fp.write(detokenize(self.data)) - source_seen_fp.close() - - self.data = self.apply_all_recur(self.data, report=True) - - result_fname = "%s-clean.tex" % (file) - print "Writing %s [" % (result_fname) - result_fp = open(result_fname, "w") - result_fp.write(self.smart_detokenize()) - result_fp.close() - print "] file %s" % (result_fname) - print "] file %s" % (source_file) - - def process_if_newer(self, file): - """ - \input{file} is be added to the token list. - If the input file is newer it is processed. - Returns tokenized \input{file}. - """ - file = cut_extension(file, ".tex") - tex_file = file + ".tex" - clean_tex_file = file + "-clean.tex" - if newer(clean_tex_file, tex_file): - print "Using %s." % (clean_tex_file) - else: - ts = Tex_stream() - ts.data = [] - ts.defs = self.defs - ts.process_file(file) - to_add = "\\input{%s}" % (file) - return tokenize(to_add) - - -# Main - -long_optlist = ["debug", "defs="] -options, restargs = getopt_map("x", long_optlist) - -debug = False -if options.has_key("--debug"): - debug = True - -root = restargs[0] -root = cut_extension(root, ".tex") -if options.has_key("--defs"): - defs_root = options["--defs"] -else: - defs_root = "%s" % (root) -defs_db = defs_root -defs_db_file = defs_root + ".db" - -ts = Tex_stream() -ts.defs_db = defs_db -ts.defs_db_file = defs_db_file -ts.debug = debug - -ts.restore_defs() -for root in restargs: - ts.process_file(root) - -print "(Re)creating defs db %s" % (defs_db) -ts.save_defs() diff --git a/docs/assets/2018_PRACE_MaX/latex/img/convergence_pressure.png b/docs/assets/2018_PRACE_MaX/latex/img/convergence_pressure.png deleted file mode 100644 index a4323e7e..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/img/convergence_pressure.png and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/img/graph/graph-full.pdf b/docs/assets/2018_PRACE_MaX/latex/img/graph/graph-full.pdf deleted file mode 100644 index beeacec3..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/img/graph/graph-full.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/img/graph/graph-inputonly.pdf b/docs/assets/2018_PRACE_MaX/latex/img/graph/graph-inputonly.pdf deleted file mode 100644 index 33088ce6..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/img/graph/graph-inputonly.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/img/graph/graph.svg b/docs/assets/2018_PRACE_MaX/latex/img/graph/graph.svg deleted file mode 100644 index 89b754a4..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/img/graph/graph.svg +++ /dev/null @@ -1,842 +0,0 @@ - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - - - - - - - retrieved - - - remote_folder - - output_parameters - ParameterData - FolderData - RemoteData - energy=-8eVforces=[...]... - aiida.outdata-file.xml... - /scratch/run/a3/2b/2d62@cluster.uni.edu - - - - - - output_parameters - Additionalparsednodes - - - - - ... - - - StructureData - ParameterData - KpointsData - Code - UpfData - - - - - - - - - - - - - - - PwCalculation - - code - - structure - - - parameters - - - kpoints - - pseudo_Ba - - pseudo_Ti - - pseudo_O - Ba-lda.UPF - Ti-lda.UPF - O-lda.UPF - 3x3x3 - BaTiO3alat=4 Å - cutoff=50Ry... - /home/.../pw.x@cluster.uni.edu - - - diff --git a/docs/assets/2018_PRACE_MaX/latex/img/magnetization_smearing_perovskites.pdf b/docs/assets/2018_PRACE_MaX/latex/img/magnetization_smearing_perovskites.pdf deleted file mode 100644 index 73196a87..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/img/magnetization_smearing_perovskites.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/img/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps b/docs/assets/2018_PRACE_MaX/latex/img/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps deleted file mode 100644 index 6e8fd379..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/img/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps +++ /dev/null @@ -1,6953 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Title: /home/mounet/Documents/PYTHON/AiiDA_scripts/my_first_scripts/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps -%%Creator: matplotlib version 1.1.1rc, http://matplotlib.sourceforge.net/ -%%CreationDate: Wed Oct 29 08:48:08 2014 -%%Orientation: portrait -%%BoundingBox: -528 -63 1140 855 -%%EndComments -%%BeginProlog -/mpldict 8 dict def -mpldict begin -/m { moveto } bind def -/l { lineto } bind def -/r { rlineto } bind def -/c { curveto } bind def -/cl { closepath } bind def -/box { -m -1 index 0 r -0 exch r -neg 0 r -cl -} bind def -/clipbox { -box -clip -newpath -} bind def -%!PS-Adobe-3.0 Resource-Font -%%Title: DejaVu Sans -%%Copyright: Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. DejaVu changes are in public domain -%%Creator: Converted from TrueType to type 3 by PPR -25 dict begin -/_d{bind def}bind def -/_m{moveto}_d -/_l{lineto}_d -/_cl{closepath eofill}_d -/_c{curveto}_d -/_sc{7 -1 roll{setcachedevice}{pop pop pop pop pop pop}ifelse}_d -/_e{exec}_d -/FontName /DejaVuSans def -/PaintType 0 def -/FontMatrix[.001 0 0 .001 0 0]def -/FontBBox[-1021 -415 1681 1167]def -/FontType 3 def -/Encoding [ /space /period /slash /zero /one /two /three /four /five /A /B /C /F /G /H /K /L /M /N /O /P /R /S /T /Y /Z /bracketleft /bracketright /underscore /a /b /c /d /e /f /g /h /i /l /m /n /o /p /r /s /t /u /v /z ] def -/FontInfo 10 dict dup begin -/FamilyName (DejaVu Sans) def -/FullName (DejaVu Sans) def -/Notice (Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. 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-1,44 +0,0 @@ -#!/usr/bin/env python -""" -Filters for conversion from LaTeX to MarkDown. - - * handle bash/python code blocks -""" -from __future__ import absolute_import -from pandocfilters import toJSONFilter, CodeBlock - - -def log(msg): - """Log message to file.""" - with open('/tmp/dump', 'w') as f: - f.write(msg) - - -def code(key, value, format, meta): # pylint: disable=unused-argument,redefined-builtin - """Handle python/console code blocks.""" - if key == "CodeBlock": - properties = value[0] - attributes = properties[2] - - # this signifies python codeblock - if ['frame', 'leftline'] in attributes: - # set 'python' class - properties[1] = ['python'] - else: - # set class - # - for markdown: use 'terminal' - # - for rst: use 'console' - properties[1] = ['console'] - - # unset attrs - properties[2] = [] - - value[0] = properties - - #log(str(value)) - - return CodeBlock(*value) - - -if __name__ == "__main__": - toJSONFilter(code) diff --git a/docs/assets/2018_PRACE_MaX/latex/requirements.txt b/docs/assets/2018_PRACE_MaX/latex/requirements.txt deleted file mode 100644 index 55260b13..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pandocfilters diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/1_1_ssh_connection.tex b/docs/assets/2018_PRACE_MaX/latex/sections/1_1_ssh_connection.tex deleted file mode 100644 index b526c100..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/1_1_ssh_connection.tex +++ /dev/null @@ -1,74 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -%\newpage -\subsection{Instructions to SSH to the Amazon EC2 instance} -\label{sec:sshintro} -You should have received an IP address from the instructors, and two files with a private and a public -SSH key (\verb|aiida_tutorial_NUM| and -\verb|aiida_tutorial_NUM.pub|), where \verb|NUM| is an -integer. -These allow you to connect to an Amazon EC2 instance (different for -each participant of the tutorial). To connect via \texttt{ssh} to this machine follow the steps below, depending on the computer you have. - -\textbf{Note!} \emph{If you decide to work in pairs, one of the two people should discard his email. The other person should forward his email to the colleague, and both should then use the same virtual machine IP and account (ssh key). In this case, you will be both using the same account, so be careful not to delete the work of your colleague.} - -\subsubsection*{Linux and Mac} -\begin{itemize} -% \item If you are using your own laptop, skip this point and go -% directly to the next one. If your are using one the classroom -% computer, login into one of the machines using your ETHZ -% account. If you are not from ETHZ you should have received a -% username and password to connect first to -% \texttt{login.phys.ethz.ch} (please do not forget to change this -% password). - \item If needed, create a \texttt{.ssh} directory in your home (\verb|mkdir ~/.ssh|), and set its permissions: - \\ \verb|chmod 700 ~/.ssh| - \item Copy in this \texttt{.ssh} directory the two files - \verb|aiida_tutorial_NUM| and\\ - \verb|aiida_tutorial_NUM.pub| - \item Set the correct permissions on the private key: \\ - \verb|chmod 600 ~/.ssh/aiida_tutorial_NUM| (then check with \verb|ls -l| that the permissions of this file are now \verb|-rw-------|). - \item Create (or modify if it already exists) the \texttt{config} file in your \texttt{.ssh} directory, adding the following lines: -\begin{verbatim} -Host aiidatutorial - Hostname IP_ADDRESS - User aiida - IdentityFile ~/.ssh/aiida_tutorial_NUM - LocalForward 8888 localhost:8888 -\end{verbatim} -where you have to replace \verb|IP_ADDRESS| with the IP address -provided to you. - \item You can then \texttt{ssh} to the Amazon EC2 instance from the - terminal, using simply - \begin{verbatim} - ssh -X -C aiidatutorial - \end{verbatim} - (connecting with \verb|-X| --- note that sometimes \verb|-Y| is needed - instead --- - will allow you to run graphical programs such as xmgrace or - gnuplot interactively, even if they might not be very responsive as the - Amazon virtual machines are in Ireland). -\end{itemize} - -\subsubsection*{Windows} -\begin{itemize} -\item Install PuTTY. -\item Run PuTTYGen, load the \verb|aiida_tutorial_NN| private key - (button \verb|"Load"|). remember to choose to show ``All files - (*.*)'' in the window, and select the file without any extension - (Type: File). -\item In the same window, click on ``Save private Key'', and save the - key with the name\\ \verb|aiida_tutorial_NN.ppk|. -\item Run Pageant: it will add a new icon near the clock, in the - bottom right of your screen. -\item Right click on this Pageant icon, and click on ``View Keys''. -\item Click on \verb|"Add key"| and select the - \verb|aiida_tutorial_NN.ppk| you saved a few steps above. -\item Run PuTTY, put the given IP address as hostname. Write \verb|aiidatutorial| in Saved Sessions and click \verb|Save|. Go to Connection $\to$ Data and put \verb|aiida| as autologin username. Under Connection, go to SSH $\to$ Tunnels, type \texttt{8888} in the \texttt{Source Port} box and \texttt{localhost:8888} in \texttt{Destination} and click \verb|Add|. Click on \verb|Save| again on the Session screen. - -\item Now select \verb|aiidatutorial| from the session list, click \verb|Load| and, finally, \verb|open|. - -\end{itemize} - -\subsection*{Everybody: connect to the machine and start jupyter} - -Before starting the tutorial, connect via SSH to the Amazon machine as explained above (the Amazon machine already contains a pre-configured AiiDA installation and some test data for this tutorial). diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/1_2_preparation.tex b/docs/assets/2018_PRACE_MaX/latex/sections/1_2_preparation.tex deleted file mode 100644 index 69636bca..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/1_2_preparation.tex +++ /dev/null @@ -1,35 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -\subsection*{Before starting} - -Once connected to your machine, type in the remote terminal -\begin{bashcommand} - workon aiida -\end{bashcommand} -This will enable the virtual environment in which AiiDA is installed, allowing you to use AiiDA. %You will need to type this command at every new connection you open to the Amazon machine. - -Now type in the same terminal -\begin{bashcommand} - jupyter notebook --no-browser -\end{bashcommand} -This will run a server with a web application called \texttt{jupyter}, which is used to create interactive python notebooks. To connect to this application, copy the URL that has been printed to the terminal (it will be something like \texttt{http://localhost:8888/?token=2a3ba37cd1...}) and paste it into the URL bar of a web browser. You will see a list of folders: these are folders on the remote Amazon computer. -We will use \texttt{jupyter} in section \ref{sec:querybuilder} and optionally in other sections as well. - -Now launch an identical \cmd{ssh} connection (again, as explained above) in another terminal, and type \texttt{workon aiida} here too. This terminal is the one you will actually use in this tutorial. - -Note: Since the port listening is set to a specific port (8888) in the section \ref{sec:sshintro}, you have to make sure on the server the Jupiter notebook is running on the port 8888. Otherwise, use an alternative port for listening. - -% \textbf{We suggest to open two terminals with such an \cmd{ssh} connection}: - -A final note: for details on AiiDA that may not be fully explained here, you can refer to the full AiiDA documentation, available online at \url{http://aiida-core.readthedocs.io/en/latest/}. - -\subsection*{Troubleshooting tips (in case you have issues later)} -% Should some of these also be in the online version? -\begin{itemize} -\item If you get an error like \texttt{ImportError: No module named aiida} or \texttt{No command 'verdi' found} double check that you have loaded the virtual environment with \texttt{workon aiida} before launching python, ipython or the jupyter server. -\item If your browser cannot connect to the jupyter instance, check that you have correctly configured SSH tunneling/forwarding as described above. Also note that you should run the jupyter server from the terminal connected to the Amazon machine, while the web browser should be opened locally on your laptop or worstation. -\item The Jupyter Notebook officially supports the latest stable versions of Chrome, Safari and Firefox. See \url{http://jupyter-notebook.readthedocs.io/en/4.x/notebook.html#browser-compatibility} for more information on broswer compatibility (and update your browser if it is too old). -% Marco: tested and working on Firefox 53.0.2 -% Change this link if we switch to jupyter 5 or later -\end{itemize} - - diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/2_verdi_graphs.tex b/docs/assets/2018_PRACE_MaX/latex/sections/2_verdi_graphs.tex deleted file mode 100644 index 4570389c..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/2_verdi_graphs.tex +++ /dev/null @@ -1,268 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -\section[Verdi command line]{Using the verdi command line} - -This part of the tutorial will help to familiarize you with the command-line utility \cmd{verdi}, one of the most common ways to interact with AiiDA. \cmd{verdi} with its subcommands enables a variety of operations such as inspecting the status of ongoing or terminated calculations, showing the details of calculations, computers, codes, or data structures, access the input and the output of a calculation, etc. Similar to the \texttt{bash} shell, verdi command support Tab completion. Try right now to type \cmd{verdi}, followed by a space, in a terminal and tap Tab twice to have a list of subcommands. Whenever you need the explanation of a command type \cmd{verdi help} or add \cmd{-h} flag if you are using any of the \cmd{verdi} subcommands. -Finally, fields enclosed in angular brackets, such as \texttt{}, are placeholders to be replaced by the actual value of that field (an integer, a string, etc...). - -\subsection{The list of calculations} -Let us try our first \cmd{verdi} commands. Type in the terminal -\begin{bashcommand} -verdi calculation list -\end{bashcommand} -(Note: the first time you run this command, it might take a few seconds as it is the first time you are accessing the database in the virtual machine. Subsequent calls will be faster). -This will print the list of ongoing calculations, which should be empty. The first output line should look like -\begin{verbatim} -PK Creation State Type Computer Job state ----- ---------- ------- ------ ---------- ----------- - -Total results: 0 - -Info: last time an entry changed state: never -\end{verbatim} - -In order to print a list with all calculations that finished correctly in the AiiDA database, you can use the \cmd{-s/-{}-states} flag as follows: -\begin{bashcommand} -verdi calculation list --states FINISHED -\end{bashcommand} -Another very typical option combination allows to get calculations in \emph{any} state (flag \cmd{-a}) generated in the past \cmd{NUM} days (\cmd{-p }): e.g., for calculation in the past 1 day: \cmd{verdi calculation list -p1 -a}. Since you have not yet run any calculations at the virtual machine that you currently use and all the existing calculations were imported and belong to a different user, you can type (flag \cmd{-A} shows the calculations of all the users): -\begin{bashcommand} -verdi calculation list -A --states IMPORTED -\end{bashcommand} - -Each row of the output identifies a calculation and shows some information about it. For a more detailed list of properties, choose one row by noting down its PK (primary key) number (first column of the output) and type in the terminal -\begin{bashcommand} -verdi calculation show -\end{bashcommand} -The output depends on the specific pk chosen and should inform you about the input nodes (e.g. pseudopotentials, kpoints, initial structure, etc.), and output nodes (e.g. output structure, output parameters, etc.). - -\begin{tcolorbox} -\textbf{PKs/IDs vs.\@ UUIDs}: Beside the (integer) PK, very convenient to reference a calculation or data node -in your database, every node has a UUID (Universally Unique ID) to identify it, that is preserved even when you share some nodes with coworkers---while the PK -will most likely change. You can see the UUID in the output of \texttt{verdi calculation show} or \texttt{verdi node show}. Moreover, if you have already a UUID and you want -to get the corresponding PK in your database, you can use \texttt{verdi node show -u }, -as we are going to do now. -\end{tcolorbox} - -Let us now consider the node with \texttt{UUID = ce81c420-7751-48f6-af8e-eb7c6a30cec3}, which identifies a relaxation of a BaTiO$_3$ unit cell run with Quantum Espresso \cmd{pw.x}. -You can check the information on this node and get the PK with: -\begin{verbatim} -$ verdi node show -u ce81c420-7751-48f6-af8e-eb7c6a30cec3 -Property Value -------------- ------------------------------------ -type PwCalculation -pk 4235 -uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 -label -description -ctime 2014-10-27 17:51:21.781045+00:00 -mtime 2018-05-16 11:19:39.848446+00:00 -process state -finish status -computer [2] daint -code pw-SVN-piz-daint - -Inputs PK Type ----------- ---- ------------- -parameters 4236 ParameterData -kpoints 4526 KpointsData -pseudo_Ba 966 UpfData -pseudo_Ti 4315 UpfData -settings 4529 ParameterData -pseudo_O 4342 UpfData -structure 436 StructureData - -Outputs PK Type ------------------------ ---- ------------- -output_kpoints 3665 KpointsData -output_parameters 3670 ParameterData -output_structure 3666 StructureData -retrieved 3668 FolderData -output_trajectory_array 265 ArrayData -remote_folder 1977 RemoteData -\end{verbatim} -\emph{Keep in mind that you can also use just a part (beginning) of the UUID, as long as it is unique, to show the node information information.} For example, to display the above information, you could also type \cmd{verdi node show -u ce81c420}. In what follows, we are going to mention only the prefixes of the UUIDs since they are sufficient to identify the correct node. - -\subsection{A typical AiiDA graph} -\label{sec:aiida_graph} -AiiDA stores inputs required by a calculation as well as the its outputs in the database. These objects are connected in a graph that looks like Fig.~\ref{fig:graph}. We suggest that you have a look to the figure before going ahead. - -\begin{figure} -\centering -\includegraphics[width=0.5\textwidth]{img/graph/graph-inputonly} - -\vspace {1cm} - -\includegraphics[width=0.5\textwidth]{img/graph/graph-full} -\caption{\label{fig:graph}\textbf{(a, top)} Graph with all inputs (data, circles; and code, diamond) to the Quantum Espresso calculation (square) that you will create in Sec.~\ref{sec:qe} of this tutorial. \textbf{(b, bottom)} Same as (a), but also with the outputs that the daemon will create and connect automatically. The RemoteData node is created during submission and can be thought as a symbolic link to the remote folder in which the calculation runs on the cluster. The other nodes are created when the calculation has finished, after retrieval and parsing. The node with linkname ``retrieved'' contains the raw output files stored in the AiiDA repository; all other nodes are added by the parser. Additional nodes (symbolized in gray) can be added by the parser (e.g., an output StructureData if you performed a relaxation calculation, a TrajectoryData for molecular dynamics, \ldots).} -\end{figure} - -You can create a similar graph for any calculation node by using the utility \cmd{verdi graph generate }. For example, before you obtained information (in text form) for \texttt{UUID = ce81c420}. To visualize similar information in graph(ical) form, run (replacing \cmd{} with the PK of the node): -\begin{bashcommand} -verdi graph generate -\end{bashcommand} - -This command will create the file \texttt{.dot} that can be rendered by means of the utility \cmd{dot}. If you now type -\begin{bashcommand} -dot -Tpdf -o .pdf .dot -\end{bashcommand} -you will create a pdf file \texttt{.pdf}. You can open this file on the Amazon machine by using \cmd{evince} or, if you feel that the ssh connection is too slow, copy it via \cmd{scp} to your local machine. -To do so, if you are using Linux/Mac OS X, you can type in your \emph{local} machine: -\begin{bashcommand} -scp aiidatutorial: -\end{bashcommand} -and then open the file. Alternatively, you can use graphical software to achieve the same, for instance: WinSCP on Windows, Cyberduck on the Mac, or the ``Connect to server'' option in the main menu after clicking on the desktop for Ubuntu. - -Spend some time to familiarize yourself with the graph structure. Choose the root node (highlighted in blue) and trace back the parent calculation which produced the structure used as an input. This is an example of a Quantum ESPRESSO pw.x calculation, where the input structure was actually obtained as the output of a previous calculation. We will now inspect the different elements of this graph. - -\subsection{Inspecting the nodes of a graph} -\subsubsection*{ParameterData and Calculations} -Now, let us have a closer look at the some of the nodes appearing in the graph. -Choose the node of the type \texttt{ParameterData} with input link name \texttt{parameters} -(to double check, it should have UUID \texttt{d1bbe1ea}) -and type in the terminal: -\begin{bashcommand} -verdi data parameter show -\end{bashcommand} -A \texttt{ParameterData} contains a dictionary (i.e., key--value pairs), stored in the database in a format ready to be queried (we will learn how to run queries later on in this tutorial). The command above will print the content dictionary, containing the parameters used to define the input file for the calculation. You can compare the dictionary with the content of the raw input file to Quantum ESPRESSO (that was generated by AiiDA) via the command -\begin{bashcommand} -verdi calculation inputcat -\end{bashcommand} -where you substitute the pk of the calculation node. -Check the consistency of the parameters written in the input file and those stored in the ParameterData node. Even if you don't know the meaning of the input flags of a Quantum ESPRESSO calculation, you should be able to see how the input dictionary -has been converted to Fortran namelists. - -The previous command just printed the content of the ``default'' input file \texttt{aiida.in}. -To see a list of all the files used to run a calculation (input file, submission script, etc.) instead type -\begin{bashcommand} -verdi calculation inputls -\end{bashcommand} -(Adding a \texttt{-{}-color} flag allows you to easily distinguish files from folders by a different coloring). - -Once you know the name of the file you want to visualize, you can call the \cmd{verdi -calculation inputcat} command specifying the path. For instance, to see the submission -script, you can do: -\begin{bashcommand} -verdi calculation inputcat -p _aiidasubmit.sh -\end{bashcommand} - -%%%%%%%%%%%%%%%%%%%%%%Structure data%%%%%%%%%%%%%%%%%%% -\subsubsection*{StructureData} -Now let us focus on StructureData objects, representing a crystal structure. -We can consider for instance the input structure to the calculation we were -considering before (it should have UUID prefix \texttt{3a4b1270}). -Such objects can be inspected interactively by means of an atomic viewer such as the one provided by \texttt{ase}. AiiDA however supports several other viewers such as \texttt{xcrysden}, \texttt{jmol}, and \texttt{vmd}. Type in the terminal -\begin{bashcommand} -verdi data structure show --format ase -\end{bashcommand} -to show the selected structure (it will take a few seconds to appear, and you can rotate the view with the right mouse button---if -you receive some errors, make sure you started your SSH connection with the -\texttt{-X} or \texttt{-Y} flag). - -Alternatively, especially if showing them interactively is too slow over SSH, you can export the content of a structure node in various popular formats such as \texttt{xyz} or \texttt{xsf}. This is achieved by typing in the terminal -\begin{bashcommand} -verdi data structure export --format xsf > .xsf -\end{bashcommand} -You can open the generated \texttt{xsf} file and observe the cell and the coordinates. Then, you can then copy \texttt{.xsf} from the Amazon machine to your local one and then visualize it, e.g. with xcrysden (if you have it installed): -\begin{bashcommand} -xcrysden --xsf .xsf -\end{bashcommand} - -%%%%%%%%Verdi code%%%%%%%%%%%% -\subsubsection*{Codes and computers} -Let us focus now on the nodes of type \texttt{code}. A code represents (in the database) the actual executable used to run the calculation. Find the pk of such a node in the graph and type -\begin{bashcommand} -verdi code show -\end{bashcommand} - -The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc. To show a list of all available codes type -\begin{bashcommand} -verdi code list -\end{bashcommand} -If you want to show all codes, including hidden ones and those created by other users, use \texttt{verdi code list -a -A}. Now, among the entries of the output you should also find the code just shown. - -%%%%%%%%%%%Verdi computer%%%%%%%%%% -Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command -\begin{bashcommand} -verdi computer list -a -\end{bashcommand} -(\cmd{-a} shows all computers, also the one imported in your database but that you did not configure, i.e., to which you don't have access). -Details about each computer can be obtained by the command -\begin{bashcommand} -verdi computer show -\end{bashcommand} -Now you have the tools to answer the question: -\begin{tcolorbox} -What is the scheduler installed on the computer where the calculations of the graph have run? -\end{tcolorbox} - - -%%%%%%%%%%%%%%%verdi output|res%%%%%%%%%%%%%%%% -\subsubsection*{Calculation results} -The results of a calculation can be accessed directly from the calculation node. Type in the terminal -\begin{bashcommand} -verdi calculation res -\end{bashcommand} -which will print the output dictionary of the ``scalar'' results parsed by AiiDA at the end of the calculation. Note that this is actually a shortcut for -\begin{bashcommand} -verdi data parameter show -\end{bashcommand} -where \texttt{pk2} refers to the ParameterData node attached as an output of the calculation node, with link name \texttt{output\_parameters}. - -\begin{tcolorbox} -By looking at the output of the command, what is the Fermi energy of the calculation with UUID prefix \texttt{ce81c420}? -\end{tcolorbox} - - - -%%%%%%%%%%%%verdi output cat%%%%%%%%%%% -Similarly to what you did for the calculation inputs, you can access the output files via the commands -\begin{bashcommand} -verdi calculation outputls -\end{bashcommand} -and -\begin{bashcommand} -verdi calculation outputcat -\end{bashcommand} -Use the latter to verify that the Fermi energy that you have found in the last step has been extracted correctly from the output file (Hint: filter the lines containing the string ``Fermi'', e.g. using \texttt{grep}, to isolate the relevant lines). - -%%%%%%%%%%%verdi data array show%%%%%%%%%%%% -The results of calculations are stored in two ways: \texttt{ParameterData} objects are stored in the database, which makes querying them very convenient, whereas \texttt{ArrayData} objects are stored on the disk. Once more, use the command \cmd{verdi data array show } to know the Fermi energy obtained from calculation with UUID prefix \texttt{ce81c420} (you need to use, this time, the PK of the output ArrayData of the calculation, with link name \cmd{output\_trajectory\_array}). As you might have realized the difference now is that the whole series of values of the Fermi energy calculated after each relax/vc-relax step are stored. (The choice of what to store in \texttt{ParameterData} and \texttt{ArrayData} nodes is made by the parser of \texttt{pw.x} implemented in the \texttt{aiida-quantumespresso} plugin.) - -%%%%%%%%%%%verdi comments%%%%%%%%%%%%%%5 -\subsubsection*{(Optional section) Comments} -AiiDA offers the possibility to attach comments to a calculation node, in order to be able to remember more easily its details. Node with UUID prefix ce81c420 has no comment already defined, but you can add a very instructive one by typing in the terminal -%%%%%%%%%%verdi comment%%%%%%%%%%%%%% -\begin{bashcommand} -verdi comment add -c "vc-relax of a BaTiO3 done with QE pw.x" -\end{bashcommand} -Now, if you ask for a list of all comments associated to that calculation by typing -\begin{bashcommand} -verdi comment show -\end{bashcommand} -the comment that you just added will appear together with some useful information such as its creator and creation date. We let you play with the other options of \cmd{verdi comment} command to learn how to update or remove comments. - - -% %%%%%%%%%%TO BE DISCUSSED%%%%%%%%%%%%%%%%%% -% \textcolor{red}{We might discuss the \cmd{verdi node repo ls|cat command} e.g.} -% \begin{bashcommand} -% verdi node repo cat -p 4079 raw_input/aiida.in -% \end{bashcommand} -% \textcolor{red}{Moreover, at this state no RemoteData concept has been introduced} - - -\subsection{AiiDA groups of calculations} -In AiiDA, calculations (and more generally nodes) can be organized in groups, which are particularly useful to assign a set of calculations or data to a common project. This allows you to have quick access to a whole set of calculations with no need for tedious browsing of the database or writing complex scripts for retrieving the desired nodes. Type in the terminal -\begin{bashcommand} -verdi group list -\end{bashcommand} -to show a list of the groups that already exist in the database. Choose the PK of the group named \texttt{tutorial\_pbesol} and look at the calculations that it contains by typing -\begin{bashcommand} -verdi group show -\end{bashcommand} -In this case, we have used the name of the group to organize calculations according to the pseudopotential that has been used to perform them. Among the rows printed by the last command you will be able to find the calculation we have been inspecting until now. - -If, instead, you want to know all the groups to which a specific node belomngs, you can run -\begin{bashcommand} -verdi group list --node -\end{bashcommand} - diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/3_verdi_shell_and_orm.tex b/docs/assets/2018_PRACE_MaX/latex/sections/3_verdi_shell_and_orm.tex deleted file mode 100644 index 53682bec..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/3_verdi_shell_and_orm.tex +++ /dev/null @@ -1,329 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -\section[Verdi shell and AiiDA objects]{Using the verdi shell and familiarizing -with AiiDA objects\label{shell}} - -In this section we will use an interactive ipython environment with all the basic AiiDA classes -already loaded. We propose two realizations of such a tool. The first consist of a special ipython shell where all the AiiDA classes, methods and functions are accessible. -Type in the terminal -%Open a second terminal, connect to the Amazon machine with \cmd{ssh} and type: -\begin{bashcommand} - verdi shell -\end{bashcommand} -For all the everyday AiiDA-based operations, i.e. creating, -querying and using AiiDA objects, the \cmd{verdi shell} is probably the best tool. -In this case, we suggest that you use two terminals, one for the \cmd{verdi shell} and one to execute bash commands. - -The second option is based on \texttt{jupyter} notebooks and is probably most suitable to the purposes of our tutorial. Go to the browser where you have opened \texttt{jupyter} and click \texttt{New} $\to$ \texttt{Python 2} (top right corner). This will open an ipython notebook based on cells where you can type portions of python code. The code will not be executed until you press \verb|Shift+Enter| from within a cell. -Type in the first cell -\begin{pythoncommand} - %aiida -\end{pythoncommand} -and execute it. This will set exactly the same environment as the \cmd{verdi shell}. -The notebook will be automatically saved upon any modification and when you think you are done, you can export your notebook in many formats by going to \verb|File| $\to$ \verb|Download as|. We suggest you to have a look to the drop-down menus \texttt{Insert} and \texttt{Cell} where you will find the main commands to manage the cells of your notebook. -\textbf{The \cmd{verdi shell} and the \cmd{jupyter} notebook are completely equivalent. Use either according to your personal preference.} - -Note: you will still need sometimes to type command-line -instructions in \cmd{bash} in the first terminal you opened today. -To differentiate these from the commands to be typed in the -\cmd{verdi shell}, the latter will be marked in this document by a vertical line -on the left, like: -\begin{pythoncommand} - some verdi shell command -\end{pythoncommand} -while command-line instructions in \cmd{bash} to be typed on a terminal will -be encapsulated between horizontal lines: -\begin{bashcommand} - some bash command -\end{bashcommand} -Alternatively, to avoid changing terminal, you can execute \cmd{bash} commands within the \cmd{verdi shell} or the notebook adding an exclamation mark before the command itself -\begin{pythoncommand} - !some bash command -\end{pythoncommand} - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% Load node and .res method -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -\subsection[load_node]{Loading a node\label{load_node}} - -Most AiiDA objects are represented by nodes, identified in the database by its pk number (an integer). -You can access a node using the following command in the shell: -\begin{pythoncommand} - node = load_node(PK) -\end{pythoncommand} -Load a node using one of the calculation pks visible in the graph you displayed -in the previous section of the tutorial. Then get the energy of the -calculation with the command -\begin{pythoncommand} - node.res.energy -\end{pythoncommand} -You can also type -\begin{pythoncommand} - node.res. -\end{pythoncommand} -and then press \cmd{TAB} to see all the possible output -results of the calculation. - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% Loading several kinds of nodes -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -\subsection{Loading different kinds of nodes} - -\subsubsection{Pseudopotentials} - -From the graph displayed in Section~\ref{sec:aiida_graph}, find the pk of the barium pseudopotential file (LDA). Load it and verify that it describes barium. Type -\begin{pythoncommand} - upf = load_node(PK) - upf.element -\end{pythoncommand} -All methods of \texttt{UpfData} are accessible by typing \texttt{upf.} and then pressing \cmd{TAB}. - -\subsubsection{k-points} - -A set of k-points in the Brillouin zone is represented by an instance of the -\texttt{KpointsData} class. Choose one from the graph of Section~\ref{sec:aiida_graph}, -load it as \texttt{kpoints} and inspect its content: -% kpoints = load_node(PK) -\begin{pythoncommand} - kpoints.get_kpoints_mesh() -\end{pythoncommand} -Then get the full (explicit) list of k-points belonging to this mesh using -\begin{pythoncommand} - kpoints.get_kpoints_mesh(print_list=True) -\end{pythoncommand} -If you incurred in a \texttt{AttributeError}, it means that the kpoints instance does not -represent a regular mesh but rather a list of -k-points defined by their crystal coordinates (typically used when plotting a band structure). -In this case, get the list of k-points coordinates using -\begin{pythoncommand} - kpoints.get_kpoints() -\end{pythoncommand} -If you prefer Cartesian (rather than crystal) coordinates, type -\begin{pythoncommand} - kpoints.get_kpoints(cartesian=True) -\end{pythoncommand} - -%%%%%%%%%K points%%%%%%%%%%5 -%\subsubsection{K-points definition} -For later use in this tutorial, let us try now to create a kpoints instance, to -describe a regular $2\times2\times2$ mesh of k-points, centered at the Gamma point (i.e. without offset). -This can be done with the following commands: -\begin{pythoncommand} - from aiida.orm.data.array.kpoints import KpointsData - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh,kpoints_mesh,kpoints_mesh]) - kpoints.store() -\end{pythoncommand} - -The import performed in the first line is however unpractical as it requires to remember the exact location of the module containing the KpointsData class. Instead, it is easier to use the \cmd{DataFactory} function instead of an explicit import. - -\begin{pythoncommand} - KpointsData = DataFactory("array.kpoints") -\end{pythoncommand} - -This function loads the appropriate class defined in a string (here \texttt{array.kpoints}).\footnote{The string provided to the \cmd{DataFactory} encodes both the location and the name of the required class according to some specific rules.} -Therefore, \texttt{KpointsData} is not a class instance, but the kpoints class itself! - - -\subsubsection{Parameters} - -Nested dictionaries with individual parameters, as well as lists and arrays, are -represented in AiiDA with \texttt{Parameter\-Data} objects. Get the PK and load -the input parameters of a calculation in the graph of Section~\ref{sec:aiida_graph}. -Then display its content by typing -\begin{pythoncommand} - params.get_dict() -\end{pythoncommand} -where \texttt{params} is the \texttt{ParameterData} node you loaded. %You can -%also access directly the first level of dictionary keys by pressing \cmd{TAB} -%after having typed -%\begin{pythoncommand} -% params.dict. -%\end{pythoncommand} -Modify the dictionary content so that the wave-function cutoff is now set to 20 Ry. -Note that you cannot modify an object already stored in the database. -To save the modification, you must create a new ParameterData object. Similarly to what discussed before, first load the \texttt{ParameterData} class by typing -\begin{pythoncommand} - ParameterData = DataFactory('parameter') -\end{pythoncommand} -Then an instance of the class (i.e. the parameter object -that we want to create) is created and initialized by the command -\begin{pythoncommand} - new_params = ParameterData(dict=YOUR_DICT) -\end{pythoncommand} -where \texttt{YOUR\_DICT} is the modified dictionary. -Note that the parameter object is not yet stored in the database. -In fact, if you simply type \texttt{new\_params} in the verdi shell, you will be prompted with a string notifying you the ``unstored'' status. -To save an entry in the database corresponding to the \texttt{new\_params} object, you need to type a last command in the verdi shell: -\begin{pythoncommand} - new_params.store() -\end{pythoncommand} - -\subsubsection{Structures} - -Find a structure in the graph of Section~\ref{sec:aiida_graph} and load it. Display -its chemical formula, atomic positions and species using -\begin{pythoncommand} - structure.get_formula() - structure.sites -\end{pythoncommand} -where \texttt{structure} is the structure you loaded. If you are familiar with ASE and PYMATGEN, -you can convert this structure to those formats by typing -\begin{pythoncommand} - structure.get_ase() - structure.get_pymatgen() -\end{pythoncommand} -%You then have access to all the ASE/PYMATGEN methods. -Let's try now to define a new structure to study, specifically a silicon -crystal. -In the \cmd{verdi shell}, define a cubic unit cell as a $3\times3$ matrix, with lattice parameter $a_{lat}=5.4$ \AA: -\begin{pythoncommand} - alat = 5.4 - the_cell = [[alat/2,alat/2,0.],[alat/2,0.,alat/2],[0.,alat/2,alat/2]] -\end{pythoncommand} - -{\bf Note}: Default units for crystal structure cell and coordinates in AiiDA are \AA. - -Structures in AiiDA are instances of \texttt{StructureData} class: load it in the verdi shell -\begin{pythoncommand} - StructureData = DataFactory("structure") -\end{pythoncommand} -Now, initialize the class instance (i.e.\ is the structure we want to study) by the command -\begin{pythoncommand} - structure = StructureData(cell=the_cell) -\end{pythoncommand} -which sets the cubic cell defined before. From now on, you can access the cell with the command -\begin{pythoncommand} - structure.cell -\end{pythoncommand} -Finally, append each of the 2 atoms of the cell command. You can do it -using commands like -\begin{pythoncommand} - structure.append_atom(position=(alat/4.,alat/4.,alat/4.),symbols="Si") -\end{pythoncommand} -for the first `Si' atom. Repeat it for the other atomic site $\left(0,0,0\right)$. -You can access and inspect\footnote{if you set the structure incorrectly, for example with overlapping atoms, it is very likely that any DFT code will fail!} the structure sites with the command -\begin{pythoncommand} - structure.sites -\end{pythoncommand} -If you make a mistake, start over from \texttt{structure = StructureData(cell=the\_cell)}, or equivalently use \\ -\texttt{structure.clear\_kinds()} to remove all kinds (atomic species) and sites. -Alternatively, AiiDA structures can also be -converted directly from ASE~\cite{ref:ASE} structures using\footnote{We purposefully do not provide advanced commands -for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks -already exist (such as ASE or pymatgen).} -\begin{pythoncommand} - from ase.lattice.spacegroup import crystal - ase_structure = crystal('Si', [(0,0,0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90],primitive_cell=True) - structure=StructureData(ase=ase_structure) -\end{pythoncommand} -% -Now you can store the new structure object in the database with the command: -\begin{pythoncommand} - structure.store() -\end{pythoncommand} -% -% -Finally, we can also import the silicon structure from an external (online) repository -such as the Crystallography Open Database~\cite{ref:COD}: -\begin{pythoncommand} -from aiida.tools.dbimporters.plugins.cod import CodDbImporter -importer = CodDbImporter() -for entry in importer.query(formula='Si',spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print "Formula", structure.get_formula() - print "Unit cell volume: ", structure.get_cell_volume() -\end{pythoncommand} -In that case two duplicate structures are found for Si. -%\footnote{Note: one could compare these two structures with the pymatgen tool -%StructureMatcher} - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% .inp / .out methods -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -\subsection{Accessing inputs and outputs} - -Load again the calculation node used in Section~\ref{load_node}: -\begin{pythoncommand} - calc = load_node(PK) -\end{pythoncommand} -Then type -\begin{pythoncommand} - calc.inp. -\end{pythoncommand} -and press \cmd{TAB}: you will see all the link names between the -calculation and its input nodes. You can use a specific linkname to access the -corresponding input node, e.g.: -\begin{pythoncommand} - calc.inp.structure -\end{pythoncommand} - -You can use the \cmd{inp} method multiple times in order to browse the graph. For instance, -if the input structure node that you just accessed is the output of another calculation, you can access the latter by typing - -\begin{pythoncommand} - calc2 = calc.inp.structure.inp.output_structure -\end{pythoncommand} -Here \texttt{calc2} is the \texttt{PwCalculation} that produced the structure used as an input for \texttt{calc}. - -Similarly, if you type: -\begin{pythoncommand} - calc2.out. -\end{pythoncommand} -and then \cmd{TAB}, you will list all output link names of the calculation. -One of them leads to the structure that was the input of \texttt{calc} we loaded -previously: -\begin{pythoncommand} - calc2.out.output_structure -\end{pythoncommand} -Note that links have a single name, that was assigned by the -calculation that used the corresponding input or produced the corresponding -output, as illustrated in Fig.~\ref{fig:graph}. - -For a more programmatic approach, you can get a list of the inputs and outputs of a node, say \texttt{calc}, with the methods -\begin{pythoncommand} - calc.get_inputs() - calc.get_outputs() -\end{pythoncommand} - -Alternatively, you can get a dictionary where the keys are the link names and the values are the linked objects, with the methods -\begin{pythoncommand} - calc.get_inputs_dict() - calc.get_outputs_dict() -\end{pythoncommand} - -Note: You will sometime see entries in the dictionary with names like \texttt{output\_kpoints\_3511}. These exist because standard python dictionaries require unique key names while link labels may not be unique. Therefore, we use the link label plus the PK separated by underscores. - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% Upf Families -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -\subsection{Pseudopotential families} - -Pseudopotentials in AiiDA are grouped in ``families'' that contain one -single pseudo per element. We will see how to work with UPF pseudopotentials (the format used by Quantum ESPRESSO and some other codes).\\ -Download and untar the SSSP~\cite{ref:SSSP} pseudopotentials via the -commands: -% wget http://www.materialscloud.org/sssp/pseudos/SSSP_eff_PBESOL.tar.gz -\begin{bashcommand} - wget https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz -\end{bashcommand} -Then you can upload the whole set of pseudopotentials to AiiDA by to the following -\texttt{verdi} command: -\begin{bashcommand} -verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' -\end{bashcommand} -In the command above, \texttt{SSSP\_efficiency\_pseudos} is the folder containing the pseudopotentials, 'SSSP' is the name given to the family and the last -argument is its description.\\ -Finally, you can list all the pseudo families present in the database with -\begin{bashcommand} - verdi data upf listfamilies -\end{bashcommand} - diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/4_qe_submission.tex b/docs/assets/2018_PRACE_MaX/latex/sections/4_qe_submission.tex deleted file mode 100644 index 93c8c6a6..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/4_qe_submission.tex +++ /dev/null @@ -1,427 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -%%%%%%%%%INTRO%%%%%%%%% -\section[Submit, monitor and debug calculations]{\label{sec:qe}Submit, monitor and debug calculations} -The goal of this section is to understand how to create new data in AiiDA. We will launch a total energy calculation and check its results. We will introduce intentionally some common mistakes along the process of defining and submitting a calculation and we will explain you how to recognize and correct them. While this debugging is done here `manually', workflows (that we will learn later in this tutorial) can automate this procedure considerably. -%Optionally, you will run a band structure calculation using a previous (``parent'') calculation. -For computing the DFT energy of the silicon crystal (with a PBE functional) we will use Quantum ESPRESSO~\cite{ref:QE}, in particular the PWscf code (\texttt{pw.x}). -Besides the AiiDA-core package, a number of plugins exist for many different codes. -These are listed in the \href{https://aiidateam.github.io/aiida-registry/}{AiiDA plugin registry}\footnote{\url{https://aiidateam.github.io/aiida-registry/}}. -In particular, the ``aiida-quantumespresso'' plugin (already installed in your machine) provides a very extensive set of plugins, covering most (if not all) the functionalities of the underlying codes. - - -\subsection{The AiiDA daemon} - -First of all, check that the AiiDA daemon is actually running. The AiiDA daemon is a program running all the time in the background, checking if new calculations appear and need to be submitted to the scheduler\footnote[1]{i.e. first queued on the cluster, and then run when the queuing system allows it}. The daemon also takes care of all the necessary operations before the calculation submission\footnote[2]{creating a directory where to run and copying there the input files}, and after the calculation has completed on the cluster.\footnote[3]{retrieving and copying some of the output files in the database repository, parsing one or several output files, attaching some data to the calculation and storing them in the database.} -Type in the terminal -\begin{bashcommand} -verdi daemon status -\end{bashcommand} -If the daemon is running, the output should look like - -\begin{verbatim} - Profile: default - Daemon is running as PID 1650 since 2018-05-16 16:26:04 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 1653 8.225 0 2018-05-16 16:26:04 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers -\end{verbatim} -If this is not the case, type in the terminal -\begin{bashcommand} -verdi daemon start -\end{bashcommand} -to start the daemon. - - -%%%%%%%%%%%%%%CREATE AND SUBMIT CALCULATION%%%%%%%%%%%%% -%N.B. In the old tutorial calculation are submitted via the interactive shell, whereas here users will do it via verdi run -%%%%%%%%%%%%%%% -\subsection{Creating a new calculation\label{sec:create_calc}} -To launch a calculation, you will need to interact with AiiDA mainly in the \cmd{verdi shell}. -We strongly suggest you to first try the commands in the shell, and then copy them in a script ``test\_pw.py'' using a text editor. This will be very useful for later execution of a similar series of commands. - -\textbf{The best way to run python scripts using AiiDA functionalities is to run them in a terminal by means of the command} -\begin{bashcommand} - verdi run -\end{bashcommand} - -Every calculation sent to a cluster is linked to a code, which describes the executable file to be used. -Therefore, first load the suitable code: -\begin{pythoncommand} - code = Code.get_from_string() -\end{pythoncommand} -Here \texttt{Code} is the general AiiDA class handling all possible codes, and \texttt{code} is a class instance tagged as \texttt{} (see the first part of the tutorial for listing all codes installed in your AiiDA machine). -You might also want to list only the codes that define a default calculation plugin for the pw.x code of Quantum ESPRESSO. You can do this with the following command: -\begin{bashcommand} -verdi code list -p quantumespresso.pw -\end{bashcommand} -Pick the correct codename, that might look like, e.g. \texttt{qe-pw-6.2.1@localhost}. - -Once run, AiiDA calculations are instances of the class \texttt{Calculation}, more precisely of one of its subclasses, each corresponding to a code specific plugin (for example, the PWscf plugin). You have already seen \texttt{Calculation} classes in the previous sections. - -However, to create a new calculation, rather than manually creating a new class, -the suggested way is to use a \texttt{Builder}, that helps in setting the various -calculation inputs and parameters, and provides TAB-completion. - -To obtain a new builder, we can use the \texttt{get\_builder} method of the \texttt{code} object: -\begin{pythoncommand} - builder = code.get_builder() -\end{pythoncommand} -This returns a builder that helps in setting up the inputs for the \texttt{PwCalculation} class (associated to the \texttt{quantumespresso.pw} plugin, i.e. the default -plugin for the code you chose before). - -As the first step, you can assign a (short) label or a (long) description -to the calculation that you are going to create, that you might find -convenient in the future. This can be achieved with: -\begin{pythoncommand} - builder.label = "PW test" - builder.description = "My first AiiDA calc with Quantum ESPRESSO on BaTiO3" -\end{pythoncommand} -This information will be saved in the database for later query or inspection. -Note that you can press TAB after writing \texttt{builder.} to see all available inputs. - -Now you have to specify the number of machines (a.k.a. cluster nodes) you are going to run on and the maximum time allowed for the calculation. These general calculation options, -that are independent of the code or plugin, but rather mainly passed later to the scheduler -that handles the queue, are all grouped under ``builder.options'': -\begin{pythoncommand} - builder.options.resources = {'num_machines': 1} - builder.options.max_wallclock_seconds = 30*60 -\end{pythoncommand} -Just like the normal inputs, these builder options are also TAB-completed. -Type "builder.options." and hit the TAB button to see the list of available options. - -%%%%%%%%%%%%%%%%%%%%%% -\subsubsection{Preparation of inputs} -Quantum ESPRESSO requires an input file containing Fortran namelists and variables, plus some cards sections (the documentation is available \href{http://www.quantum-espresso.org/wp-content/uploads/Doc/INPUT_PW.html}{online}\footnote{\url{http://www.quantum-espresso.org/wp-content/uploads/Doc/INPUT_PW.html}}). -The Quantum ESPRESSO plugin of AiiDA requires quite a few nodes in input, which are documented -\href{http://aiida-core.readthedocs.io/en/latest/plugins/quantumespresso/pw.html}{online}\footnote{\url{http://aiida-core.readthedocs.io/en/latest/plugins/quantumespresso/pw.html}}. -Here we will instruct our calculation with a minimal configuration for computing the energy of silicon. We need: -\begin{enumerate} -\item Pseudopotentials -\item a structure -\item the k-points -\item the input parameters -\end{enumerate} -We leave the parameters as the last thing to setup and start with structure, k-points, and pseudopotentials. - -\begin{tcolorbox} -Use what you learned in the previous section and define these two kinds of objects in this script. -Define in particular a silicon structure and a 2$\times$2$\times$2 mesh of k-points. -Notice that if you just copy and paste the code that you executed previously, you will create duplicated information in the database (i.e. every time you will execute the script, you will create another StructureData, another KpointsData, \dots). -In fact, you already have the opportunity to re-use an already existing structure.\footnote{However, to avoid duplication of KpointsData, you should first learn how to query the database, therefore we will ignore this duplication issue for now.} -Use therefore a combination of the bash command \cmd{verdi data structure list} and of the shell command \texttt{load\_node()} to get an object representing the structure created earlier. -%For the KpointsData instead we will ignore this duplication for the time being, and you can simply use the same code of this morning. -\end{tcolorbox} - -\subsubsection{Attaching the input information to the calculation} -So far we have defined (or loaded) some of the input data, but we haven't instructed the calculation to use them. -To do this, let's just set the appropriate attributes of the builder (we assume here -that you created the structure and k-points AiiDA nodes before and called them -\texttt{structure} and \texttt{kpoints}, respectively): -\begin{pythoncommand} - builder.structure = structure - builder.kpoints = kpoints -\end{pythoncommand} -Note that you can set in the builder both stored and unstored nodes. -AiiDA will take care of storing the unstored nodes upon submission. Otherwise, -if you decide not to submit, nothing will be stored in the database. - -Moreover, PWscf also needs information on the pseudopotentials, specified by UpfData objects. -This is set by storing a dictionary in ``builder.pseudo'', with keys being the -kind names, and value being the UpfData pseudopotential nodes. -To simplify the task of choosing pseudopotentials, we can however use a helper -function that automatically returns this dictionary picking the pseudopotentials -from a given UPF family. - -You can list the preconfigured families from the command line: -\begin{bashcommand} - verdi data upf listfamilies -\end{bashcommand} -Pick the one you configured earlier or one of the \texttt{SSSP} families that we provide, and link it to the calculation using the command: -\begin{pythoncommand} - from aiida.orm.data.upf import get_pseudos_from_structure - builder.pseudo = get_pseudos_from_structure(structure, '') -\end{pythoncommand} - - - -\subsubsection{Preparing and debugging input parameters} -The last thing we miss is a set of parameters (i.e. cutoffs, convergence thresholds, etc\dots) to launch the Quantum ESPRESSO calculation. -This part requires acquaintance with Quantum ESPRESSO and, very often, this is the part to tune when a calculation shows a problem. -Let's therefore use this part of the tutorial to learn how to debug problems, and {\bf let's introduce errors intentionally}. -Note also that some of the problems we will investigate appear the first times you launch calculations and can be systematically avoided by using workflows. - - -Let's define a set of input parameters for Quantum ESPRESSO, preparing a dictionary of the form: -\begin{pythoncommand} -parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, -} -\end{pythoncommand} -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, except for an invalid key called ``mickeymouse''. -When Quantum ESPRESSO receives an unrecognized key (even when you misspell one) its behavior is to stop almost immediately. -By default, the AiiDA plugin will not validate your input and simply pass it over. -Therefore let's pass this dictionary to the calculation and observe this unsuccessful behavior. - -As done before, load the ParameterData class -\begin{pythoncommand} - ParameterData = DataFactory("parameter") -\end{pythoncommand} -and create an instance of the class containing all the input parameters you just defined -\begin{pythoncommand} - parameters = ParameterData(dict=parameters_dict) -\end{pythoncommand} -Finally, set the parameters in the builder -\begin{pythoncommand} - builder.parameters = parameters -\end{pythoncommand} - - - - - - -\subsubsection{Simulate submission} -At this stage, you have recreated in memory (it's not yet stored in the database) the input of the graph shown in Fig.~\ref{fig:graph}a, whereas the outputs will be created later by the daemon. - -In order to check how AiiDA creates the actual input files for the calculation, we can simulate the submission process with the (otherwise optional) command -\begin{pythoncommand} - builder.submit_test() -\end{pythoncommand} -This creates a folder of the form \texttt{submit\_test/[date]-0000[x]} in the current directory. Check (in your second terminal) the input file \texttt{aiida.in} within this folder, comparing it with the content of the input data nodes you created earlier, and that the `pseudo' folder contains the needed pseudopotentials. You can also check the submission script \texttt{\_aiidasubmit.sh} (the scheduler that is installed on the machine is Torque, so AiiDA creates the files with the proper format for this scheduler). -Note: you cannot correct the input file from the ``submit\_test'' folder: you have to correct the script and re-execute it; the files created by \texttt{submit\_test()} are only for final inspection. - - - - - - - - - -\subsubsection{Storing and submitting the calculation} -Up to now the calculation \texttt{calc} is kept in memory and not in the database. -We will now submit it, that will implicitly create a \texttt{PwCalculation} class, -store it in the database, store also all its inputs parameters, k-points, structure, -and properly link them. -To submit it, run -\begin{pythoncommand} - from aiida.work.launch import submit - calc = submit(builder) -\end{pythoncommand} -\texttt{calc} will now be the stored \texttt{PwCalculation}, already submitted -to the daemon. The calculation has now a ``database primary key" or \texttt{pk} -(an integer ID) to the calculation (typing \texttt{calc.pk} will print this number). -Moreover, it also gets a universally-unique ID (\texttt{UUID}), -visible with \texttt{calc.uuid} that does not change even upon sharing -the data with collaborators (while the \texttt{pk} will change in that case). - -Now that the calculation is stored, you can also attach any additional attributes -of your choice, which are called ``extra'' and defined in as key-value pairs. -For example, you can add an extra attribute called \texttt{element}, with value \texttt{Si} through -\begin{pythoncommand} - calc.set_extra("element","Si") -\end{pythoncommand} -You will see later the advantage of doing so for querying. - -In the meantine, as soon as you submitted your calculation, the daemon picked it up -and started to perform all the operations to do the actual submission, going through -input file generation, submission to the queue, waiting for it to run and finish, -retrieving the output files, parsing them, storing them in the database and setting the -state of the calculation to \texttt{Finished}. - -\textbf{N.B.} If the daemon is not running the calculation will remain in the \texttt{NEW} state until when you start it. - - - -%%%%%%%%%%%%%%%%%%%MONITOR CALCULATION%%%% -%Add here parts on troubleshooting failed caluclations -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\subsubsection{Checking the status of the calculation} -You can check the calculation status from the command line: -\begin{bashcommand} - verdi calculation list -\end{bashcommand} -Note that \cmd{verdi} commands can be slow in this tutorial when the calculation is running (because you just have one CPU which is also used by the PWscf calculation). - -By now, it is possible that the calculation you submitted has already finished, -and therefore that you don't see any calculation in the output. In fact, -by default, the command only prints calculations that are still being handled by the daemon, i.e.\ those with a state that is not \texttt{FINISHED} yet\footnote{For JobCalculations (i.e., calculations that are submitted to a remote computer through a scheduler) there is an additional "Job state" (last column of the output of \texttt{verdi calculation list}) that can either be FINISHED if all went well, or one of the possible failure states (FAILED, PARSINGFAILED, SUBMISSIONFAILED, RETRIEVALFAILED). These states are represented as -a Finished state (third column of \texttt{verdi calculation list}, with a zero/non-zero -error code depending if they finished/did not finish correctly). This latter state -is more general than just JobCalculations and also applies to workflows, as we will -see later in the tutorial.}. - -To see also (your) calculations that have finished (and limit those only to the one created in the past day), use instead -\begin{bashcommand} - verdi calculation list -a -p1 -\end{bashcommand} -as explained in the first section. - -To inspect the list of input files generated by the AiiDA (this can be done even -when the calculation did not finish yet), type -\begin{bashcommand} - verdi calculation inputls -c -\end{bashcommand} -with \texttt{pk\_number} the pk number of your calculation. This will show the contents of the input directory (\cmd{-c} prints directories in colour). Then you can also check the content of the actual input file with -\begin{bashcommand} - verdi calculation inputcat | less -\end{bashcommand} - - - - - - - - - - - - - - -\subsection{Troubleshooting} -After all this work the calculation should end up in a FAILED Job state (last column of -\texttt{verdi calculation list}), and -correspondingly the error code near the ``Finished" status of the State should be non-zero -(400 for FAILED calculations). This was expected, since we used an invalid key in the input parameters. -Situations like this happen (probably often...) in real life, so we built in AiiDA the tools to traceback the problem source and correct it. - -A first way to proceed is the manual inspection of the output file of PWscf. You can visualize it with: -\begin{bashcommand} - verdi calculation outputcat | less -\end{bashcommand} -This can be a good primer for problem inspection. -For something more compact, you can also try to inspect the calculation log (from AiiDA): -\begin{bashcommand} - verdi calculation logshow -\end{bashcommand} -If the calculation has encountered a mistake, this log shows a handful of warnings coming from various processes, such as the daemon, the parser of the output or the scheduler on the cluster. -In production runs, errors will mostly come from an unexpected termination of the PWscf calculation. -The most programmatic way to handle these errors is to inspect the warnings key by loading the calculation object, say \texttt{calc}, and the using the following method: -\begin{pythoncommand} -calc.res.warnings -\end{pythoncommand} -This will print a list of strings reporting errors experienced during the execution, that can be easily read in python (and thus addressed programmatically), but are also reported in the calculation log. -With any of these three methods you can understand that the problem is something like an `invalid input key', which is exactly what we did. - -Let's use a parameters dictionary that actually works. -Modify the script \texttt{test\_pw.py} script modifying the parameter dictionary as -\begin{pythoncommand} -parameters_dict = { - "CONTROL": {"calculation": "scf", - }, - "SYSTEM": {"ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": {"conv_thr": 1.e-6, - } - } -\end{pythoncommand} - -If you launch the modified script by typing -\begin{bashcommand} - verdi run test_pw.py -\end{bashcommand} -you should now be able to see a calculation reaching successfully the FINISHED state. -Now you can access the results as you have seen earlier. -For example, note down the pk of the calculation so that you can load it in the \texttt{verdi shell} and check the total energy with the commands: -\begin{pythoncommand} -calc=load_node() -calc.res.energy -\end{pythoncommand} - -% As a last thing for this section you can run it via the \cmd{verdi run} command. You may substitute the last command by something like -% \begin{pythoncommand} -% print calc.res.energy -% \end{pythoncommand} -% in order to print the final energy. and verify that it corresponds to what obtained via the stepwise ``shell submission''. -% -% We remind that to run an AiiDA script you need to type -% \begin{bashcommand} -% verdi run pw_test.py -% \end{bashcommand} - - - - - - - -% -% -%\subsection{Optional: band structure calculation} -% -%Now that you know how to restart a calculation, with a few extra commands you can compute the band structure of silicon. -%Identify a calculation that successfully computed the total energy of silicon and load it. -%Now, we create a restart calculation, but this time we let the calculation use the charge density of the `parent' calculation. -%\begin{pythoncommand} -%c1 = load_node(PK) -%c2 = c1.create_restart(use_output_structure=True) -%\end{pythoncommand} -%PWscf requires to set a flag to signal that you want to obtain the energy of bands (and not the total energy). -%Therefore, modify the input dictionary with -%\begin{pythoncommand} -%new_dict = c2.inp.parameters.get_dict() -%new_dict['CONTROL']['calculation'] = 'bands' -%new_parameters = ParameterData(dict=parameter_dict) -%\end{pythoncommand} -%By default, the AiiDA plugin of PWscf does not retrieve the files containing the entire band structure (mainly for concerns of disk space usage). -%If you want to see the band structure, you need to set also an additional input node called \texttt{settings} -%\begin{pythoncommand} -%settings_dict = {'also_bands': True, -% 'PARENT_FOLDER_SYMLINK': True} -%settings = ParameterData(dict=settings_dict) -%\end{pythoncommand} -%Notice that we also set another (optional) key \texttt{PARENT\_FOLDER\_SYMLINK}, so that the new calculation \texttt{c2} will not copy the old charge density file in a new folder, but will use the old one via a symlink (helping you to save space on the cluster). -%Lastly, we want to compute the band structure along the high symmetry paths of the Brillouin zone. -%In this case you do not need to provide explicitely the coordinates of the k-points. Type instead -%\begin{pythoncommand} -%kpoints = KpointsData() -%kpoints.set_cell(c2.inp.structure.cell, c2.inp.structure.pbc) -%kpoints.set_kpoints_path(kpoint_distance = 0.05) -%\end{pythoncommand} -%In this way, you passed the crystal structure to the kpoints class, which has a built in utility to build the list of k-points on high symmetry directions of the Brillouin zone. -%\texttt{kpoint\_distance} is simply a parameter determining the resolution: the smaller, the more k-points. -%Now, don't forget to set the calculation to use these new nodes -%\begin{pythoncommand} -%c2.use_kpoints(kpoints) -%c2.use_parameters(new_parameters) -%c2.use_settings(settings) -%\end{pythoncommand} -%And the band structure is ready to be computed. -% -%After the calculation has run, checked that it finished successfully. -%In this case, the AiiDA parser has read the output files containing the band structure and has inserted this information in the database. -%To visually inspect the band structure, load the calculation (previously \texttt{c2}) and look for an output node at linkname \texttt{output\_band}. -%\begin{pythoncommand} -%band = c2.out.output_band -%\end{pythoncommand} -%In this way you loaded the \texttt{BandsData} object that contains all the information concerning the band structure. -%Take your time to explore the methods of this class. -%Here we just mention the command -%\begin{pythoncommand} -%band.export('silicon_band_structure.agr') -%\end{pythoncommand} -%This command should have created an xmgrace plot, which you can open and inspect from the bash shell: -%\begin{bashcommand} -%xmgrace silicon_band_structure.agr -%\end{bashcommand} -% - - - diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/5_querybuilder.tex b/docs/assets/2018_PRACE_MaX/latex/sections/5_querybuilder.tex deleted file mode 100644 index 99b4134d..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/5_querybuilder.tex +++ /dev/null @@ -1,563 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -%%%%%%%%Inherited from old tutorial -%Task 1 and Taks 2 do not actually need QueryTool, as they inspect a previously defined group. -%The decision to improve it is left to authors of this part -%Taks 3 might be reused but recast to the logic and syntax of QueryBuilder -%%%%%%%% - -\section{Queries in AiiDA: The QueryBuilder} -\label{sec:querybuilder} -In this part of the tutorial we will focus on how to query our database using a querying tool for AiiDA called the \textit{QueryBuilder}. -Queries are, very loosely defined, questions to your database. -We will first show you some simple examples and tasks on how to explore your database. -Then we will proceed to a more concrete exercise on the screening of magnetic and metallic perovskites.\\ - -% In the end we will present some more complicated examples and exercises showing all the possibilities of the QueryBuilder. - - - -%To be deleted or incorporated to the above text: -% -%In the first part of the tutorial, you got an idea of how a single calculation is submitted with AiiDA and how its results can be retrieved, both from the command line and from the \verb|verdi shell|. In this second part of the tutorial, we will instead focus on how to systematically retrieve, query and analyse the results of multiple calculations using AiiDA features. The idea of this part of the tutorial is to screen magnetic and metallic perovskites. -%For time reasons, a set of calculations have already been performed on 57 perovskites, using three different pseudopotential families (LDA, PBE and PBESOL, all from GBRV 1.2~\cite{ref:GBRV}). Each of them was run with the same type of script you tried in Sec.~\ref{sec:qe}, so there is nothing new. -%These calculations have been imported, and they were created by a different user. - - -%\textcolor{red}{LK: Some text on what is the difference, and to use -A option for all-users with command line} - - -% If you want to reproduce them, simply put the code of Sec.~\ref{sec:qe} in a Python function, and change the A and B species in a for loop.These calculations are spin-polarised (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell.} - -%\textcolor{red}{LK: Some words in QueryBuilder here} - - -\subsection*{Task 1 - Introduction to QueryBuilder} -\begin{table}[b] -\centering -\begin{tabular}{cc} -\hline -Node \& subclasses & Number in DB \\ \hline -Node & 4707 \\ -StructureData & 621 \\ -ParameterData & 1338 \\ -KpointsData & 861 \\ -UpfData & 99 \\ -JobCalculation & 448 \\ \hline -\end{tabular} -\caption{List of some Node subclasses and how many times they occur in our test database.} -\label{fig.types} -\end{table} - - -In this task we will use the QueryBuilder to do some basic queries and understand our database. -As a first step we should import our querying tool, the \textit{QueryBuilder}. -\begin{pythoncommand} -from aiida.orm.querybuilder import QueryBuilder -\end{pythoncommand} -After the above import, we create our first query. -To do so, we will have to instantiate a QueryBuilder instance: -\begin{pythoncommand} -qb = QueryBuilder() -\end{pythoncommand} -Our query is still empty, we have not yet defined what we want to see. -For example, we will ask for all the nodes of our database. -This is as simple as appending the Node class to the query that we construct. -\begin{pythoncommand} -qb.append(Node) -\end{pythoncommand} -At this point, we can finish our query by asking back all nodes and by typing -\begin{pythoncommand} -qb.all() # Returns all nodes in the database -\end{pythoncommand} -However, this command will return us all the Nodes directly, which may not be the most wise thing to do -considering that is the biggest family of AiiDA stored objects that we can query. -To understand the size of the result, we can type the following command: -\begin{pythoncommand} -qb.count() # Returns an integer, the number of nodes in the database -\end{pythoncommand} -% Taking some stuff out do reduce length -%~ If we decide to get a large number of results, -%~ then it is could be better to retrieve them using an iterator and not using the \texttt{.all()} method. -%~ \begin{pythoncommand} -%~ for node, in qb.iterall(): - %~ print node -%~ \end{pythoncommand} -If you are interested to retrieve a subclass of a node, append that specific subclass instead of Node: -\begin{pythoncommand} -StructureData = DataFactory('structure') -qb = QueryBuilder() # Creating a new QueryBuilder instance -qb.append(StructureData) # Telling the QueryBuilder instance that I want structures -qb.all() # Asking for all the results! -\end{pythoncommand} - - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item -Try now to find the number of instances for some subclasses of Node (e.g. StructureData, ParameterData, etc.) -that are stored in your database. -The result should look like \autoref{fig.types}. -Of course, the numbers can be different! -\end{itemize} -\end{tcolorbox} - -\textbf{Comment:} If you are familiar with the SQL (Structured Query Language) syntax then you may wonder what the issued SQL command is. -This can be easily seen by typing: -\begin{pythoncommand} -str(qb) -\end{pythoncommand} - -\textbf{Comment:} -If you want to get inspired by the available QueryBuilder options you can just press the -\emph{tab} key in an interactive shell (after typing \texttt{qb.}) to see the available options. - -\textbf{Comment:} After you run a query, a new \texttt{QueryBuilder} instance -needs to be defined if you want to make a new query. - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\subsection*{Task 2 - Projections and filters} - -\begin{table}[h] -\begin{center} - -\begin{tabular}{ccc} \hline -Operator & Datatype & Example \\ \hline -== & All & \{'==':12\} \\ -in & All & \{'in':['FINISHED', 'PARSING']\} \\ \hline -$>,<,<=,>=$ & floats, integers, dates & \{'$>$':5.2\} \\ \hline -like & Chars & \{'like':'calculation\%'\} \\ -ilike & Chars & \{'ilike':'caLculAtioN\%'\} \\ \hline -or & & \{'or':[\{'$<$':5.3\}, \{'$>$':6.3\}]\} \\ -and & & \{'and':[\{'$>$':5.3\}, \{'$<$':6.3\}]\} \\ -\hline -\end{tabular} -\end{center} -\caption{Operators currently implemented for all backends.} -\label{tab.filterops} -\end{table} - -In database language performing a projection means to extract one or more specific columns from a table. In the AiiDA language this is equivalent to say that we select what properties a query should return out of the queried objects. -For example, we might be interested only in the id of a set of nodes (or their creation date, or any stored value). -To this purpose we should suitably instruct a QueryBuilder object by means of the "project" key. -For example, if we would like to get all the ids of the nodes, we would type the following: -\begin{pythoncommand} -qb = QueryBuilder() -qb.append(Node, project=["id"]) -qb.all() -\end{pythoncommand} - -\begin{table}[b] -\centering -\begin{tabular}{cc} -\hline -Entity & Properties \\ \hline -Node & id, uuid, type, label, description, ctime, mtime \\ \hline -Computer & \begin{tabular}{@{}c@{}}id, uuid, name, hostname, description,\\ enabled, transport\_type, scheduler\_type\end{tabular} \\ \hline -User & id, email, first\_name, last\_name, institution \\ \hline -Group & id, uuid, name, type, time, description \\ \hline -\end{tabular} -\caption{A selection of entities and some of their properties.} -\label{tab.properties} -\end{table} - - - -\begin{tcolorbox} -Please note that if you would like to perform an operation on the \emph{pk} of a node, -you should use the keyword \emph{id} in QueryBuilder queries. -\end{tcolorbox} - - -Most likely, performing a query implies to select only those elements that fulfill certain criteria. -For example, we might want to select all the calculations that were launched on a specific date. -In database language, this is called "adding a filter" to a query. -A filter is a boolean operator that returns True or False. -\autoref{tab.filterops} lists all operators that we implemented. -A selection of entities and some of their properties that you can use at your projections and filters -can be found at table \autoref{tab.properties}. - -If you want to add filters to your query, you simply add the \emph{filters} keyword with a dictionary. -Suppose you want to know the creation date of a structure of which you know the uuid: -\begin{pythoncommand} -qb = QueryBuilder() # Instantiating a new QueryBuilder -qb.append( - StructureData, # I want structures! - project=["ctime"], # I'm interested in creation time! - filters={"uuid": { - "==":"ace6523a-2019-47c4-98a3-48429265f62c" - }}) # I want the structure with this UUID -qb.all() -\end{pythoncommand} -Try it out! -There is also the possibility to combine multiple filters on the same object using the ``and'' or the ``or'' - keyword in the filter section. Let's see an example. -\begin{pythoncommand} -from datetime import datetime, timedelta -qb = QueryBuilder() -qb.append( - StructureData, - project=["uuid"], # I want to see only the UUID - filters={"or":[ # First filter is an or statement - {"ctime": {">":datetime.now() - timedelta(days=12)}}, - {"label": "graphene"} - ]} - ) -qb.all() -\end{pythoncommand} - -In the above example we added an ``or'' keyword between the two filters. -The query return every structure in the database that was created -in the last 12 days or is named "graphene". \\ -\textbf{Hints for the exercises:} -\begin{itemize} -\item The operator '>', '<' works with date-type properties with the expected behavior. -\item For your date comparisons you will need to create a \texttt{datetime} object to which you can assign a date of your preference. - You will have to do the necessary import (\texttt{from datetime import datetime}) and create an object by giving a specific date. - E.g. datetime(2015, 12, 26). For further information, you can consult the Python's online documentation. -\end{itemize} - -\begin{tcolorbox} -\textbf{Exercises:} -\begin{itemize} -\item -Write a query that returns all instances of StructureData that have been created after the 1st of January 2016. -\item -Write a query that returns all instances of Group whose name starts with ``tutorial''. -\end{itemize} -\end{tcolorbox} - -% \textbf{Hint 2:} The creation time is stored in a column called \textit{ctime}. - -\subsection*{Task 3 - Defining relationships} -In the previous tasks we saw how to select specific entities from AiiDA, how to apply projections -on their properties and how to apply filters. -Moreover, you should know by now how to write complex filters including ``and'' and ``or'' keywords. -In this task we will see how to associate entities by defining relationships among them. - -Defining a relationship between two entities is as easy as appending another entity to the QueryBuilder query. -For example, let's look at the following query: - -\begin{pythoncommand} -from aiida.orm import Group, JobCalculation -qb = QueryBuilder() -qb.append(JobCalculation, - tag="mycalculation", # I tag, so I can refer to this entity later - project=["*"]) # I'm asking for ORM instances ("*") -qb.append(Group, # Also asking for groups - group_of="mycalculation", # I want the calculation to be part of the group - filters={ # A filter, the name has to be "pbe_calculation" - "name":{"==":"pbe_calculation"} - }) -qb.all() -\end{pythoncommand} -This returns all jobs that belong to a Group with the name ``pbe\_calculation''. -The \emph{project=["*"]} returns the AiiDA orm instance (i.e. an instance of \emph{aiida.orm.calculation.job.JobCalculation}). -% -%The graphical representation of the above query can be seen at Figure~\ref{fig:simple_query}. -%~ \begin{figure}[h] -%~ \begin{center} -%~ \includegraphics[width=3.5cm]{img/qb_example_1.png} -%~ \end{center} -%~ \caption{Query: Fetch me all the RemoteData that belong to a Group.} -%~ \label{fig:simple_query} -%~ \end{figure} -% -% -There are few details that we should remember from the above query: -\begin{itemize} -\item We \emph{append} to the QueryBuilder instance new entities, and specify how they linked -to previous entities with keywords (``input\_of'', ``output\_of'', ``group\_of''). -The possible relationship keywords can be seen at table~\ref{table:join_rel}. -\item Therefore, we have to \emph{tag} the entities that we want to reference. -We pass these tags along with the keyword, to define a relationship. -\end{itemize} - - -\begin{table} - \begin{center} - \begin{tabular}{l l l l} - Entity from & Entity to & Relationship & Explanation\\ \hline\hline - Node & Node & input\_of & One node as input of another node\\ - Node & Node & output\_of & One node as output of another node\\ - Node & Node & ancestor\_of & One node as the ancestor of another node\\ - Node & Node & descendant\_of & One node as descendant of another node\\\hline - Group & Node & group\_of & The group of a node\\ - Node & Group & member\_of & The node is a member of a group\\\hline - Computer & Node & computer\_of & The computer of a node\\ - Node & Computer & has\_computer & The node of a computer\\\hline - User & Node & creator\_of & The creator of a node is a user\\ - Node & User & created\_by & The node was created by a user\\ - \end{tabular} - \caption{Available relationships} - \label{table:join_rel} - \end{center} - \end{table} - - - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} - \item Write a query that returns all the StructureData that are an input of a JobCalculation. -\end{itemize} -More (optional) exercises on entity relationships can be found at the appendix. -\end{tcolorbox} - - -\subsection*{Task 4 - Attributes and extras} -Node and its subclasses have properties stored in key/value format. -These are called \emph{attributes} and \emph{extras} and can be used in filters and projections as the other properties that we have seen previously. - -Let's project the value of the property \emph{attributes.energy\_smearing}. -\begin{pythoncommand} -qb = QueryBuilder() -qb.append( - ParameterData, - project=["attributes.energy_smearing"] - ) -qb.all() -\end{pythoncommand} -The above query takes the attributes properties of every ParameterData and searches for a -key called energy\_smearing. If that key is found, the value is projected, -otherwise the value is None. Try it out! -% Of course, searching inside key/values can go as deep as needed. -% E.g. you can project on attributes.a.b.c which will correspond to a property \texttt{\{..., "a": \{... "b": \{... "c": "val" ...\} ...\}, ...\}} % THIS STUFF DOES NOT CORRESPOND TO OUTPUT -~\\~\\ -% textcolor{blue}{SZ: What happens with lists?} - -\textbf{Hints for the exercises:} -\begin{itemize} -\item If you are unsure about the key of the node that you would like to project, you can print all the attributes of a specific node to get some inspiration. This can be done by calling the \texttt{.get\_attrs()} method of a specific node. -\item The pseudopotentials are stored in AiiDA in with the help of the ORM class UpfData. The element that the pseudopotential correspond to is stored in the \emph{attributes.element} property. -\end{itemize} - -\begin{tcolorbox} -\textbf{Exercises:} -\begin{itemize} - \item Print all the attributes of any ParameterData node stored in your database. - \item Write a query that checks if you have the pseudopotentials for the element \textit{Si}. Do the same for \textit{C}. -\end{itemize} -More (optional) exercises on attributes and extras can be found at the appendix. -\end{tcolorbox} - -%\textcolor{blue}{SZ: Distinct doesn't seem to work on attributes.} - -\subsection*{Task 5 - A small ``high-throughput'' analysis} - -\begin{figure}[!b] - \includegraphics[width=\linewidth]{img/magnetization_smearing_perovskites.pdf} - \caption{The contribution from the smearing to the total energy (upper) and the magnetization per unit cell (lower) - in all the perovskites analyzed and for three different pseudopotential families.} - \label{fig:barplot_perov} -\end{figure} -% -%In the first part of the tutorial, you got an idea of how a single calculation is submitted with AiiDA and how its results can be retrieved, both from the command line and from the \verb|verdi shell|. -In this part of the tutorial, we will focus on how to systematically retrieve, query and analyze the results of multiple calculations using AiiDA. -We know you're able to do this yourself, but to save time, a set of calculations have already been done with AiiDA for you on 57 perovskites, -using three different pseudopotential families (LDA, PBE and PBESOL, all from GBRV 1.2~\cite{ref:GBRV}). -These calculations are spin-polarized (without spin-orbit coupling), -use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. -The idea of this part of the tutorial is to ``screen'' for magnetic and metallic perovskites in a ``high-throughput'' way. - -As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. -Once more check the list of groups in your database by typing - -\begin{bashcommand} - verdi group list -\end{bashcommand} - -The calculations needed for this task were put in three different groups whose names start with "tutorial\_" (one for each pseudopotential family). -The main task is to make a plot showing, for all perovskites and for each pseudopotential family, -the total magnetization and the $-TS$ contribution from the smearing to the total energy. -An example is shown in \autoref{fig:barplot_perov}. - -\subsubsection*{Preparing the analysis} -Let us now guide you through the definition of the query that allows you to retrieve the relevant data.\\~\\ -\textbf{Hint for the exercise}: -\begin{itemize} -\item To select multiple names in a filter your filter-dictionary should look like -\begin{pythoncommand} -qb.append(Group, filters={"name": {"in": ["name1","name2","name3"]}}) -\end{pythoncommand} -\end{itemize} - -\begin{tcolorbox} -\textbf{Exercise:}\\~\\ -Please perform the following steps -\begin{enumerate} -\item Write a query to retrieve the three groups and project the respective names. -\item Extend the query to the PwCalculation nodes that are members of the selected groups. -\item Extend the query so that also the chemical formula of the input structure of each calculation is returned. -For simplicity the formulas have been added in the extras of each structure node under the key \textit{"formula"}. -The chemical formula of a StructureData node can also be accessed by the method \cmd{structure.get\_formula()} -\item Every successful PwCalculation has in output a ParameterData instance that stores the results as key-value pairs. -You can find these pairs among the attributes. -To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. -For convenience, the units are also added as key/value pairs (with the same key name, but with \cmd{\_units} appended). -% Load a ParameterData node (ex. pk=3516) and find the keys that you are interested in by typing: -% \begin{pythoncommand} -% node.get_attrs() -% \end{pythoncommand} -Extend the query so that also the output ParameterData of each calculation is returned. -Project only the attributes relevant to your analysis (like smearing energy, ...). -\end{enumerate} -\end{tcolorbox} - -\subsubsection*{Running the query and plotting the results} - -% We will now get into a small high-throughput analysis of a larger number of PwCalculations. -% The goal is to extend the above query so that you project also on the resulting magnetization, smearing, and group name of the group they belong to. -Now that your query is ready just run it: -\begin{pythoncommand} - res = qb.dict() -\end{pythoncommand} -The above results returns a list of dictionaries. -In order to be able to plot the data that you have retrieved a script called \texttt{plot\_calculation\_results.py} -was provided to you. This script reads an input text file containing the data and produces a plot similar to \autoref{fig:barplot_perov}. - -You should prepare this input text file in the following format, -formatting the data present in \texttt{res}. -Each row in the input file represents one calculation and contains the following informations separated by a comma: - -\begin{enumerate} -\item The structure formula. -\item The name of the pseudo family. Remove the tutorial\_ substring from the name if it bothers you. -\item The smearing energy -\item The unit used for the smearing energy. -\item The magnetization. -\item The unit of the magnetization. -\end{enumerate} - -As an example, the first three lines can look like: -\begin{bashcommand} -Sn2O3, pbesol, -1.95921960844e-05, eV, 0.0, Bohrmag / cell -CaSiO3, pbe, 0.0, eV, 0.0, Bohrmag / cell -CuNbO3, pbe, -0.010202636111, eV, 0.0, Bohrmag / cell -\end{bashcommand} - -%Once this is done, you can read the this file with the provided script. - - -\iffalse -You should be able to print the data in the desired format with about ten lines of python code. In case you are not very familiar with python, you may take inspiration from the following snippet: - -\begin{pythoncommand} -with open("res.txt","w") as fout: - for row in res: - formula = row["structure"]["extras.formula"] - magnetization = row["results"]["attributes.total_magnetization"] - magnetization_units = row["results"]["attributes.total_magnetization_units"] - smearing = row["results"]["attributes.energy_smearing"] - smearing_units = row["results"]["attributes.energy_smearing_units"] - pseudo_family = row["group"]["name"].replace("tutorial_", "") - str_row = ", ".join( - map(str, (formula, pseudo_family, - smearing, smearing_units, magnetization, magnetization_units - )) - ) - fout.write("{}\n".format(str_row)) - -\end{pythoncommand} -\fi - - -If you are producing the desired output, write that output to a file (here we will call it \texttt{res.txt}). -If you are using \emph{jupyter}, you can plot the files and see the results directly in the browser. First, in a cell, do -\begin{pythoncommand} -cd ~/tutorial_scripts/ -\end{pythoncommand} -to go to the right folder, and then in the following cell type: -\begin{pythoncommand} -%pylab inline -import plot_calculation_results -plot_calculation_results.plot_results('../res.txt') -\end{pythoncommand} -(possibly replacing \texttt{../res.txt} with the correct location of the file). - -Otherwise, if you are not using \emph{jupyter}, to plot your data just go in the shell and change directory to the subfolder \texttt{tutorial\_scripts}. Then type in the shell -\begin{bashcommand} -python plot_calculation_results.py ../res.txt -\end{bashcommand} -(or replace \texttt{../res.txt} with the correct location of the \texttt{res.txt} file). The \texttt{plot\_calculation\_results.py} file is already provided to you among the scripts given to you for the tutorial, in the \texttt{tutorial\_scripts} folder. If everything is right, you should get a plot similar to \autoref{fig:barplot_perov}. -You can also pass an option to store the output as a plot on a file: -\begin{bashcommand} -python plot_calculation_results.py res.txt -o myresult.pdf -\end{bashcommand} - - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item Look at the plot that you produced. Which of the perovskites are metals? -\end{itemize} -\end{tcolorbox} - - -%~ \begin{pythoncommand} -%~ qb = QueryBuilder() -%~ qb.append(StructureData, project="*") -%~ qb.append(Calculation, tag="calc") -%~ qb.append( - %~ ParameterData, - %~ project="attributes.energy", - %~ filters={"attributes.energy":{"<=":0}} - %~ ) -%~ qb.append(Group, group_of="calc") -%~ -%~ for structure, energy in qb.iterall(): - %~ print structure.get_formula(), energy -%~ \end{pythoncommand} - - -%~ Figures~\ref{fig:barplot_mag} and~\ref{fig:barplot_smearing} show the plots you should obtain (note that in Fig.~\ref{fig:barplot_smearing} we actually plotted $|-TS|=TS$ since $-TS$ is negative for all the species studied). - -% - -%\begin{figure}[tb] -% \includegraphics[width=\linewidth]{img/smearing_energy_vs_perovskites_allpseudo_Gaussian_0p02.png} -% \caption{$-TS$ contribution to the total energy (in absolute value, since it is actually negative for all species) from the smearing, for all the perovskites analyzed and for three different pseudopotential families\label{fig:barplot_smearing}.} -%\end{figure} -% -%Two python scripts can help you doing this (you can find them in the subfolder \texttt{$\sim$/tutorial\_scripts}): -%\begin{itemize} -%\item \texttt{get\_pks.py}: gives a list of pk numbers for a given group, possibly filtering also for those perovskites with given ``A"" and/or ``B"" species. -% -%Use it from the command line in the following way: -%\begin{bashcommand} -% ./get_pks.py -A -B -%\end{bashcommand} -%This script prints on output the list of pks (first column) and chemical formulas (second column) of the perovskite(s). The \cmd{-A} and \cmd{-B} options are optional. -% \item \texttt{get\_info.py}: prints several information about a given calculation. -% -%You can use it (from a terminal) with the command -%\begin{bashcommand} -% ./get_info.py -%\end{bashcommand} -%\end{itemize} -%% -%We suggest that you give a look to the content of the scripts we provide, to understand what they do. To prepare the plots, you can either create your own python script taking inspiration from the two scripts that we provide, or (if you do not want to write a Python script, but rather use bash commands) you can simply use the provided scripts as they are. -%Note also that if the graphical plotting software is too slow to load over SSH, you can create a text data file on the remote machine, then copy the data on your computer, and prepare the plot there (you can use any software you like: gnuplot, xmgrace, or even simply Excel). - - -%~ \subsection*{Task 5.2} -%~ We chose five calculations (using the PBEsol pseudopotential family) of five different representative perovskites, for which we plotted the $-TS$ contribution as a function of the Kohn-Sham band-gap. The resulting plot is shown in \cref{fig:smearing_vs_bandgaps}. \begin{tcolorbox} -%~ Use this plot and the plots you obtained for the smearing energy of all perovskites, to define a criterion to distinguish which compounds are metals and which ones are insulators. -%~ \end{tcolorbox} - -% -%\begin{figure}[tb] -% \centering -% \includegraphics[width=0.7\linewidth]{img/smearing_energy_vs_bandgap_Gaussian_0p02.png} -% \caption{$-TS$ contribution (in absolute value) vs. Kohn-Sham band-gap for five selected perovskites, using the pseudopotentials with PBEsol functional\label{fig:smearing_vs_bandgaps}.} -%\end{figure} -% - -%~ \subsection*{Task 5.3} -%~ Finally, we want to print the list of perovskites which are metals according to the criterion we defined, and those that are magnetic. To this aim, we can use the \emph{QueryBuilder} that helps you making easy queries to the database, without the need of knowing the SQL language. - - - -%~ \begin{tcolorbox} -%~ Do another query to find out metallic perovskites according to the criterion on the smearing energy you found above. Check that your query results are consistent with the plots you did at the beginning of the exercise. -%~ \end{tcolorbox} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/5_querybuilder_jupyter.tex b/docs/assets/2018_PRACE_MaX/latex/sections/5_querybuilder_jupyter.tex deleted file mode 100644 index 2a89cca9..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/5_querybuilder_jupyter.tex +++ /dev/null @@ -1,29 +0,0 @@ -% Version using the Jupyter Notebook - -\section{Queries in AiiDA: The QueryBuilder} -\label{sec:querybuilder} - -\begin{tcolorbox} -This part of the tutorial is provided only in interactive mode through a Jupyter notebook, which you will be able to run in your browser. -To accomplish this we first need to start the Jupyter server, if you didn't do it already at the very beginning of the tutorial. -First make sure you are connected to the virtual machine with local forwarding enabled, as described in section \ref{sec:sshintro}. -Then, on the virtual machine, first make sure your are in the \texttt{aiida} virtual environment: - -\begin{bashcommand} -workon aiida -\end{bashcommand} - -If the virtual environment is successfully loaded, your prompt should be prefixed with \texttt{(aiida)}. -To finally launch the Jupyter server, execute the following commands: - -\begin{bashcommand} -cd ~/examples/aiida-demos/tutorial/ -jupyter notebook --no-browser -\end{bashcommand} - -If all went well, you should now be able to open up a browser on your local machine and point it to the following address \texttt{http://localhost:8888/?token=2a3ba3...} (replace the token with the one printed on output by the previous command). -This should now show you a directory navigator. -To open the notebook, click on \texttt{querybuilder} and then select the file \texttt{tutorial.ipynb}. -Note that there is also a \texttt{solution.ipynb}, which is a copy of the same notebook, but which contains the solutions to all the exercises. -You can use this version at your own discretion if you get stuck at some point (but we suggest that you try not to look at it at first). -\end{tcolorbox} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/6_workflows.tex b/docs/assets/2018_PRACE_MaX/latex/sections/6_workflows.tex deleted file mode 100644 index fae37abe..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/6_workflows.tex +++ /dev/null @@ -1,581 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -%%%%%%%%%%%%%%%%This slot is devoted to workflows, although the material inherited from the old tutorial does not use them. -% Nevertheless, it has been suggested to use workflows for calculating equation of state, so all this stuff can be inspirational -%%%%%%%%%%%%%%%%%%%%%% - -\section{AiiDA Workflows: workfunctions, workchains} - -The aim of the last part of this tutorial is to introduce the concept of workflows in AiiDA. - -In this section, we will ask you to: -\begin{enumerate} -\item Understand how to keep the provenance when running small python scripts to convert one data object into another (postprocessing, preparation of inputs, \ldots) -\item Understand how to represent simple python functions in the AiiDA database -\item Learn how to write a simple workflow in AiiDA (without and with remote calculation submission) -\item Learn how to write a workflow with checkpoints: this means that, even if your -workflows requires external calculations to start, them and their dependences are managed through the daemon. While you are waiting for the calculations to complete, you can stop and even shutdown the computer in which AiiDA is running. When you restart, the workflow will -continue from where it was. -\item (optional) Go a bit deeper in the syntax of workflows with checkpoints (WorkChain), e.g. implementing a convergence workflow using \texttt{while} loops. -\end{enumerate} - -A note: this is probably the most ``complex'' part of the tutorial. -We suggest that you try to understand the underlying logic behind the scripts, -without focusing too much on the details of the workflows implementation or the syntax. -If you want, you can then focus more on the technicalities in a second reading. - -\subsection[Workfunctions]{Introduction} -The ultimate aim of this section is to create a workflow to calculate the equation of state of silicon. This is a very common task for an \textit{ab initio} researcher. An equation of state consists in calculating the total energy $E$ as a function of the unit cell volume $V$. The minimal energy is reached at the equilibrium volume $V^{\star}$. Equivalently, the equilibrium is defined by a vanishing pressure $p=-dE/dV$. In the vicinity of the minimum, the functional form of the equation of state can be approximated by a parabola. Such an approximation greatly simplifies the calculation of the bulk modulus, that is proportional to the second derivative of the energy $d^2E/dV^2$ (a more advanced treatment requires fitting the curve with, e.g., the Birch--Murnaghan expression). - -The process of calculating an equation of state puts together several operations. -First, we need to define and store in the AiiDA database the basic structure of, e.g., bulk Si. -Next, one has to define several structures with different lattice parameters. Those structures must be connected between them in the database, in order to ensure that their provenance is recorded. In other words, we want to be sure that in the future we will know that if we find a bunch of rescaled structures in the database, they all descend from the same one. How to link two nodes in the database in a easy way is the subject of Sec.~\ref{sec:provenancewf}. - -In the following sections, the newly created structures will then serve as an input for total energy calculations performed, in this tutorial, with Quantum ESPRESSO. This task is very similar to what you have done in the previous part of the tutorial. Finally, you will fit the resulting energies as a function of volume to get the bulk modulus. -As the EOS task is very common, we will show how to automate its computation with workflows, and how to deal with both serial and parallel (i.e., independent) execution of multiple tasks. Finally, we will show how to introduce more complex logic in your workflows such as loops and conditional statements (Sec.~\ref{sec:convpressure}), with an example on a convergence loop to find iteratively the minimum of an EOS. - - -\subsection[Workfunctions]{\label{sec:provenancewf}Workfunctions: a way to generalize provenance in AiiDA} - - -%Figure with workfunction graph -\begin{figure}[!th] -\centering - \includegraphics[width=\linewidth]{img/workfunctions} - \caption{ \label{Fig:workfunctions}Typical graphs created by using a workfunction. (a) The workfunction ``create\_structure'' takes a \texttt{Str} object as input and returns a single \texttt{StructureData} object which is used as input for the workfunction ``rescale'' together with a \texttt{Float} object. This latter workfunction returns another \texttt{StructureData} object, defining a crystal having the rescaled lattice constant. (b) Graph generated by nesting workfunctions. A wrapper workfunction ``create\_rescaled" calls serially ``create\_structure'' and ``rescale". This relationship is stored via ``CALL'' links.} -\end{figure} - - -Imagine to have a function that takes as input a string of the name of a chemical element and generates the corresponding bulk structure as a \texttt{StructureData} object. The function might look like this (you will find this function in the folder \texttt{/home/aiida/tutorial\_scripts/create\_rescale.py} on your virtual machine): - -\begin{pythoncommand} -def create_diamond_fcc(element): - """ - Workfunction to create the crystal structure of a given element. - For simplicity, only Si and Ge are valid elements. - :param element: The element to create the structure with. - :return: The structure. - """ - import numpy as np - elem_alat= { - "Si": 5.431, # Angstrom - "Ge": 5.658, - } - - # Validate input element - symbol = str(element) - if symbol not in elem_alat.keys(): - raise ValueError("Valid elements are only Si and Ge") - - # Create cel starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - the_cell = np.array([[0., 0.5, 0.5], - [0.5, 0., 0.5], - [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - StructureData = DataFactory("structure") - structure = StructureData(cell=the_cell) - structure.append_atom(position=(0., 0., 0.), symbols=str(element)) - structure.append_atom(position=(0.25*alat, 0.25*alat, 0.25*alat), - symbols=str(element)) - return structure -\end{pythoncommand} - -For the equation of state you need another function that takes as input a \texttt{StructureData} object and a rescaling factor, and returns a \texttt{StructureData} object with the rescaled lattice parameter (you will find this function in the same file \texttt{create\_rescale.py} on your virtual machine): - -\begin{pythoncommand} -def rescale(structure, scale): - """ - Workfunction to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - the_ase = structure.get_ase() - new_ase = the_ase.copy() - new_ase.set_cell(the_ase.get_cell() * float(scale), scale_atoms=True) - new_structure = DataFactory('structure')(ase=new_ase) - return new_structure -\end{pythoncommand} - -In order to generate the rescaled starting structures, say for five different lattice parameters you would combine the two functions. Enter the following commands in the \texttt{verdi shell} from the \texttt{tutorial\_scripts} folder. - -\begin{pythoncommand} -from create_rescale import create_diamond_fcc, rescale - -s0 = create_diamond_fcc("Si") -rescaled_structures = [rescale(s0, factor) for factor - in (0.98, 0.99, 1.0, 1.1, 1.2)] -\end{pythoncommand} - -and store them in the database: - -\begin{pythoncommand} -s0.store() -for struct in rescaled_structures: - struct.store() -\end{pythoncommand} - -\begin{tcolorbox} -Run the commands above to store all the structures. -\end{tcolorbox} - -As expected, all the structures that you have created are not linked in any manner as you can verify via the \cmd{get\_inputs()/get\_outputs()} methods of the StuctureData class. -Instead, you would like these objects to be connected as sketched in Fig.~\ref{Fig:workfunctions}a. Now that you are familiar with AiiDA, you know that the way to connect two data nodes is through a calculation. -%However, you do not want to run such simple python functions on a cluster! Rather, you would like to simply run them as simple python functions, but keep at the same time the provenance. -In order to ``wrap'' python functions and automate the generation of the -needed links, in AiiDA we provide you with what we call ``workfunctions''. A normal function can be converted to a workfunction by using the \texttt{@workfunction} decorator\footnote{In simple (or even simplified) words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form \texttt{@decorating\_function\_name} on the line just before the \texttt{def} line of the decorated function. If you want to know more, there are many online resources explaining python decorators.} that takes care of storing the execution as a calculation and adding the links between the input and output data nodes. - -\begin{tcolorbox} -In our case, what you need to do is to modify the two functions as follows (note that we import \texttt{workfunction} as \texttt{wf} to be shorter, but this is not required). You can do it in the file \texttt{create\_rescale.py}: -\end{tcolorbox} - -\begin{pythoncommand} -# Add this import -from aiida.work import workfunction as wf - -# Add decorators -@wf -def create_diamond_fcc(element): - ... - ... - -@wf -def rescale(structure, scale): - ... - ... -\end{pythoncommand} - -\emph{Important}: when you use workfunctions, you have to make sure that their input and output are actually Data nodes, so that they can be stored in the database. AiiDA objects such as \texttt{StructureData}, ParameterData, etc.\@ carry around information about their provenance as stored in the database. This is why we must use the special database-storable types Float, Str, etc.\@ as shown in the snippet below. - -\begin{tcolorbox} -Try now to run the following script: -\end{tcolorbox} -\begin{pythoncommand} -from aiida.orm.data.base import Float, Str -from create_rescale import create_diamond_fcc, rescale - -s0 = create_diamond_fcc(Str("Si")) -rescaled_structures = [rescale(s0,Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] -\end{pythoncommand} -and check now that the output of \texttt{s0} as well as the input of the rescaled structures point to an intermediate ProcessCalculation node, representing the execution of the -workfunction, see Fig.~\ref{Fig:workfunctions}. -\begin{tcolorbox} -For instance, you can check that the output links of \texttt{s0} are the five \texttt{rescale} calculations: -\end{tcolorbox} -\begin{pythoncommand} -s0.get_outputs() -\end{pythoncommand} -which outputs -\begin{pythoncommand} -[, - , - , - , - ] -\end{pythoncommand} -\begin{tcolorbox} -and the inputs of each ProcessCalculation (``rescale'') are obtained with: -\end{tcolorbox} -\begin{pythoncommand} -for s in s0.get_outputs(): - print s.get_inputs() -\end{pythoncommand} -that will return -\begin{pythoncommand} -[0.98, ] -[0.99, ] -[1.0, ] -[1.1, ] -[1.2, ] -\end{pythoncommand} - -\subsubsection{Workfunction nesting} -One key advantage of workfunctions is that they can be nested, namely, a workfunction can invoke workfunctions inside its definition, and this ``call'' relationship will also be automatically recorded in the database. -As an example, let us combine the two previously defined workfunctions by means of a wrapper workfunction called ``create\_rescaled'' that takes as input the element and the rescale factor. -\begin{tcolorbox} -Type in your shell (or modify the functions defined in \texttt{create\_rescale.py} and then run): -\end{tcolorbox} -%%%%%%%%%%Call link -\begin{pythoncommand} -@wf -def create_rescaled(element, scale): - """ - Workfunction to create and immediately rescale - a crystal structure of a given element. - """ - s0 = create_diamond_fcc(element) - return rescale(s0,scale) -\end{pythoncommand} -and create an already rescaled structure by typing -\begin{pythoncommand} -s1 = create_rescaled(element=Str("Si"), scale=Float(0.98)) -\end{pythoncommand} -\begin{tcolorbox} -Now inspect the input links of \texttt{s1}: -\end{tcolorbox} -\begin{pythoncommand} -In [6]: s1.get_inputs() -Out[6]: -[, - , - ] -\end{pythoncommand} - -The object \texttt{s1} has three incoming links, corresponding to \emph{two} different calculations as input (in this case, pks 5002 and 5005). These correspond to the calculations ``create\_rescaled'' and ``rescale'' as shown in Fig.~\ref{Fig:workfunctions}b. -It is normal that calculation 5005 has two links, don't worry about that\footnote{If you are curious: the two links have the same label, but are of different \emph{link\_type}: one is a \textbf{create} link, that keeps track of the calculation that actually generated the node. Instead the other one is of type \textbf{return}, stating that the workfunction, beside creating that node, also returned it as an output. Calculation 5002 instead only returned the node but it did not generate it, therefore there is only one link between it and the final \texttt{StructureData}.}. -To see the ``call'' link, inspect now the outputs of the calculation appearing only once in the list. Write down its \texttt{} (in general, it will be different from 5002), then in the shell load the corresponding node and inspect the outputs: -\begin{pythoncommand} -In [12]: p1 = load_node() -In [13]: p1.get_outputs_dict() -\end{pythoncommand} -\begin{tcolorbox} -You should be able to identify the two ``children" calculations as well as the final structure (you will see the calculations linked via CALL links: these are calculation-to-calculation links representing the fact that \texttt{create\_rescaled} called two sub-workfunctions). The graphical representation of what you have in the database should match Fig.~\ref{Fig:workfunctions}b. -\end{tcolorbox} - -\subsection{\label{sec:sync} Run a simple workflow} - -Let us now use the workfunctions that we have just created to build a simple workflow to calculate the equation of state of silicon. We will consider five different values of the lattice parameter obtained rescaling the experimental minimum, $a=5.431~\text{\AA}$, by a factor in $[0.96, 0.98, 1.0, 1.02, 1.04]$. We will write a simple script that runs a series of five calculations and at the end returns the volume and the total energy corresponding to each value of the lattice parameter. For your convenience, besides the functions that you have written so far in the file \texttt{create\_rescale.py}, we provide you with some other utilities to get the correct pseudopotential and to generate a pw input file, in the module \texttt{common\_wf.py} which has been put in the \texttt{tutorial\_scripts} folder. - -\begin{tcolorbox} -We have already created the following script named \texttt{simple\_sync\_workflow.py}, which you are free to look at but please go through the lines carefully and make sure you understand them. -If you decide to create your own new script, make sure to also place it in the folder \texttt{tutorial\_scripts}, otherwise the imports won't work. -\end{tcolorbox} -Besides the functions in the local folder -\begin{pythoncommand} -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params -\end{pythoncommand} -you need to import few further AiiDA classes and functions: -\begin{pythoncommand} -from aiida.work import run, Process -from aiida.work import workfunction as wf -from aiida.orm.data.base import Str, Float -from aiida.orm import CalculationFactory, DataFactory -\end{pythoncommand} - -The only imported function that deserves an explanation is \texttt{run}. -For the time being, you just need to know that it is a function that needs to be used to execute a new workflow. -The actual body of the script is the following. -We suggest that you first have a careful look at it before running it. - -\begin{pythoncommand} -# Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' -PwCalculation = CalculationFactory('quantumespresso.pw') - -scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) -labels = ["c1", "c2", "c3", "c4", "c5"] - -@wf -def run_eos_wf(codename, pseudo_family, element): - print "Workfunction node identifiers: {}".format(Process.current().calc) - s0 = create_diamond_fcc(Str(element)) - - calcs = {} - for label, factor in zip(labels, scale_facs): - s = rescale(s0, Float(factor)) - inputs = generate_scf_input_params(s, str(codename), Str(pseudo_family)) - print "Running a scf for {} with scale factor {}".format(element, factor) - result = run(PwCalculation, **inputs) - print "RESULT: {}".format(result) - calcs[label] = get_info(result) - - eos = [] - for label in labels: - eos.append(calcs[label]) - - # Return information to plot the EOS - ParameterData = DataFactory("parameter") - retdict = { - 'initial_structure': s0, - 'result': ParameterData(dict={'eos_data': eos}) - } - - return retdict -\end{pythoncommand} - -If you look into the previous snippets of code, you will notice that the way we submit a QE calculation is slightly different from what you have seen in the first part of the tutorial. The following: -\begin{pythoncommand} -result = run(PwCalculation, **inputs) -\end{pythoncommand} -runs in the current python session (without the daemon), waits for its completion and returns the output in the user-defined variable \texttt{result}. -The latter is a dictionary whose values are the output nodes generated by the calculation, with the link labels as keys. -For example, once the calculation is finished, in order to access the total energy, we need to access the ParameterData node which is linked via the ``output\_parameters'' link (see again Fig.~1 of Day 1 Tutorial, to see inputs and outputs of a Quantum ESPRESSO calculation). -Once the right node is retrieved as \cmd{result[`output\_parameters']}, we need to get the \texttt{energy} attribute. The global operation is achieved by the command -\begin{pythoncommand} -result['output_parameters'].dict.energy -\end{pythoncommand} -As you see, the function \texttt{run\_eos\_wf} has been decorated as a workfunction to keep track of the provenance. -Finally, in order to get the \texttt{} associated to the workfunction (and print on the screen for our later reference), we have used the following command to get the node corresponding to the ProcessCalculation: -\begin{pythoncommand} -from aiida.work import Process -print Process.current().calc -\end{pythoncommand} - -To run the workflow it suffices to call the function \texttt{run\_eos\_wf} in a python script providing the required input parameters. For simplicity, we have included few lines at the end of the script that invoke the function with a static choice of parameters: - -\begin{pythoncommand} -def run_eos(codename='pw-5.1@localhost', pseudo_family='GBRV_lda', element="Si"): - return run_eos_wf(Str(codename), Str(pseudo_family), Str(element)) - -if __name__ == '__main__': - run_eos() -\end{pythoncommand} - -\begin{tcolorbox} -Run the workflow by running the following command from the \texttt{tutorial\_scripts} directory: -\begin{bashcommand} -verdi run simple_sync_workflow.py -\end{bashcommand} -and write down the \texttt{} of the ProcessCalculation printed on screen at execution. -\end{tcolorbox} - -The command above locks the shell until the full workflow has completed (we will see in a moment how to avoid this). -While the calculation is running, you can use (in a different shell) the command \cmd{verdi work list} to show ongoing and finished workfunctions. You can ``grep'' for the \texttt{} you are interested in. Additionally, you can use the command \cmd{verdi work status } to show the tree of the sub-workfunctions called by the root workfunction with a given \texttt{}. - -\begin{tcolorbox} -Wait for the calculation to finish, then call the function \cmd{plot\_eos()} that we provided in the file \texttt{common\_wf.py} to plot the equation of state and fit it with a Birch--Murnaghan equation. -\end{tcolorbox} - -\subsection{\label{sec:wf-multiple-calcs}Run multiple calculations} - -You should have noticed that the calculations for different lattice parameters are executed serially, although they might perfectly be executed in parallel because their inputs and outputs are not connected in any way. -In the language of workflows, these calculations are executed in a synchronous (or blocking) way, whereas we would like to have them running \emph{asynchronously} (i.e., in a non-blocking way, to run them in parallel). -One way to achieve this to submit the calculation to the daemon using the \cmd{submit} function. -Make a copy of the script \texttt{simple\_sync\_workflow.py} that we worked on in the previous section and name it \texttt{simple\_submit\_workflow.py}. -To make the new script work asynchronously, simply change the following subset of lines: -\begin{pythoncommand} -from aiida.work import run -[...] -for label, factor in zip(labels, scale_facs): - [...] - result = run(PwCalculation, **inputs) - calcs[label] = get_info(result) -[...] -eos = [] -for label in labels: - eos.append(calcs[label]) -\end{pythoncommand} -replacing them with -\begin{pythoncommand} -from aiida.work import submit -from time import sleep -[...] -for label, factor in zip(labels, scale_facs): - [...] - calcs[label] = submit(PwCalculation, **inputs) -[...] -# Wait for the calculations to finish -for calc in calcs.values(): - while not calc.is_finished: - sleep(1) - -eos = [] -for label in labels: - eos.append(get_info(calcs[label].get_outputs_dict())) -\end{pythoncommand} - -The main differences are: -\begin{itemize} - \item \cmd{run} is replaced by \cmd{submit} - \item The return value of \texttt{submit} is not a dictionary describing the outputs of the calculation, but it is the calculation node for that submission. - \item Each calculation starts in the background and calculation nodes are added to the \texttt{calc} dictionary. - \item At the end of the loop, when all calculations have been launched with \cmd{submit}, another loop is used to wait for all calculations to finish before gathering the results as the final step. - -\end{itemize} -In the next section we will show you another way to achieve this, which has the added bonus that it introduces checkpoints in the workfunction, from which the calculation can be resumed should it be interrupted. - -\begin{tcolorbox} -After applying the modifications, run the script. You will see that all calculations start at the same time, without waiting for the previous ones to finish. -\end{tcolorbox} -If in the meantime you run \cmd{verdi work status }, all five calculations are already shown as output. Also, if you run \cmd{verdi calculation list}, you will see how the calculations are submitted to the scheduler. - - -\subsection{\label{sec:workchainsimple}Workchains, or how not to get lost if your computer shuts down or crashes} -The simple workflows that we have used so far have been launched by a python script that needs to be running for the whole time of the execution, namely the time in which the calculations are submitted, and the actual time needed by Quantum ESPRESSO to perform the calculation and the time taken to retrieve the results. -If you had killed the main python process during this time, the workflow would not have terminated correctly. Perhaps you have kill the calculation and you experienced the unpleasant consequences: intermediate calculation results are potentially lost and it is extremely difficult to restart a workflow from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way to insert checkpoints, where the main code defining a workflow can be stopped (you can even shut down the machine on which AiiDA is running!). We call these workfunctions with checkpoints ``workchains'' because, as you will see, they basically amount to splitting a workfunction in a chain of steps. Each step is then ran by the daemon, in a way similar to the remote calculations. - -The basic rules that allow you to convert your workfunction-based script to a workchain-based one are listed in Table~\ref{Tab:wf2frag}, which focus on the code used to perform the calculation of an equation of state. The modifications needed are put side-to-side to allow for a direct comparison. In the following, when referencing a specific part of the code we will refer to the line number appearing in Table~\ref{Tab:wf2frag}. - -\begin{sidewaystable} -\caption{\label{Tab:wf2frag}Side-to-side comparison of the EOS workflow using standard workfunctions (left panel) or ``workchains'' (right panel).} -\scriptsize -\begin{tabular}{|c|c|} -\hline -{Workfunctions}& -{Workchains}\\ -\hline -{ -\begin{lstlisting}[language=Python,numbers=left,moredelim={[is][\color{gray}]{??}{??}}] -from aiida.work import submit, Process -# ... - - - - - - - - - - - - - -@wf -def run_eos_wf(codename, pseudo_family, element): - # ... - s0 = create_diamond_fcc(Str(element)) - - - calcs = {} - for label, factor in zip(labels, scale_facs): - s = rescale(s0,Float(factor)) - inputs = generate_scf_input_params( - s, str(codename), pseudo_family) - # ... - calcs[label] = submit(PwCalculation, **inputs) - - - # Wait for the calculations to finish - for calc in calcs.values(): - while not calc.is_finished: - sleep(1) - - eos = [] - for label in labels: - eos.append(get_info(calcs[label].get_outputs_dict())) - - #Return information to plot the EOS - ParameterData = DataFactory("parameter") - retdict = { - 'initial_structure': s0, - 'result': ParameterData(dict={'eos_data': eos}) - } - - return retdict - -\end{lstlisting} -} & -{ -\begin{lstlisting}[language=Python,numbers=left,moredelim={[is][\color{gray}]{??}{??}}] -from aiida.work.workchain import WorkChain, ToContext -# ... - -class EquationOfState(WorkChain): - @classmethod - def define(cls, spec): - super(EquationOfState, cls).define(spec) - spec.input('element', valid_type=Str) - spec.input('code', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.outline( - cls.run_pw, - cls.return_results, - ) - - - def run_pw(self): - # ... - self.ctx.s0 = create_diamond_fcc(Str(self.inputs.element)) - - - calcs = {} - for label, factor in zip(labels, scale_facs): - s = rescale(self.ctx.s0,Float(factor)) - inputs = generate_scf_input_params( - s, str(self.inputs.code), self.inputs.pseudo_family) - # ... - future = self.submit(PwCalculation, **inputs) - calcs[label] = future - - # Ask the workflow to continue when the results are ready - # and store them in the context - return ToContext(**calcs) - - def return_results(self): - eos = [] - for label in labels: - eos.append(get_info(self.ctx[label].get_outputs_dict())) - - # Return information to plot the EOS - ParameterData = DataFactory('parameter') - retdict = { - 'initial_structure': self.ctx.s0, - 'result': ParameterData(dict={'eos_data': eos}) - } - for link_name, node in retdict.iteritems(): - self.out(link_name, node) - -\end{lstlisting} -} -\\ -\hline -\end{tabular} -\end{sidewaystable} - - -\begin{itemize} - \item Instead of using decorated functions you need to define a class, inheriting from a prototype class called \cmd{WorkChain} that is provided by AiiDA (line 4) - % - \item Within your class you need to implement a \cmd{define} classmethod that always takes \texttt{cls} and \texttt{spec} as inputs. (lines 6--7). Here you specify the main information on the workchain, in particular: - \begin{itemize} - \item the \emph{inputs} that the workchain expects. This is obtained by means of the \text{spec.input()} method, which provides as the key feature the automatic validation of the input types via the \texttt{valid\_type} argument (lines 8--10). The same holds true for outputs, as you can use the \texttt{spec.output()} method to state what output types are expected to be returned by the workchain. Both \texttt{spec.input()} and \texttt{spec.output()} methods are optional, and if not specified, the workchain will accept any set of inputs and will not perform any check on the outputs, as long as the values are database storable AiiDA types. - % - \item the \cmd{outline} consisting in a list of ``steps'' that you want to run, put in the right sequence (lines 11--14). This is obtained by means of the method \cmd{spec.outline()} which takes as input the steps. \emph{Note}: in this example we just split the main execution in two sequential steps, that is, first \cmd{run\_pw} then \cmd{return\_results}. However, more complex logic is allowed, as will be explained in the Sec.~\ref{sec:convpressure}. - \end{itemize} - % - \item You need to split your main code into methods, with the names you specified before into the outline (\cmd{run\_pw} and \cmd{return\_results} in this example, lines 17 and 35). Where exactly should you split the code? Well, the splitting points should be put where you would normally block the execution of the script for collecting results in a standard workfunction, namely whenever you call the method \cmd{.result()}. Each method should accept only one parameter, \texttt{self}, e.g. \cmd{def step\_name(self)}. - % - \item You will notice that the methods reference the attribute \cmd{ctx} through \cmd{self.ctx}, which is called the \emph{context} and is inherited from the base class \texttt{WorkChain}. - A python function or workfunction normally just stores variables in the local scope of the function. - For instance, in the example of the subsection~\ref{sec:sync}, you stored the \cmd{calc\_results} in the \cmd{eos} list, that was a local variable. - In workchains, instead, to preserve variables between different steps, you need to store them in a special dictionary called \emph{context}. - As explained above, the context variable \cmd{ctx} is inherited from the base class \texttt{WorkChain}, and at each step method you just need to update its content. - AiiDA will take care of saving the context somewhere between workflow steps (on disk, in the database, \ldots{}, depending on how AiiDA was configured). - For your convenience, you can also access the value of a context variable as \cmd{self.ctx.varname} instead of \cmd{self.ctx['varname']} (see e.g. lines 19, 24, 38, 43). - % - \item Any submission within the workflow should not call the normal \cmd{run} or \cmd{submit} functions, but \cmd{self.submit} to which you have to pass the Process class, and a dictionary of inputs (line 28). - % - \item The submission in line 28, returns a future and not the actual calculation, because at that point in time we have only just launched the calculation to the daemon and it is not yet completed. Therefore it literally is a ``future'' result. Yet we still need to add these futures to the context, so that in the next step of the workchain, when the calculations are in fact completed, we can access them and continue the work. To do this, we can use the \texttt{ToContext} class. This class takes a dictionary, where the values are the futures and the keys will be the names under which the corresponding calculations will be made available in the context when they are done. See line 33 how the \texttt{ToContext} object is created and returned from the step. - By doing this, the workchain will implicitly wait for the results of all the futures you have specified, and then call the next step \emph{only when all futures have completed}. - % - \item \emph{Return values}: While in a normal workfunction you attach output nodes to the \texttt{FunctionCalculation} by invoking the \textit{return} statement, in a workchain you need to call \cmd{self.out(link\_name, node)} for each node you want to return (line 46-47). Of course, if you have already prepared a dictionary of outputs, you can just use the following syntax: -\begin{pythoncommand} -self.out_many(retdict) # Keys are link names, value the nodes -\end{pythoncommand} - - -The advantage of this different syntax is that you can start emitting output nodes already in the middle of the execution, and not necessarily at the very end as it happens for normal functions (\textit{return} is always the last instruction executed in a function). Also, note that once you have called \cmd{self.out(link\_name, node)} on a given \cmd{link\_name}, you can no longer call \cmd{self.out()} on the same \cmd{link\_name}: this will raise an exception. -\end{itemize} - -Inspect the example in the table that compares the two versions of workfunctions to understand in detail the different syntaxes. - -Finally, the workflow has to be run. For this you have to use the function \cmd{run} passing as arguments the \texttt{EquationOfState} class and the inputs as key-value arguments. For example, you can execute - -\begin{pythoncommand} - run(EquationOfState, element=Str('Si'), code=Str('qe-pw-6.2.1@localhost'), - pseudo_family=Str('GBRV_lda')) -\end{pythoncommand} - -While the workflow is running, you can check (in a different terminal) what is happening -to the calculations using \cmd{verdi calculation list}. You will see that after a few seconds the calculations are all submitted to the scheduler and can potentially run at the same time. - -\begin{tcolorbox} -\textbf{Note:} -You will see warnings that say \texttt{`Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint'}, these are generated by the \texttt{PwCalculation} which is unable to save a checkpoint because it is not in a so called `importable path'. Simply put this means that if AiiDA were to try and reload the class it wouldn't know which file to find it in.\\ -To get around this you could simply put the workchain in a different file that is in the `PYTHONPATH' and then launch it by importing it in your launch file, this way AiiDA knows where to find it next time it loads the checkpoint. -\end{tcolorbox} - -As an additional exercise (optional), instead of running the main workflow (\texttt{EquationOfState}), -try to submit it. Note that the file where the WorkChain is defined will need to be globally importable (so the daemon knows how to load it) and you need to launch it (with \cmd{submit}) from a different python file. The easiest way to achieve this is typically to embed the workflow inside a python package. - -\begin{tcolorbox} -\textbf{Note:} -As good practice, you should try to keep the steps as short as possible in term of execution time. The reason is that the daemon can be stopped and restarted only between execution steps and not if a step is in the middle of a long execution. -\end{tcolorbox} - -Finally, as an optional exercise if you have time, you can jump to the Appendix~\ref{sec:convpressure}, which shows how to introduce more complex logic into your WorkChains (if conditionals, while loops etc.). The exercise will show how to realize a convergence loop to obtain the minimum-volume structure in a EOS using the Newton's algorithm. diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/7_bandstructure.tex b/docs/assets/2018_PRACE_MaX/latex/sections/7_bandstructure.tex deleted file mode 100644 index dfef5ccf..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/7_bandstructure.tex +++ /dev/null @@ -1,46 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex - -\section{\label{sec:workchain_demonstration}WorkChains, a real-world example: computing a band structure for a simple crystal structure} - -\textbf{Note}: \emph{If you still have enough time, you might want to check first Appendix~\ref{sec:convpressure} before continuing with this section.} - -As a final demonstration of the power of WorkChains in AiiDA, we want to give a demonstration of a WorkChain that we have written that will take a structure as its only input and will compute its band structure. -All of the steps that would normally have to be done manually by the researcher, choosing appropriate pseudopotentials, energy cutoffs, k-points meshes, high-symmetry k-point paths and performing the various calculation steps, are performed automatically by the WorkChain. - -The demonstration of the workchain will be performed in a Jupyter notebook. -To run it, follow the instructions that were given for the querybuilder notebook in section \ref{sec:querybuilder}. -The only difference is that instead of selecting the notebook in the \texttt{querybuilder} directory, go to \texttt{pw/bandstructure} instead and choose the \texttt{bandstructure.ipynb} notebook. -There you will find some example structures that are loaded from COD, through the importer integrated within AiiDA. -Note that the required time to calculate the bandstructure for these example structures ranges from 3 minutes to almost half an hour, given that the virtual machine is running on a single core with minimal computational power. -It is not necessary to run these examples as it may take too long to complete. -For reference, the expected output band structures are plotted in Fig.\,\ref{fig:workchain_band_structures}. - -\begin{figure} -\begin{subfigure}{.5\textwidth} - \centering - \includegraphics[width=.9\linewidth]{sections/images/bandstructures/Al_bands.pdf} - \caption{Al} - \label{fig:workchain_band_structures_Al} -\end{subfigure}% -\begin{subfigure}{.5\textwidth} - \centering - \includegraphics[width=.9\linewidth]{sections/images/bandstructures/GaAs_bands.pdf} - \caption{GaAs} - \label{fig:workchain_band_structures_GaAs} -\end{subfigure}% -\newline -\begin{subfigure}{.5\textwidth} - \centering - \includegraphics[width=.9\linewidth]{sections/images/bandstructures/CaF2_bands.pdf} - \caption{CaF$_2$} - \label{fig:workchain_band_structures_CaF2} -\end{subfigure}% -\begin{subfigure}{.5\textwidth} - \centering - \includegraphics[width=.9\linewidth]{sections/images/bandstructures/hBN_bands.pdf} - \caption{$h$-BN} - \label{fig:workchain_band_structures_hBN} -\end{subfigure}% -\caption{Electronic band structures of four different crystal structures computed with AiiDA's PwBandsWorkChain} -\label{fig:workchain_band_structures} -\end{figure} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/8_computer_code_setup.tex b/docs/assets/2018_PRACE_MaX/latex/sections/8_computer_code_setup.tex deleted file mode 100644 index 8a684b46..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/8_computer_code_setup.tex +++ /dev/null @@ -1,484 +0,0 @@ -% !TEX root = ../AiiDA_tutorial_addition.tex -%%%%%%%%%%% -%Various Installation and setup procedures -%%%%%%%%%%% - - -\section[Installation and Setup]{Various installation and setup procedures} -\subsection{\label{sec:codesetup}Setting up a new computer and code in AiiDA: NWChem} - -To be able to run a calculation, AiiDA needs to know certain information such as which computer the code can be found on and what the executable name is. In the main tutorial, for instance, you have been using an existing Quantum ESPRESSO pw code. -You can check its name calling -\begin{bashcommand} -verdi code list -\end{bashcommand} -and then inpsect its properties using -\begin{bashcommand} -verdi code show -\end{bashcommand} - -\begin{tcolorbox} -\textbf{Exercises:} -\begin{itemize} -\item -Setup a new code (for the simulation package NWChem) -\end{itemize} -\end{tcolorbox} - -To learn how to setup a new code, let us configure AiiDA to be able to run NWChem, an open source computational chemistry package. First we can check which calculation plugins are available: - -\begin{bashcommand} -verdi calculation plugins -\end{bashcommand} - -You will see an entry called \texttt{nwchem.pymatgen}. This plugin creates the input and parses the output files and acts as the interface between AiiDA and NWChem (the parsing part is delegated to the pymatgen code, which explains the name of the plugin). To configure the code type: - -\begin{bashcommand} -verdi code setup -\end{bashcommand} - -Provide the following details to tell AiiDA about the code (if you want to learn more details on the \texttt{verdi code setup} command, refer to Appendix~\ref{app:codesetup} at page \pageref{app:codesetup}): - -\begin{bashcommand} -=> Label: NWChem -=> Description: NWChem computational chemistry code -=> Local: False -=> Default input plugin: nwchem.pymatgen -=> Remote computer name: localhost -=> Remote absolute path: /usr/bin/nwchem -=> Text to prepend to each command execution -[Press CTRL+D for the next two steps] -\end{bashcommand} - -If you want to inspect the details of the code you just generated, you can run -\begin{bashcommand} -verdi code show NWChem@localhost -\end{bashcommand} - -\textbf{Note} Similarly, also new computers (e.g., a new cluster) can be configured using the \texttt{verdi computer setup} command, as we will see in Sec.~\ref{sec:computersetuptask}. - -\subsubsection*{Task (optional, if you want to use the code you just created)} - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item Run a simple simulation with the code NWChem -\end{itemize} -\end{tcolorbox} - - -Use the following \texttt{ParametersData} dict to run an NWChem calculation on silicon carbide: - -\begin{pythoncommand} -parameters = ParameterData(dict={ - 'directives': [ - ['set nwpw:minimizer', '2'], - ['set nwpw:psi_nolattice', '.true.'], - ['set includestress', '.true.'] - ], - 'geometry_options': [ - 'units', - 'au', - 'center', - 'noautosym', - 'noautoz', - 'print' - ], - 'memory_options': [], - 'symmetry_options': [], - 'tasks': [ - { - 'alternate_directives': { - 'driver': {'clear': '', 'maxiter': 40}, - 'nwpw': {'ewald_ncut': 8, - 'simulation_cell': '\n ngrid 16 16 16\n end'} - }, - 'basis_set': {}, - 'basis_set_option': 'cartesian', - 'charge': 0, - 'operation': 'optimize', - 'spin_multiplicity': None, - 'theory': 'pspw', - 'theory_directives': {}, - 'title': None - } - ], - 'add_cell': True -}) -\end{pythoncommand} - -and we can use an ASE \texttt{Atoms} object to create the structure like so: - -\begin{pythoncommand} -from ase import Atoms -asestruc = Atoms(['Si', 'Si', 'Si' ,'Si', 'C', 'C', 'C', 'C'], - cell=[8.277, 8.277, 8.277]) -asestruc.set_scaled_positions([ - (-0.5, -0.5, -0.5), - (0.0, 0.0, -0.5), - (0.0, -0.5, 0.0), - (-0.5, 0.0, 0.0), - (-0.25, -0.25, -0.25), - (0.25 ,0.25 ,-0.25), - (0.25, -0.25, 0.25), - (-0.25 ,0.25 ,0.25), -]) -structure = StructureData(ase=asestruc) -\end{pythoncommand} - -The procedure is the same as before with QuantumEspresso except for fetching the NWCode. This should take about a minute to run. - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item Inspect the results. -\end{itemize} -\end{tcolorbox} - -Once the calculation is done you can retrieve the calculation node using the PK from \texttt{verdi calculation list -a -p1}. After loading the node have a look at \texttt{node.out.trajectory}. This stores information about the positions of the atoms at each relaxation steps. You can for instance see how the cell evolved by printing -\cmd{node.out.trajectory.get\_cells()}, and how the positions changed with -\cmd{node.out.trajectory.get\_positions()}. - - -\section{\label{sec:computersetuptask}Setup a new computer} -\subsection{Setting up the computer in the database} -Until now, we just had on computer configured in the test machine, called \texttt{localhost}. To see all computers configured, you can use -\begin{bashcommand} -verdi computer list -\end{bashcommand} - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item Install a new computer (the test machine of your neighbor). -\item Check if the installation was successful. -\end{itemize} -\end{tcolorbox} - -\subsubsection*{Preliminary steps} -\begin{itemize} -\item As a first step, choose a computer you have. You can try to setup this machine. -If you don't have one, or you want to try a tested setup, you can setup the same computer on which -you are running the tests, but this time not with a local transport, but via SSH, as an example. The instructions below are for this use case. We will call the computer \texttt{localhost-ssh}. - -\item Generate a new ssh key pair, without passphrase: -\begin{verbatim} - ssh-keygen -\end{verbatim} -and press enter when asked. -Copy the content of the \texttt{$\sim$/.ssh/id\_rsa.pub} file that was created and -paste it as a new line into the file \texttt{$\sim$/.ssh/authorized\_keys}. \textbf{IMPORTANT!} -Append it to the file, without modifying the current content, otherwise you will -not be able to ssh into the machine again. - -\item Check that you can connect to the computer: from the shell, run \texttt{ssh localhost} and check that you can connect. Then, logout (only once to close the ssh session; -you should remain on the tutorial machine). -\end{itemize} - -Now you are ready to setup the computer. - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item Run the command \texttt{verdi computer setup} to install the computer called \texttt{localhost-ssh}. See below for some hints. -\end{itemize} -\end{tcolorbox} - -Hints: -\begin{itemize} -\item Read appendix~\ref{app:computersetup} for an explanation of the various items you will be asked for -\item Type \texttt{localhost} when you are asked for the fully-qualified hostname -\item As transport, use SSH; as scheduler, write \texttt{torque} -\item As work directory, you can use \texttt{/home/\{username\}/.aiida\_run} -\item As MPI run command, the default value should be OK: \texttt{mpirun -np \{tot\_num\_mpiprocs\}} -\item set only 2 CPU per machine for this tutorial -\item Leave empty the next two prepend/append strings (you need to press CTRL+D as indicated) -\end{itemize} - -\subsection{Configuring the computer connection credentials} - -You can now type \texttt{verdi computer list -a} to see that you computer is now present (\texttt{-a} to show all computers, also the unconfigured ones and the ones not setup by you). - -You will notice that your computer is marked as ``unconfigured'' (and you might see also other unconfigured computers, coming with the import data that we used in earlier parts of this tutorial). Indeed, the command \cmd{verdi computer setup} just creates a reference for the computer in your database. Something similar also happens when you import data. However, if you want to run simulations with a computer, you need also to specify the credentials of how to connect to it. - -To do this, you need to run -\begin{bashcommand} -verdi computer configure COMPUTERNAME -\end{bashcommand} - -\begin{tcolorbox} -\textbf{Exercise:} -\begin{itemize} -\item Configure your computer with the above command (see hints below) -\item Check if AiiDA can connect to the computer -\end{itemize} -\end{tcolorbox} - -Hints: -\begin{itemize} -\item If you properly set up the \texttt{$\sim$/.ssh/config} file in the step before, AiiDA will read that information and all parameters will be already set by default. You just need to press Enter. -\item Still, try to understand each item---you can refer to Appendix~\ref{sec:computerconfigure} for a detailed explanation of each item. -\end{itemize} - -When you are done, you can test if everything worked by running -\begin{bashcommand} -verdi computer test COMPUTERNAME -\end{bashcommand} -If everything worked, The last line should read something like ``Test completed (all tests succeeded)''. - -\subsection{Final useful commands} -To get information on a specific computer, run: -\begin{bashcommand} -verdi computer show COMPUTERNAME -\end{bashcommand} - -It is also possible to enable/disable a computer with the command -\begin{bashcommand} -verdi computer enable COMPUTERNAME -verdi computer disable COMPUTERNAME -\end{bashcommand} -The last two commands might be useful when new resources might -or not be available (e.g. during maintenance), -so that you can still submit calculations to -AiiDA, but AiiDA will not try to connect to the computer until when -you enable the computer again. - -\begin{tcolorbox} -\textbf{Exercise (optional):} -\begin{itemize} -\item Configure the Quantum ESPRESSO code as explained in Sec.~\ref{sec:codesetup}. -To know the \texttt{pw.x} executable path, that you will need during the -code configuration, you can find it in the output of \texttt{verdi code show CODENAME} for the code that was already setup for you and that you used during the tutorial (on the machine \texttt{localhost} that uses a \texttt{local} transport -- you are instead -setting up the same code but on the \texttt{localhost-ssh} machine, that uses a \texttt{ssh} transport). -\item Try to run a simulation with this code, to see if it works, as explained in the main tutorial. -\end{itemize} -\end{tcolorbox} - - - - - -\begin{appendices} -\section{\label{app:computersetup}Options when setting up and configuring a computer} -\subsection{\texttt{verdi computer setup}} -Here is a list of what is asked during the execution of \cmd{verdi computer setup}, together with an explanation of each item. - -\begin{itemize} - -\item {} -\textbf{Computer name}: the (user-friendly) name of the new computer instance -which is about to be created in the DB (the name is used for instance when -you have to pick up a computer to launch a calculation on it). Names must -be unique. This command should be thought as a AiiDA-wise configuration of -computer, independent of the AiiDA user that will actually use it. - -\item {} -\textbf{Fully-qualified hostname}: the fully-qualified hostname of the computer -to which you want to connect. - -\item {} -\textbf{Description}: A human-readable description of this computer; this is -useful if you have a lot of computers and you want to add some text to -distinguish them (e.g.: ``cluster of computers at EPFL, with 434 nodes, 2 GB of RAM per CPU'') - -\item {} -\textbf{Enabled}: either True or False; if False, the computer is disabled -and calculations associated with it will not be submitted. This allows to -disable temporarily a computer if it is giving problems or it is down for -maintenance, without the need to delete it from the DB. - -\item {} -\textbf{Transport type}: The name of the transport to be used. A list of valid -transport types can be obtained typing \textit{?} - -\item {} -\textbf{Scheduler type}: The name of the plugin to be used to manage the -job scheduler on the computer. A list of valid -scheduler plugins can be obtained typing \textit{?}. - -\item {} -\textbf{shebang line at the beginning of the submission script}: -The first line of any submission script. The default is \texttt{\#!/bin/bash}. -This is often used by the scheduler to decide which shell to use. AiiDA expects -this to be bash, but some supercomputer need a login shell, and then you need -to specify \texttt{\#!/bin/bash -l}. - -\item {} -\textbf{AiiDA work directory}: The absolute path of the directory on the -remote computer where AiiDA will run the calculations -(often, it is the scratch of the computer). You can (should) use the -\textit{\{username\}} replacement, that will be replaced by your username on the -remote computer automatically: this allows the same computer to be used -by different users, without the need to setup a different computer for -each one. - -\item {} -\textbf{mpirun command}: The \cmd{mpirun} command needed on the cluster to run parallel MPI -programs. You can (should) use the \textit{\{tot\_num\_mpiprocs\}} replacement, -that will be replaced by the total number of cpus, or the other -scheduler-dependent fields. - -\item {} -\textbf{Default number of CPUs}: Setup the number of CPUs per node of the cluster. In this way, if you specify to use one node (a 'machine' in AiiDA), the calculation will try by default to use all the CPUs of the node. - -\item {} -\textbf{Text to prepend to each command execution}: This is a multiline string, -whose content will be prepended inside the submission script before the -real execution of the job. It is your responsibility to write proper \cmd{bash} code! -This is intended for computer-dependent code, like for instance loading a -module that should always be loaded on that specific computer. \emph{Remember} -\emph{to end the input by pressing} \textit{\textless{}CTRL\textgreater{}+D}. - -\item {} -\textbf{Text to append to each command execution}: This is a multiline string, -whose content will be appended inside the submission script after the -real execution of the job. It is your responsibility to write proper \cmd{bash} code! -This is intended for computer-dependent code. \emph{Remember} -\emph{to end the input by pressing} \textit{\textless{}CTRL\textgreater{}+D}. - -\end{itemize} - -\subsection{\label{sec:computerconfigure}\texttt{verdi computer configure}} -To setup the credentials to be able to run on a computer, you should use the following command: -\begin{bashcommand} -verdi computer configure COMPUTERNAME -\end{bashcommand} - -When using the SSH transport, the command will try to provide automatically default answers, mainly reading -the existing ssh configuration in \textbf{\textasciitilde{}/.ssh/config}, and in most cases one -simply need to press enter a few times. - -At the moment, the in-line help (i.e., just typing \textbf{?} to get -some help) is not yet supported in \cmd{verdi configure}, but only in -\cmd{verdi setup}. - -The following options will be asked for ssh transport type. - -\begin{itemize} -\item {} -\textbf{username}: your username on the remote machine - -\item {} -\textbf{port}: the port to connect to (the default SSH port is 22) - -\item {} -\textbf{look\_for\_keys}: automatically look for the private key in \textbf{\textasciitilde{}/.ssh}. -Default: True. - -\item {} -\textbf{key\_filename}: the absolute path to your private SSH key. You can leave -it empty to use the default SSH key, if you set \textbf{look\_for\_keys} to True. - -\item {} -\textbf{timeout}: A timeout in seconds if there is no response (e.g., the -machine is down). You can leave it empty to use the default value. - -\item {} -\textbf{allow\_agent}: If True, it will try to use an SSH agent. - -\item {} -\textbf{proxy\_command}: Leave empty if you do not need a proxy command (i.e., -if you can directly connect to the machine). - -\item {} -\textbf{compress}: True to compress the traffic (recommended) - -\item {} -\textbf{gss\_auth, gss\_kex, gss\_deleg\_creds, gss\_host}: These are parameters needed if your cluster requires authentication via Kerberos. If you don't know what Kerberos is, just ignore these four variables and leave their value to the default one (in particular, \texttt{gss\_auth = no}). Otherwise, read the documentation of AiiDA (and of the paramiko python package) online for more details. - -\item {} -\textbf{load\_system\_host\_keys}: True to load the known hosts keys from the -default SSH location (recommended) - -\item {} -\textbf{key\_policy}: What is the policy in case the host is not known. -It is a string among the following: -\begin{itemize} -\item {} -\textbf{RejectPolicy} (default, recommended): reject the connection if the -host is not known. - -\item {} -\textbf{WarningPolicy} (\emph{not} recommended): issue a warning if the -host is not known. - -\item {} -\textbf{AutoAddPolicy} (\emph{not} recommended): automatically add the host key -at the first connection to the host. - -\end{itemize} - -\end{itemize} - -\section{\label{app:codesetup}Setting up a code: \texttt{verdi code setup} options} - -Once you have at least one computer configured, you can configure the codes, using the command \cmd{verdi code setup}. - -In AiiDA, for full reproducibility of each calculation, we store each code in the database, and attach to each calculation a given code. This has the further advantage to make it very easy to query for all calculations that were run with a given code (for instance because I am looking for phonon calculations, or because I discovered that a specific version had a bug and I want to rerun the calculations). - -In AiiDA there are two types of codes: - -\begin{itemize} - \item \textbf{Remote codes} are installed on a remote computer like a supercomputer. - \item \textbf{Local codes} are not already present on the remote machine and must be copied for every submission. -\end{itemize} - -The command \cmd{verdi code setup} will ask several values. Here is a description of their meaning. - -\begin{itemize} -\item {} -\textbf{label}: A label to refer to this code. Note: this label is not enforced -to be unique. However, if you try to keep it unique, you can use it later -to refer and use to your code. Otherwise, you need to remember its ID or UUID. - -\item {} -\textbf{description}: A human-readable description of this code (for instance ``Quantum -Espresso v.5.0.2 with 5.0.3 patches, pw.x code, compiled with openmpi'') - -\item {} -\textbf{default input plugin}: A string that identifies the default input plugin to -used to generate new calculations to use with this code. -This string has to be a valid string recognised by the \textbf{CalculationFactory} -function. To get the list of all available Calculation plugin strings, -use the \cmd{verdi calculation plugins} command. Note: if you do not want to -specify a default input plugin, you can write the string ``None'', but this is -discouraged, because then you will not be able to use -the \textbf{.get\_builder()} method of the \textbf{Code} object. - -\item {} -\textbf{local}: either True (for local codes) or False (for remote -codes). For the meaning of the distinction, see above. Depending -on your choice, you will be asked for: -\begin{itemize} -\item {} -LOCAL CODES: -\begin{itemize} -\item {} -\textbf{Folder with the code}: The folder on your local computer in which there -are the files to be stored in the AiiDA repository, and that will then be -copied over to the remote computers for every submitted calculation. -This must be an absolute path on your computer. - -\item {} -\textbf{Relative path of the executable}: The relative path of the executable -file inside the folder entered in the previous step. - -\end{itemize} - -\item {} -REMOTE CODES: -\begin{itemize} -\item {} -\textbf{Remote computer name}: The computer name as on which the code resides, -as configured and stored in the AiiDA database - -\item {} -\textbf{Remote absolute path}: The (full) absolute path of the code executable -on the remote machine - -\end{itemize} - -\end{itemize} - -\end{itemize} -\end{appendices} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/appendix_AB.tex b/docs/assets/2018_PRACE_MaX/latex/sections/appendix_AB.tex deleted file mode 100644 index 45e2b9cf..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/appendix_AB.tex +++ /dev/null @@ -1,157 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -\section{Calculation input validation} -This appendix shows additional ways to debug possible errors with QE, how to use a useful tool that we included in AiiDA to validate the input to Quantum ESPRESSO (and possibly suggest the correct name to mispelled keywords) - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. -Let's check for example this input dictionary, where we inserted two mistakes: -\begin{pythoncommand} -parameters_dict = { - "CTRL": { - "calculation": "scf", - "restart_mode": "from_scratch", - }, - "SYSTEM": { - "nat": 2, - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } -} -\end{pythoncommand} - -The two mistakes in the dictionary are the following. -First, we wrote a wrong namelist name ('CTRL' instead of 'CONTROL'). -Second, we inserted the number of atoms explicitly: while that is how the number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system: in fact this information is already contained in the StructureData. Modify the script with this ParameterData. -In this case, we use a tool (the input validator that we provide in AiiDA) to check the input file before submitting. Therefore, the behavior will be slightly different from the previous example: the plugin will check for some keys and will refuse to submit the calculation. -This kind of mistakes is revealed by either -\begin{itemize} -\item Submitting a calculation. You will see the calculation ending up in the SUBMISSIONFAILED status. Check therefore the logs to recognize the source of the error. -\item Submitting a test. You will not be able to successfully create the test and the traceback will guide you to the problem. -\end{itemize} - - -Over the time this kind of trivial mistakes can be annoying, but they can be avoided with a utility function that checks the ``grammar'' of the input parameters. -In your script, after you defined the parameters\_dict, you can validate it with the command -(note that you need to pass also the input crystal structure, \texttt{s}, to -allow the validator to perform all needed checks): - -\begin{pythoncommand} -PwCalculation = CalculationFactory('quantumespresso.pw') -validated_dict = PwCalculation.input_helper(parameters_dict, structure=s) -parameters = ParameterData(dict=validated_dict) -\end{pythoncommand} - -The \texttt{input\_helper} method will check for the correctness of the input parameters. -If misspelling or incorrect keys are detected, the method raises an exception, which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, without providing the namelists (useful if you don't remember where to put some variables in the input file). -Hence, you can provide to \texttt{input\_helper()} a dictionary like this one: -\begin{pythoncommand} -parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, -} -validated_dict = PwCalculation.input_helper( - parameters_dict, structure=s, flat_mode=True) -\end{pythoncommand} -If you print the \texttt{validated\_dict}, it will look like: -\begin{pythoncommand} -{ - "CONTROL": { - "calculation": "scf", - "tstress": True, - "tprnfor": True, - }, - "SYSTEM": { - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } - } -\end{pythoncommand} - - - -\section{Restarting calculations} -Up to now, we have only presented cases in which we were passing wrong input parameters to the calculations, which required us to modify the input scripts and relaunch calculations from scratch. -There are several other scenarios in which, more generally, we need to restart calculations from the last step that they have executed. -For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to restart and/or modify a calculation that has run previously. As an example, let us first submit a total energy calculation using a parameters dictionary of the form: -\begin{pythoncommand} -parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, -} -\end{pythoncommand} -and submit the calculation with this input. -In this case, we set a very low number of self consistent iterations (3), too small to be able to reach the desired accuracy of 10$^{-14}$: therefore the calculation will not reach a complete end and will be flagged in a FAILED state. However, there is no mistake in the parameter dictionary. - -Now, create a new script file, where you will try to restart and correct the input dictionary. -We first load the calculation that has just failed (let's call it \texttt{c1}) -\begin{pythoncommand} -old_calc = load_node(PK) -\end{pythoncommand} -(take care of using the correct PK). -Then, create a new Builder \texttt{builder} which is set to reuse all inputs -from the previous step, with a few adaptations to the input parameters -that might be needed by the code to properly deal with restarts. -\begin{pythoncommand} -from aiida_quantumespresso.utils.restart import create_restart_pw -builder = create_restart_pw( - old_calc, - use_output_structure=False, - restart_from_beginning=False, - force_restart=True) -\end{pythoncommand} -The flag usage (most of them are optional) is: -\begin{itemize} - \item \texttt{use\_output\_structure}: if True and \texttt{old\_calc} has an output structure, the new calculation will use it as input; - \item \texttt{restart\_from\_beginning}: if False the new calculation will start from the charge density of \texttt{old\_calc}, it will start from the beginning otherwise; - \item \texttt{force\_restart}: if True, the new calculation will be created even - if \texttt{old\_calc} is not in a FINISHED job state. -\end{itemize} - -Since this calculation has exactly the same parameters of before, we have to modify the input parameters and increase \texttt{electron\_maxstep} to a larger value. -To this aim, let's load the dictionary of values and change it -\begin{pythoncommand} -old_parameters = builder.parameters -parameters_dict = old_parameters.get_dict() -parameters_dict['ELECTRONS']['electron_maxstep'] = 100 -\end{pythoncommand} -Note that you cannot modify the \texttt{old\_parameters} object: it has been used by calculation \texttt{c1} and is saved in the database; hence a modification would break the provenance. -We have to create a new ParameterData and pass it to c2: -\begin{pythoncommand} -ParameterData = DataFactory('parameter') -new_parameters = ParameterData(dict=parameters_dict) -builder.parameters = new_parameters -\end{pythoncommand} -Now you can launch the new calculation -\begin{pythoncommand} -from aiida.work.run import submit -new_calc = submit(builder) -print new_calc.pk -\end{pythoncommand} -that this time can proceed until the end and return converged total energy. -Using the restart method, the script is much shorter than the one needed to launch a new one from scratch: you didn't need to define pseudopotentials, structures and k-points, which are the same as before. -You can indeed inspect the new calculation to check that now it actually -completed successfully. \ No newline at end of file diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/appendix_CD.tex b/docs/assets/2018_PRACE_MaX/latex/sections/appendix_CD.tex deleted file mode 100644 index b6fd21b9..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/appendix_CD.tex +++ /dev/null @@ -1,159 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -\section{Queries in AiiDA - Optional examples and exercises} - - -\subsection*{Optional exercises on relationships (Task 3)} -\textbf{Hint for the exercises:} -\begin{itemize} -\item You can have projections on properties of more than one entity in your query. You just have to add the \emph{project} key (specifying the list of properties that you want to project) along with the corresponding entity when you append it. -\end{itemize} - -\begin{tcolorbox} -\textbf{Exercises:}\\~\\ -Try to write the following queries: -\begin{itemize} - \item Find all descendants of a StructureData with a certain uuid. Print both the StructureData and the descendant. - \item Find all the FolderData created by a specific user. -\end{itemize} -\end{tcolorbox} - -\subsection*{Optional exercises on attributes and extras (Task 4)} -\textbf{Hint for the exercises:} -\begin{itemize} -\item You can easily order or limit the number of the results by using the \emph{order\_by()} and \emph{limit()} methods of the QueryBuilder. For example, we can order all our job calculation by their \textit{id} and limit the result to the first 10 as follows: -\begin{pythoncommand} -qb = QueryBuilder() -qb.append(JobCalculation, tag='calc') -qb.order_by({'calc':'id'}) -qb.limit(10) -qb.all() -\end{pythoncommand} -\end{itemize} - -\begin{tcolorbox} -\textbf{Exercises:} -\begin{itemize} - \item Write a code snippet that informs you how many pseudopotentials you have for each element. - \item Smearing contribution to the total energy for calculations: - \begin{enumerate} - \item Write a query that returns the smearing contribution to the energy stored in some instances of ParameterData. - \item Extend the previous query to also get the input structures. - \item Which structures have a smearing contribution to the energy smaller or equal to -0.02? - \end{enumerate} -\end{itemize} -\end{tcolorbox} - -\subsection*{Summarizing what we learned by now - An example} -At this point you should be able to do queries with projections, joins and filters. Moreover, you saw how to apply filters and projections even on attributes and extras. Let's discover the full power of the QueryBuilder with a complex graph query that allows you to project various properties from different nodes and apply different filters and joins. - -Imagine that you would like to get the smearing energy for all the calculations that have finished and have a $\mathrm{Sn_{2}O_{3}}$ as input. Moreover, besides from the smearing energy, you would like to print the units of this energy and the formula of the structure that was given to the calculation. -The graphical representation of this query can be seen in Figure~\ref{fig:qb2} and the actual query follows: -\begin{figure}[!th] -\begin{center} -\includegraphics[width=7cm]{img/qb_example_2.png} -\end{center} -\caption{Complex graph query.} -\label{fig:qb2} -\end{figure} - -\begin{pythoncommand} -qb = QueryBuilder() -qb.append( - StructureData, - project=["extras.formula"], - filters={"extras.formula":"Sn2O3"}, - tag="structure" - ) -qb.append( - Calculation, - tag="calculation", - output_of="structure" - ) -qb.append( - ParameterData, - tag="results", - filters={"attributes.energy_smearing":{"<=":-0.0001}}, - project=[ - "attributes.energy_smearing", - "attributes.energy_smearing_units", - ], - output_of="calculation" -) -qb.all() -\end{pythoncommand} - -\section{\label{sec:convpressure}More complex logic in workflows: while loops and conditional statements} -In the previous sections, you have been introduced to WorkChains, and the reason for using them over ``standard'' workfunctions (i.e., functions decorated with \cmd{@wf}). - -However, in the example of Sec.~\ref{sec:workchainsimple}, the \cmd{spec.outline} was quite simple, with a ``static'' sequence of two steps. -Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). -To support this scenario, the \cmd{spec.outline} can support logic: \emph{while} loops and \emph{if/elif/else} blocks. -The simplest way to explain it is to show an example: -\begin{pythoncommand} -from aiida.work.workchain import if_, while_ - -spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), -) -\end{pythoncommand} -that would \emph{roughly} correspond, in a python syntax, to: -\begin{pythoncommand} -s1() -if isA(): - s2() -elif isB(): - s3() -else: - s4() -s5() -while condition(): - s6() -\end{pythoncommand} -The only constraint is that condition functions (in the example above \cmd{isA}, \cmd{isB} and \cmd{condition}) must be class methods that returns \texttt{True} or \texttt{False} depending on whether the condition is met or not. - -A suggestion on how to write new workchains: Use the outline to help you in designing the logic. First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. Then, define one by one the methods. -For example, we have prepared a simple workfunction to optimize the lattice parameter of silicon efficiently using a Newton's algorithm on the energy derivative, i.e. the pressure $p=-dE/dV$. You can find it the code at \texttt{tutorial\_scripts/pressure\_convergence.py}. -The outline looks like this: -\begin{pythoncommand} -spec.outline( - cls.init, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.report -) -\end{pythoncommand} -This outline already roughly explains the algorithm: after an initialization (\cmd{init}) and putting the first step (number zero) in the ctx (\cmd{put\_step0\_in\_ctx}), a function to move to the next step is called (\cmd{move\_next\_step}). This is iterated while a given convergence criterion is not met (\cmd{not\_converged}). Finally, some reporting is done, including returning some output nodes (\cmd{report}). - -If you are interested in the details of the algorithm, you can inspect the file. The main ideas are described here: -\begin{description} -\item[init] Generate a \texttt{pw.x} calculation for the input structure (with volume $V$), and one for a structure where the volume is $V+4\text{\AA}^3$ (just to get a closeby volume). Store the results in the context as \cmd{r0} and \cmd{r1} -\item[put\_step0\_in\_ctx] Store in the context $V$, $E(V)$ and $dE/dV$ for the first calculation \cmd{r0} -\item[move\_next\_step] This is the most important function. Calculate $V$, $E(V)$ and $dE/dV$ for \cmd{r1}. Also, estimate $d^2E/dV^2$ from the finite difference of the first derivative of \cmd{r0} and \cmd{r1} (helper functions to achieve this are provided). -Get the $a$, $b$ and $c$ coefficients of a parabolic fit $E=aV^2 + bV + c$ and estimated the expected minimum of the EOS function as the minimum of the fit $V_0=-b/2a$. Finally, replace \cmd{r0} with \cmd{r1} in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume $V_0$, that will be stored in the context replacing \cmd{r1}. In this way, at the next iteration \cmd{r0} and \cmd{r1} will contain the latest two simulations. Finally, at each step some relevant information (coefficients $a$, $b$ and $c$, volumes, energies, energy derivatives, ...) are stored in a list called \cmd{steps}. This whole list is stored in the context because it provides quantities to be preserved between different workfunction steps. -\item[not\_converged] Return \cmd{True} if convergence has not been achieved yet. Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold (\cmd{volume\_tolerance}). -\item[report] This is the final step. Mainly, we return the output nodes: \cmd{steps} with the list of results at each step, and \cmd{structure} with the final converged structure. -\end{description} - -The results returned in \cmd{steps} can be used to represent the evolution of the minimisation algorithm. A possible way to visualize it is presented in Fig.~\ref{fig:convpressure}, obtained with an initial lattice constant of $a_{\text{lat}} = 5.2\text{\AA}$. -%You can try to reproduce the same graph by running the WorkChain and then using the script \texttt{tutorial\_scripts/plot\_convergence\_pressure.py} to generate the same plot. - -\begin{figure}[tb] -\centering\includegraphics[width=0.6\linewidth]{img/convergence_pressure} -\caption{\label{fig:convpressure}Example of results of the convergence algorithm presented in Sec.~\ref{sec:convpressure}. The bottom plot is a zoom near the minimum. The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step.} -\end{figure} - - diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/biblio.tex b/docs/assets/2018_PRACE_MaX/latex/sections/biblio.tex deleted file mode 100644 index 8996e005..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/biblio.tex +++ /dev/null @@ -1,18 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex - -\begin{thebibliography}{9} -% -\bibitem{ref:QE} -P. Giannozzi et al., J.Phys. Cond. Matt. 29, 465901 (2017). -\bibitem{ref:ASE} -S. R. Bahn and K. W. Jacobsen, %``An object-oriented scripting interface to a legacy electronic structure code'' -Comput. Sci. Eng., 4, 56-66 (2002). -\bibitem{ref:pymatgen} S. Ping Ong et al., %Python Materials Genomics (pymatgen) : A Robust, Open-Source Python Library for Materials Analysis. -Comput. Mater. Sci. 68, 314-319 (2013). %doi:10.1016/j.commatsci.2012.10.028 -\bibitem{ref:GBRV} -K.F. Garrity, J.W. Bennett, K.M. Rabe and D. Vanderbilt, Comput. Mater. Sci. 81, 446 (2014). -\bibitem{ref:SSSP} -G. Prandini, A. Marrazzo, I. E. Castelli, N. Mounet, N. Marzari, A Standard Solid State Pseudopotentials (SSSP) library optimized for accuracy and efficiency (Version 1.0, data download), Materials Cloud Archive (2018), \href{http://doi.org/10.24435/materialscloud:2018.0001/v1}{doi:10.24435/materialscloud:2018.0001/v1}. -\bibitem{ref:COD} -Crystallographic Open Database ({COD}), \href{http://www.crystallography.net/cod/}{http://www.crystallography.net/cod/}. -\end{thebibliography} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/custom_specifications.tex b/docs/assets/2018_PRACE_MaX/latex/sections/custom_specifications.tex deleted file mode 100644 index dbaba483..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/custom_specifications.tex +++ /dev/null @@ -1,34 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex - -\iftoggle{online}{ - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Version for online tutorial with virtual machine - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - % Do not show first section 'how to connect' - \newif \ifsshpreparation \sshpreparationfalse - - % Strings - \newcommand{\aiidatutorialtitle}{AiiDA tutorial} - \newcommand{\aiidatutorialdate}{May--June 2018} - \newcommand{\aiidatutorialauthors}{The AiiDA Team} - - \newcommand{\aiidaouterheader}{\aiidatutorialtitle} - \newcommand{\aiidainnerheader}{\aiidatutorialdate} -} -{ - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Version for real-life tutorial with people - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - % Show first section 'how to connect' - \newif \ifsshpreparation \sshpreparationtrue - - % Strings - \newcommand{\aiidaouterheader}{AiiDA tutorial} - \newcommand{\aiidainnerheader}{Cineca, Italy, May 30--June 1, 2018} - \newcommand{\aiidatutorialtitle}{PRACE--MaX Tutorial on high-throughput computations:\\ General methods and applications using AiiDA} - \newcommand{\aiidatutorialdate}{Cineca, Italy, May 30--June 1, 2018} - \newcommand{\aiidatutorialauthors}{Instructors: Sebastiaan Huber, Leonid Kahle, Giovanni Pizzi,\\Martin Uhrin, Spyros Zoupanos} - -} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/defs-private.sty b/docs/assets/2018_PRACE_MaX/latex/sections/defs-private.sty deleted file mode 100644 index c778d919..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/defs-private.sty +++ /dev/null @@ -1,24 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex - -\renewcommand{\headrulewidth}{0.5pt} -\renewcommand{\plainheadrulewidth}{0pt} -%\renewcommand{\footrulewidth}{0.5pt} -\renewcommand{\footrulewidth}{0.pt} -\renewcommand{\plainfootrulewidth}{0pt} - -\newcommand{\cmd}[1]{\texttt{#1}} - -\newenvironment{pythoncommand} -{\VerbatimEnvironment% -\begin{Verbatim}[frame=leftline]} -{\end{Verbatim}} - -\newenvironment{bashcommandinner} -{\VerbatimEnvironment% -\begin{Verbatim}[frame=lines]} -{\end{Verbatim}} - -\newenvironment{bashcommand} -{\VerbatimEnvironment% -\begin{center}\begin{minipage}{\linewidth}\begin{bashcommandinner}} -{\end{bashcommandinner}\end{minipage}\end{center}} diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/Al_bands.pdf b/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/Al_bands.pdf deleted file mode 100644 index fbf8020c..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/Al_bands.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/CaF2_bands.pdf b/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/CaF2_bands.pdf deleted file mode 100644 index 6e5bbec4..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/CaF2_bands.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/GaAs_bands.pdf b/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/GaAs_bands.pdf deleted file mode 100644 index f85b148f..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/GaAs_bands.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/hBN_bands.pdf b/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/hBN_bands.pdf deleted file mode 100644 index ca0ea353..00000000 Binary files a/docs/assets/2018_PRACE_MaX/latex/sections/images/bandstructures/hBN_bands.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/main_body.tex b/docs/assets/2018_PRACE_MaX/latex/sections/main_body.tex deleted file mode 100644 index fbebb13c..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/main_body.tex +++ /dev/null @@ -1,38 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex -\maketitle - -{\setcounter{tocdepth}{1} \tableofcontents} - -\section{Preliminaries} - -\ifsshpreparation -%Intro on SSH-ing to Amazon machines -\input{sections/1_1_ssh_connection.tex} -\fi -\input{sections/1_2_preparation.tex} - -% Day1 -\input{sections/2_verdi_graphs.tex} -\input{sections/3_verdi_shell_and_orm.tex} -\input{sections/4_qe_submission.tex} - -% Day2 -%\iftoggle{online} { -%\input{sections/5_querybuilder.tex} -%}{ -\input{sections/5_querybuilder_jupyter.tex} -%} -\input{sections/6_workflows.tex} -\input{sections/7_bandstructure.tex} - -% Not included -%\input{sections/8_computer_code_setup.tex} - -\begin{appendices} -\phantomsection -\addcontentsline{toc}{part}{Appendices}\part*{Appendices} -\emph{The following appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.} -\input{sections/appendix_AB.tex} -\input{sections/appendix_CD.tex} -\end{appendices} - diff --git a/docs/assets/2018_PRACE_MaX/latex/sections/preamble.tex b/docs/assets/2018_PRACE_MaX/latex/sections/preamble.tex deleted file mode 100644 index 3790c26f..00000000 --- a/docs/assets/2018_PRACE_MaX/latex/sections/preamble.tex +++ /dev/null @@ -1,79 +0,0 @@ -% !TEX root = ../AiiDA_tutorial.tex - -\usepackage[T1]{fontenc} % Use type 1 (vector) fonts -\usepackage{upquote} -\usepackage{ae,aecompl} -\usepackage{amsmath,amssymb} -\usepackage{graphicx}% Include figure files -% \usepackage{subfig} -\usepackage{hyperref} -\usepackage{xcolor} -\usepackage{url} -\usepackage{dcolumn}% Align table columns on decimal point -\usepackage{bm}% bold math -\usepackage[left=1cm,right=1cm,top=1.9cm,bottom=1.5cm]{geometry} -\usepackage{fancyhdr} -\usepackage{fancyvrb} -\usepackage{titling} -\usepackage{tcolorbox} -\usepackage{listings} -%\usepackage{lscape} -\usepackage{rotating} -\usepackage{appendix} -\usepackage{subcaption} - - -\usepackage{caption} -\captionsetup[figure]{margin=20pt,font=footnotesize,labelfont=bf,labelsep=endash} - -\usepackage{cleveref} % Clever references, needs to come after hyperref - -% Define some colours I want to use throughout -\definecolor{mygreen}{rgb}{0,0.6,0} -\definecolor{mygray}{rgb}{0.5,0.5,0.5} -\definecolor{mymauve}{rgb}{0.58,0,0.82} -\definecolor{orange}{rgb}{1,0.6,0} -\definecolor{blue}{rgb}{0,0,1} - -\definecolor{acolour}{HTML}{E66101} -\definecolor{bcolour}{HTML}{B2ABD2} -\definecolor{ccolour}{HTML}{5E3C99} - -\definecolor{linkcolor}{HTML}{2222EE} -\definecolor{urlcolor}{HTML}{2222EE} - -\pagestyle{fancy} -\fancyhf{} -\setlength\headheight{15pt} -\fancyhead[LE,RO]{\fancyplain{}{\small\textsf{\aiidaouterheader}}} -\fancyhead[RE,LO]{\fancyplain{}{\small\textsf{\aiidainnerheader}}} -%\fancyhead[RO,LE]{\fancyplain{}{\textsc{\leftmark}}} -%\fancyhead[RE,LO]{\fancyplain{}{\textsc{\rightmark}}} -\fancyfoot[C]{\textbf{\thepage}} - -\title{\aiidatutorialtitle} - -\date{\aiidatutorialdate} % Deleting this command produces today's date -\author{\aiidatutorialauthors} - -% Set up link colours -\hypersetup{ - unicode=true, % non-Latin characters in Acrobat’s bookmarks - pdftoolbar=true, % show Acrobat’s toolbar? - pdfmenubar=true, % show Acrobat’s menu? - pdffitwindow=false, % window fit to page when opened - pdfstartview={FitH}, % fits the width of the page to the window - pdftitle={AiiDA tutorial}, % title - pdfauthor={The AiiDA Team}, % author - pdfsubject={}, % subject of the document - pdfcreator={The AiiDA Team}, % creator of the document - pdfproducer={The AiiDA Team}, % producer of the document - pdfkeywords={}, % list of keywords - pdfnewwindow=true, % links in new PDF window - colorlinks=true, -% colorlinks=false, % false: boxed links; true: colored links - linkcolor=linkcolor, % color of internal links (change box color with linkbordercolor) - citecolor=acolour, % color of links to bibliography - filecolor=magenta, % color of file links - urlcolor=urlcolor % color of external links -} diff --git a/docs/assets/2018_PRACE_MaX/magnetization_smearing_perovskites.pdf b/docs/assets/2018_PRACE_MaX/magnetization_smearing_perovskites.pdf deleted file mode 100644 index 73196a87..00000000 Binary files a/docs/assets/2018_PRACE_MaX/magnetization_smearing_perovskites.pdf and /dev/null differ diff --git a/docs/assets/2018_PRACE_MaX/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps b/docs/assets/2018_PRACE_MaX/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps deleted file mode 100644 index 6e8fd379..00000000 --- a/docs/assets/2018_PRACE_MaX/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps +++ /dev/null @@ -1,6953 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Title: /home/mounet/Documents/PYTHON/AiiDA_scripts/my_first_scripts/magnetization_vs_perovskites_allpseudo_Gaussian_0p02.eps -%%Creator: matplotlib version 1.1.1rc, http://matplotlib.sourceforge.net/ -%%CreationDate: Wed Oct 29 08:48:08 2014 -%%Orientation: portrait -%%BoundingBox: -528 -63 1140 855 -%%EndComments -%%BeginProlog -/mpldict 8 dict def -mpldict begin -/m { moveto } bind def -/l { lineto } bind def -/r { rlineto } bind def -/c { curveto } bind def -/cl { closepath } bind def -/box { -m -1 index 0 r -0 exch r -neg 0 r -cl -} bind def -/clipbox { -box -clip -newpath -} bind def -%!PS-Adobe-3.0 Resource-Font -%%Title: DejaVu Sans -%%Copyright: Copyright (c) 2003 by Bitstream, Inc. 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b/docs/assets/2020_Oxford/example-gaas-wannier.aiida deleted file mode 100644 index f6714e4f..00000000 Binary files a/docs/assets/2020_Oxford/example-gaas-wannier.aiida and /dev/null differ diff --git a/docs/assets/css/style.scss b/docs/assets/css/style.scss deleted file mode 100644 index 5234200a..00000000 --- a/docs/assets/css/style.scss +++ /dev/null @@ -1,37 +0,0 @@ ---- ---- - -@import "{{ site.theme }}"; - -body{counter-reset: section} - -// Based on https://stackoverflow.com/a/535390/1069467 -// Rewrite only in main_content_wrap div -//#main_content_wrap { -#disabled { - - h2{counter-reset: sub-section} - h3{counter-reset: composite} - h4{counter-reset: detail} - - h2:before{ - counter-increment: section; - content: counter(section) " "; - } - h3:before{ - counter-increment: sub-section; - content: counter(section) "." counter(sub-section) " "; - } - h4:before{ - counter-increment: composite; - content: counter(section) "." counter(sub-section) "." counter(composite) " "; - } - h5:before{ - counter-increment: detail; - content: counter(section) "." counter(sub-section) "." counter(composite) "." counter(detail) " "; - } -} - -div.language-python pre { - background-color: #c3c3c3; -} diff --git a/docs/assets/images/MARVEL.png b/docs/assets/images/MARVEL.png deleted file mode 100644 index d17a6eda..00000000 Binary files a/docs/assets/images/MARVEL.png and /dev/null differ diff --git a/docs/assets/images/MaX.png b/docs/assets/images/MaX.png deleted file mode 100644 index 44fdbfb3..00000000 Binary files a/docs/assets/images/MaX.png and /dev/null differ diff --git a/docs/conf.py b/docs/conf.py index 6506a8b7..1cf53d9a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,49 +17,79 @@ import os import subprocess +import pathlib # -- General configuration ----------------------------------------------------- +# Load the prolog +prolog_path = "prolog.inc" +with pathlib.Path(prolog_path).open() as prologFile: + rst_prolog = prologFile.read() + # If your documentation needs a minimal Sphinx version, state it here. -needs_sphinx = '2.0.0' +needs_sphinx = "2.0.0" # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ - 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.todo', - 'sphinx.ext.ifconfig', 'sphinx.ext.intersphinx', 'sphinx.ext.viewcode', - 'IPython.sphinxext.ipython_console_highlighting', - 'IPython.sphinxext.ipython_directive', 'myst_nb', 'sphinx.ext.extlinks', - 'sphinx.ext.mathjax', 'sphinx_copybutton', 'sphinx_panels' + "sphinx.ext.autodoc", + "sphinx.ext.doctest", + "sphinx.ext.todo", + "sphinx.ext.ifconfig", + "sphinx.ext.intersphinx", + "sphinx.ext.viewcode", + "IPython.sphinxext.ipython_console_highlighting", + "IPython.sphinxext.ipython_directive", + "myst_nb", + "sphinx.ext.extlinks", + "sphinx.ext.mathjax", + "sphinx_copybutton", + "sphinx_panels", + "sphinx_tabs.tabs", +] + +myst_enable_extensions = [ + # "amsmath", + "colon_fence", # Allow ::: to define directives + # "deflist", + # "dollarmath", + # "html_admonition", + # "html_image", + # "linkify", + # "replacements", + # "smartquotes", + # "substitution", + # "tasklist", ] + ipython_mplbackend = "" -copybutton_selector = 'div:not(.no-copy)>div.highlight pre' -copybutton_prompt_text = '>>> |\\\\$ |In \\\\[\\\\d+\\\\]: |\\\\s+\\.\\.\\.: ' +copybutton_selector = "div:not(.no-copy)>div.highlight pre" +copybutton_prompt_text = ">>> |\\\\$ |In \\\\[\\\\d+\\\\]: |\\\\s+\\.\\.\\.: " copybutton_prompt_is_regexp = True todo_include_todos = True extlinks = { - 'doi': ('https://doi.org/%s', 'doi:'), + "doi": ("https://doi.org/%s", "doi:"), } # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The encoding of source files. -#source_encoding = 'utf-8-sig' +# source_encoding = 'utf-8-sig' # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'AiiDA Tutorials' +project = u"AiiDA Tutorials" # pylint: disable=redefined-builtin -copyright = u'\ +copyright = u"\ 2019, ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (Theory and Simulation of Materials (THEOS) \ and National Centre for Computational Design and Discovery of Novel Materials (NCCR MARVEL)), Switzerland \ -and ROBERT BOSCH LLC, USA. All rights reserved' +and ROBERT BOSCH LLC, USA. All rights reserved" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -74,187 +104,198 @@ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. -#language = None +# language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: -#today = '' +# today = '' # Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' +# today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = [ - 'build/*', - 'assets/*', - '**/.ipynb_checkpoints/', - 'pages/*/notebooks/*-solutions.ipynb', - 'pages/*/notebooks/*-template.ipynb', - 'pages/2019_*/notebooks/bandstructure.ipynb', - 'pages/*/notebooks/*-solutions.md', - 'pages/*/notebooks/*-template.md', - 'pages/2020_*/notebooks/bandstructure.md', -] +exclude_patterns = ["build/*", "assets/*", "**/.ipynb_checkpoints/"] # The reST default role (used for this markup: `text`) to use for all documents. -#default_role = None +# default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True +# add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). -#add_module_names = True +# add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. -#show_authors = False +# show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] +# modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_book_theme" +html_static_path = ["_static"] +html_css_files = [ + "aiida-tutor-custom.css", + "panels-custom.css", +] +html_theme_options = { + "home_page_in_toc": True, + "repository_url": "https://github.com/aiidateam/aiida-pseudo", + "repository_branch": "master", + "use_repository_button": True, + "use_issues_button": True, + "path_to_docs": "docs", + "use_edit_page_button": True, + "extra_navbar": "", +} +html_domain_indices = True +html_title = "AiiDA Tutorials" +html_logo = "_static/logo.png" # Enable labeling for figures numfig = True +panels_add_bootstrap_css = False # + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -#html_theme_options = { +# html_theme_options = { # "navigation_depth": 5, -#} +# } # Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] +# html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". -#html_title = None +# html_title = None # A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None +# html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. -#html_logo = None +# html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. -#html_favicon = None +# html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' +# html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. -#html_use_smartypants = True +# html_use_smartypants = True # Custom sidebar templates, maps document names to template names. -#html_sidebars = {} +# html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. -#html_additional_pages = {} +# html_additional_pages = {} # If false, no module index is generated. -#html_domain_indices = True +# html_domain_indices = True # If false, no index is generated. -#html_use_index = True +# html_use_index = True # If true, the index is split into individual pages for each letter. -#html_split_index = False +# html_split_index = False # If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True +# html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True +# html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True +# html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. -#html_use_opensearch = '' +# html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None +# html_file_suffix = None # Output file base name for HTML help builder. # htmlhelp_basename = 'aiidadoc' # -- Options for LaTeX output -------------------------------------------------- -latex_engine = 'xelatex' +latex_engine = "xelatex" latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - 'preamble': - r''' + "preamble": r""" \usepackage{amsmath,amsfonts,amssymb,amsthm} \usepackage{braket} \usepackage{newunicodechar} \newunicodechar{⏹}{\ensuremath{\square}} -''', +""", } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ - ('index', 'aiida-tutorials.tex', u'AiiDA Tutorials', - author.replace(',', r'\and'), 'manual'), + ( + "index", + "aiida-tutorials.tex", + u"AiiDA Tutorials", + author.replace(",", r"\and"), + "manual", + ), ] # The name of an image file (relative to this directory) to place at the top of # the title page. -#latex_logo = None +# latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. -#latex_use_parts = False +# latex_use_parts = False # If true, show page references after internal links. -#latex_show_pagerefs = False +# latex_show_pagerefs = False # If true, show URL addresses after external links. -#latex_show_urls = False +# latex_show_urls = False # Documents to append as an appendix to all manuals. -#latex_appendices = [] +# latex_appendices = [] # If false, no module index is generated. -#latex_domain_indices = True +# latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [('index', 'aiida', u'AiiDA Tutorials', [author], 1)] +man_pages = [("index", "aiida", u"AiiDA Tutorials", [author], 1)] # If true, show URL addresses after external links. -#man_show_urls = False +# man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ @@ -262,81 +303,88 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - ('index', 'aiida-tutorials', u'AiiDA Tutorials', author, 'aiida-tutorials', - 'Tutorials and demos for AiiDA', 'Miscellaneous'), + ( + "index", + "aiida-tutorials", + u"AiiDA Tutorials", + author, + "aiida-tutorials", + "Tutorials and demos for AiiDA", + "Miscellaneous", + ), ] # Documents to append as an appendix to all manuals. -#texinfo_appendices = [] +# texinfo_appendices = [] # If false, no module index is generated. -#texinfo_domain_indices = True +# texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' +# texinfo_show_urls = 'footnote' # -- Options for Epub output --------------------------------------------------- # Bibliographic Dublin Core info. -epub_title = u'AiiDA Tutorials' +epub_title = u"AiiDA Tutorials" epub_author = author epub_publisher = author epub_copyright = copyright # The language of the text. It defaults to the language option # or en if the language is not set. -#epub_language = '' +# epub_language = '' # The scheme of the identifier. Typical schemes are ISBN or URL. -#epub_scheme = '' +# epub_scheme = '' # The unique identifier of the text. This can be a ISBN number # or the project homepage. -#epub_identifier = '' +# epub_identifier = '' # A unique identification for the text. -#epub_uid = '' +# epub_uid = '' # A tuple containing the cover image and cover page html template filenames. -#epub_cover = () +# epub_cover = () # HTML files that should be inserted before the pages created by sphinx. # The format is a list of tuples containing the path and title. -#epub_pre_files = [] +# epub_pre_files = [] # HTML files shat should be inserted after the pages created by sphinx. # The format is a list of tuples containing the path and title. -#epub_post_files = [] +# epub_post_files = [] # A list of files that should not be packed into the epub file. -#epub_exclude_files = [] +# epub_exclude_files = [] # The depth of the table of contents in toc.ncx. -#epub_tocdepth = 3 +# epub_tocdepth = 3 # Allow duplicate toc entries. -#epub_tocdup = True +# epub_tocdup = True # otherwise, readthedocs.org uses their theme by default, so no need # to specify it # Warnings to ignore when using the -n (nitpicky) option # We should ignore any python built-in exception, for instance -nitpick_ignore = [('py:class', 'Warning'), ('py:class', 'exceptions.Warning')] +nitpick_ignore = [("py:class", "Warning"), ("py:class", "exceptions.Warning")] -#for line in open('nitpick-exceptions'): +# for line in open('nitpick-exceptions'): # if line.strip() == "" or line.startswith("#"): # continue # dtype, target = line.split(None, 1) # target = target.strip() # nitpick_ignore.append((dtype, target)) -suppress_warnings = ['misc.highlighting_failure'] +suppress_warnings = ["misc.highlighting_failure"] def setup(app): """Setup function called by sphinx.""" - app.add_stylesheet('css/custom.css') + app.add_stylesheet("css/custom.css") # We are not installing a full aiida environment @@ -344,18 +392,18 @@ def setup(app): # Intersphinx configuration intersphinx_mapping = { - 'aiida': ('http://aiida-core.readthedocs.io/en/latest/', None), - 'plumpy': ('https://plumpy.readthedocs.io/en/latest/', None) + "aiida": ("http://aiida-core.readthedocs.io/en/latest/", None), + "plumpy": ("https://plumpy.readthedocs.io/en/latest/", None), } # Compile all things needed before building the docs # For instance, convert the notebook templates to actual tutorial and solution versions print( - subprocess.check_output([ - 'make', '-C', - os.path.dirname(os.path.realpath(__file__)), 'pre-docs' - ], - universal_newlines=True)) + subprocess.check_output( + ["make", "-C", os.path.dirname(os.path.realpath(__file__)), "pre-docs"], + universal_newlines=True, + ) +) nb_render_priority = { "gettext": ( diff --git a/docs/index.rst b/docs/index.rst index 1e78cb05..b1263e6a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,7 +1,8 @@ AiiDA Tutorials =============== -The official home of AiiDA tutorial materials and videos. +Welcome to the official home of the AiiDA tutorial materials and videos. +The tutorials are organised by topic in *sections*, which are listed in the left sidebar or at the bottom of this page. .. panels:: :body: bg-light text-center @@ -9,77 +10,52 @@ The official home of AiiDA tutorial materials and videos. ------ :column: col-lg-12 pl-0 pr-0 - .. link-button:: 2020_Intro_Week_Homepage + .. link-button:: sections/fundamentals/index :type: ref - :text: In-Depth: 2020 Introductory Virtual tutorial + :text: Getting started: AiiDA fundamentals :classes: btn-link btn-block stretched-link ------- :column: col-lg-12 pl-0 pr-0 - :download:`AiiDA Cheat Sheet ` ------- :column: col-lg-12 pl-0 pr-0 :body: + font-weight-bold - Short Demonstrations + Previously organised tutorials ------- :column: col-lg-6 col-xs-12 pl-0 pr-0 - .. link-button:: https://aiida-tutorials.readthedocs.io/en/tutorial-qe-short/ + .. link-button:: https://aiida-tutorials.readthedocs.io/en/tutorial-2020-intro-week/ :type: url - :text: Quantum ESPRESSO introductory tutorial + :text: In-Depth: 2020 Introductory Virtual tutorial :classes: btn-link btn-block stretched-link ------- :column: col-lg-6 col-xs-12 pl-0 pr-0 - .. link-button:: Oxford 2020 Homepage - :type: ref - :text: Wannier90: "Virtual Edition" 2020 tutorial + .. link-button:: https://aiida-tutorials.readthedocs.io/en/tutorial-qe-short/ + :type: url + :text: Quantum ESPRESSO introductory tutorial :classes: btn-link btn-block stretched-link - Videos ------ For some events we have also recorded the lectures, that you can find here. You will also find the links inside the respective sections. -.. Without this, the default list is indented more than the toctree list -.. rst-class:: toctree-l1 - + * `2020 AiiDA virtual tutorial (aiida-core 1.3.0) `_ * `2019 tutorial lectures (aiida-core 1.0.0b3) `_ (`mirror `_) * `2019 plugin migration workshop lectures (aiida-core 1.0.0b2) `_ - * `2017 tutorial lectures (aiida-core 0.9.0) `_ - * `2017 short demo videos (aiida-core 0.8.0) `_ - * `2016 tutorial lectures (aiida-core 0.6.0) `_ - -.. toctree:: - :maxdepth: 1 - :caption: Tutorial materials - - 2020, BIG-MAP meeting AiiDA tutorial (aiida-core 1.4.3)<./pages/2020_BIGMAP/index> - 2020, Introductory workshop Virtual Edition (aiida-core 1.3.0)<./pages/2020_Intro_Week/index> - 2020, Wannier workshop Virtual Edition (aiida-core 1.1.1)<./pages/2020_Oxford/index> - 2019, ISSP University of Tokyo, Chiba, Japan (aiida-core 1.0.1)<./pages/2019_ISSP_Chiba_Japan/index> - 2019, IIT Mandi, Mandi, India (aiida-core 1.0.0b6)<./pages/2019_IIT_Mandi_India/index> - 2019, SINTEF, Oslo, Norway (aiida-core 1.0.0b6)<./pages/2019_SINTEF/index> - 2019, Jožef Stefan Institute, Ljubljana, Slovenia (aiida-core 1.0.0b6)<./pages/2019_Ljubljana/index> - 2019, Xiamen University, Xiamen, China (aiida-core 1.0.0b6)<./pages/2019_Xiamen/index> - 2019, EPFL, Lausanne, Switzerland (aiida-core 1.0.0b3)<./pages/2019_MARVEL_Psik_MaX/index> - 2019, University of Amsterdam, Amsterdam, Netherlands (aiida-core 0.12.2)<./pages/2019_molsim_school_Amsterdam/index> - 2018, Cineca, Bologna, Italy (aiida-core 1.0.0a1)<./pages/2018_PRACE_MaX/index> - 2018, EPFL, Lausanne, Switzerland (aiida-core 0.11.4)<./pages/2018_EPFL_molsim/index> - 2017, EPFL, Lausanne, Switzerland (aiida-core 0.9.0)<./pages/2017_MARVEL_Psik_MaX/index> - 2017, ICTP, Trieste, Italy (aiida-core 0.7.1)<./pages/2017_ICTP/index> - 2016, EPFL, Lausanne, Switzerland (aiida-core 0.6.0)<./pages/2016_MARVEL_Psik_MaX/index> - -.. toctree:: - :maxdepth: 1 - :caption: Interactive demos - - interactive notebooks, binderized (aiida-core 0.12.3) - interactive notebooks (aiida-core 0.10.0) + + .. toctree:: + :hidden: + + sections/fundamentals/index + sections/calculations/index + sections/data/index + sections/workflows/index + sections/plugins/index diff --git a/docs/pages/2016_MARVEL_Psik_MaX/index.rst b/docs/pages/2016_MARVEL_Psik_MaX/index.rst deleted file mode 100644 index 16dfefb7..00000000 --- a/docs/pages/2016_MARVEL_Psik_MaX/index.rst +++ /dev/null @@ -1,27 +0,0 @@ -+-----------------+-----------------------------------------------------+ -| Related resources | -+=================+=====================================================+ -| Virtual Machine | `AiiDA tutorial VM`_ (`installation instructions`_) | -+-----------------+-----------------------------------------------------+ -| python packages | `aiida-core 0.6.0`_ | -+-----------------+-----------------------------------------------------+ -| codes | `Quantum Espresso 5.1`_ | -+-----------------+-----------------------------------------------------+ -| videos | `recorded lectures`_ | -+-----------------+-----------------------------------------------------+ - -.. _AiiDA tutorial VM: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/AiiDA_tutorial_2016_07.ova -.. _installation instructions: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/AiiDA_tutorial_2016_07_instructions.pdf -.. _aiida-core 0.6.0: https://github.com/aiidateam/aiida_core/releases/tag/tutorial_2016_06_lausanne -.. _Quantum Espresso 5.1: https://gitlab.com/QEF/q-e/-/tags/qe-5.1.0 -.. _recorded lectures: https://www.youtube.com/watch?v=qUNYEWClS2U&list=PL19kfLn4sO_86ruxDP-sxbRXMuOr0VInR&index=4 - -The `MARVEL/Psi-k/MaX Tutorial on high-throughput computations: general methods -and applications using AiiDA -`_ took place on June -22-24, 2016 at EPFL (Lausanne, Switzerland). - -1\ :sup:`st` MARVEL/Psi-k/MaX tutorial on high-throughput computations -====================================================================== - -Please download the `tutorial instructions `_ (pdf format). diff --git a/docs/pages/2017_ICTP/index.rst b/docs/pages/2017_ICTP/index.rst deleted file mode 100644 index 6f6c56a0..00000000 --- a/docs/pages/2017_ICTP/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -+-----------------+-----------------------------------------------------+ -| Related resources | -+=================+=====================================================+ -| Virtual Machine | `AiiDA tutorial VM`_ (`installation instructions`_) | -+-----------------+-----------------------------------------------------+ -| python packages | `aiida-core 0.7.1`_ | -+-----------------+-----------------------------------------------------+ -| codes | `Quantum Espresso 5.1`_ | -+-----------------+-----------------------------------------------------+ - -.. _AiiDA tutorial VM: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/aiida_tutorial_2017_01.ova -.. _installation instructions: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/aiida_tutorial_2017_01_instructions.pdf -.. _aiida-core 0.7.1: https://github.com/aiidateam/aiida_core/releases/tag/tutorial_2017_01_trieste -.. _Quantum Espresso 5.1: https://gitlab.com/QEF/q-e/-/tags/qe-5.1.0 - -The `eigth AiiDA hands-on tutorial `_ took place on January 24-25, 2017 at ICTP (Trieste, Italy). - -AiiDA hands-on tutorial -======================= - -Please download the `tutorial instructions `_ (pdf format). diff --git a/docs/pages/2017_MARVEL_Psik_MaX/index.rst b/docs/pages/2017_MARVEL_Psik_MaX/index.rst deleted file mode 100644 index 6fbc52de..00000000 --- a/docs/pages/2017_MARVEL_Psik_MaX/index.rst +++ /dev/null @@ -1,27 +0,0 @@ -+-----------------+-----------------------------------------------------+ -| Related resources | -+=================+=====================================================+ -| Virtual Machine | `AiiDA tutorial VM`_ (`installation instructions`_) | -+-----------------+-----------------------------------------------------+ -| python packages | `aiida-core 0.9.0`_ | -+-----------------+-----------------------------------------------------+ -| codes | `Quantum Espresso 5.1`_ | -+-----------------+-----------------------------------------------------+ -| videos | `recorded lectures`_ | -+-----------------+-----------------------------------------------------+ - -.. _AiiDA tutorial VM: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/aiida_tutorial_2017_06.ova -.. _installation instructions: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/aiida_tutorial_2017_06_instructions.pdf -.. _aiida-core 0.9.0: https://pypi.org/project/aiida-core/0.9.0/ -.. _Quantum Espresso 5.1: https://gitlab.com/QEF/q-e/-/tags/qe-5.1.0 -.. _recorded lectures: https://www.youtube.com/watch?v=qD2IcaFV4Io&list=PL19kfLn4sO_86ruxDP-sxbRXMuOr0VInR - -The second edition of the `MARVEL/Psi-k/MaX Tutorial on high-throughput -computations: general methods and applications using AiiDA -`_ took place on May -29-31, 2017 at EPFL (Lausanne, Switzerland). - -2\ :sup:`nd` MARVEL/Psi-k/MaX tutorial on high-throughput computations -====================================================================== - -Please download the `tutorial instructions `_ (pdf format). diff --git a/docs/pages/2018_EPFL_molsim/index.rst b/docs/pages/2018_EPFL_molsim/index.rst deleted file mode 100644 index 46698a55..00000000 --- a/docs/pages/2018_EPFL_molsim/index.rst +++ /dev/null @@ -1,79 +0,0 @@ -+-----------------+------------------------------------------------------------------+ -| Related resources | -+=================+==================================================================+ -| Virtual Machine | `Quantum Mobile 18.04.0`_ | -+-----------------+------------------------------------------------------------------+ -| python packages | `aiida-core 0.11.4`_, `aiida-zeopp 0.1.0`_, `aiida-raspa 0.2.1`_ | -+-----------------+------------------------------------------------------------------+ -| codes | `zeo++ 0.3`_, `raspa 2.0.30`_ | -+-----------------+------------------------------------------------------------------+ - -.. _Quantum Mobile 18.04.0: https://github.com/marvel-nccr/quantum-mobile/releases/tag/18.04.0 -.. _aiida-core 0.11.4: https://pypi.org/project/aiida-core/0.11.4/ -.. _aiida-zeopp 0.1.0: https://pypi.org/project/aiida-zeopp/0.1.0/ -.. _aiida-raspa 0.2.1: https://pypi.org/project/aiida-raspa/0.2.1/ -.. _zeo++ 0.3: http://www.zeoplusplus.org/download.html -.. _raspa 2.0.30: https://github.com/iRASPA/RASPA2/releases/tag/v2.0.30 - -The following tutorial is part of the course `Understanding advanced molecular -simulation `__ -held at EPF Lausanne during the spring semester 2018. - - -Understanding advanced molecular simulation -=========================================== - -This tutorial makes use of the AiiDA plugins for the `zeo++ -`__ and `RASPA2 -`__ codes. -It is meant to be run inside the `Quantum -Mobile `__ virtual -machine. - - -AiiDA tutorial --------------- - - -.. toctree:: - :maxdepth: 1 - :numbered: - - Preparation <./tutorial/preparation> - Using the verdi command line <./tutorial/verdi-commands> - Submit, monitor and debug calculations <./tutorial/calculations> - The AiiDA python interface <./tutorial/python-interface> - Queries in AiiDA: The QueryBuilder <./tutorial/queries> - -Screening nanoporous materials ------------------------------- - -**Task:** Screen a set of metal-organic frameworks (MOFs) for their -performance in storing methane at room temperature by computing their -*deliverable capacities*, i.e. the difference between the amount of -methane stored in a fully loaded tank (at 65 bar) and an empty tank (at -5.8 bar) per volume. - -**Report:** Write a short report (1 page) outlining your approach and -identifying the five MOFs with the highest deliverable capacities. -Include an export of your AiiDA database [1]_. - -**Note:** This exercise requires a basic knowledge of python. If you are -not familiar with python, partner with someone who is. - -.. toctree:: - :maxdepth: 1 - :numbered: - - Import the structures - Perform geometric analysis <./screening/geometry> - Compute methane loading <./screening/methane-loading> - Screening <./screening/screening> - Ranking <./screening/ranking> - Exporting your database <./screening/export> - -.. [1] - Upload to a file hosting service like - `SWITCHdrive `__ or - `Dropbox `__ and include a download link in - the report. diff --git a/docs/pages/2018_EPFL_molsim/screening/export.md b/docs/pages/2018_EPFL_molsim/screening/export.md deleted file mode 100644 index 967f6ced..00000000 --- a/docs/pages/2018_EPFL_molsim/screening/export.md +++ /dev/null @@ -1,29 +0,0 @@ -Exporting your database -======================= - -Let's put the nodes to be exported into a new group `report`: - -```python -CifData=DataFactory('cif') -qb=QueryBuilder() -qb.append(Group, filters={'name':'mofs'}, tag='mofs') # filter by 'mofs' group -qb.append(CifData, member_of='mofs', tag='cifs') -cifs = qb.all() - -qb.append(Node, descendant_of='cifs') # get all children -children = qb.all() - -report, created = Group.get_or_create(name='report') -report.add_nodes([ n[0] for n in cifs + children ]) -``` - -Once you've grouped your nodes, check the contents of the group and -export it: - -```console -$ verdi group show report -$ verdi export create -g report database.aiida -``` - -*Note:* AiiDA automatically exports the direct outputs of calculations, -i.e. we don't need to add them to our group explicitly. diff --git a/docs/pages/2018_EPFL_molsim/screening/geometry.md b/docs/pages/2018_EPFL_molsim/screening/geometry.md deleted file mode 100644 index b6986b7b..00000000 --- a/docs/pages/2018_EPFL_molsim/screening/geometry.md +++ /dev/null @@ -1,116 +0,0 @@ -Perform geometric analysis of one MOF -===================================== - -Use the [zeo++](http://www.zeoplusplus.org/) code to analyze the -structure, such as the volume inside the MOF that is accessible to -methane. - -- To make things simple, start with one MOF: `DSoLANSUKYIH.cif` - -- Set up the - [Deneb](https://scitas.epfl.ch/hardware/deneb-and-eltanin) computer - at EPFL - - - Import the `deneb-molsim` computer and its codes into AiiDA - - ```console - $ verdi import {{ "/assets/2018_EPFL_molsim/deneb-molsim.aiida" | absolute_url }} - $ verdi computer list -a # should show 'deneb-molsim' - $ verdi code list -A # should show 'zeopp@deneb-molsim', 'raspa@deneb-molsim' - ``` - - - Generate an SSH key pair for passwordless connection - - ```console - $ ssh-keygen - - Generating public/private rsa key pair. - Enter file in which to save the key (/home/max/.ssh/id_rsa): - Enter passphrase (empty for no passphrase): - Enter same passphrase again: - Your identification has been savedin /home/max/.ssh/id_rsa. - Your public key has been saved in /home/max/.ssh/id_rsa.pub. - The key fingerprint is: ... - The key's randomart image is: ... - ``` - - ```console - $ ssh-copy-id @deneb1.epfl.ch - $ ssh @deneb1.epfl.ch # should now work without password - ``` - - - configure your personal access to the `deneb-molsim` computer - - ```console - $ verdi computer configure deneb-molsim - - Configuring computer 'deneb-molsim' for the AiiDA user '@epfl.ch' - Computer deneb-molsim has transport of type ssh - Note: to leave a field unconfigured, leave it empty and press [Enter] - => username = - => port = 22 - => look_for_keys = - => key_filename = - => timeout = 60 - => allow_agent = - => proxy_command = - => compress = True - => gss_auth = no - => gss_kex = no - => gss_deleg_creds = no - => gss_host = deneb1.epfl.ch - => load_system_host_keys = True - => key_policy = AutoAddPolicy - Configuration stored for your user on computer 'deneb-molsim'. - ``` - - - Finally, test your new computer - - ```console - $ verdi computer test deneb-molsim - ``` - -- Compute the density, accessible volume and [accessible surface - area](https://en.wikipedia.org/wiki/Accessible_surface_area) using - zeo++. - - Use the [zeo++ web site](http://www.zeoplusplus.org/examples.html) - to figure out what the `-ha`, `-res`, `-sa`, `-volpo` command line - options do. - - What is an appropriate value for the probe radius `r`? - - ```python - code = Code.get_from_string("zeopp@deneb-molsim") - calc = code.new_calc() - - # computational resources for calculation - calc.set_withmpi(False) - calc.set_resources( {"num_machines": 1, "tot_num_mpiprocs":} ) - calc.set_max_wallclock_seconds(30*60) \# 30 minutes - calc.set_max_memory_kb(2e6) - - # zeo++ command line parameters - NetworkParameters = DataFactory('zeopp.parameters') d={ - 'ha': True, - 'res': True, - 'sa': [, , 1000], - 'volpo': [, , 1000], - } - parameters = NetworkParameters(dict=d) - calc.use_parameters(parameters) - - # Load the cif node from the database, e.g. by PK - # cif = load_node() - calc.use_structure(cif) - - # This places files in a subfolder "submit_test" instead of submitting. - calc.submit_test() - # Uncomment lines below when the script runs through - # calc.store_all() - # calc.submit() - # print(calc) # prints UUID and PK - ``` - -**Remember:** Use `verdi run ` to run python scripts in the -same environment as in the `verdi shell`. diff --git a/docs/pages/2018_EPFL_molsim/screening/import.md b/docs/pages/2018_EPFL_molsim/screening/import.md deleted file mode 100644 index b157d92f..00000000 --- a/docs/pages/2018_EPFL_molsim/screening/import.md +++ /dev/null @@ -1,56 +0,0 @@ -Import the structures -===================== - -You can download a tar file containing the structures from - -```console -$ wget {{ "/assets/2018_EPFL_molsim/mof_structures.tar.gz" | absolute_url }} -$ tar -xf mof_structures.tar.gz -``` - -- Visualize a few structures using `jmol`: - - ```console - $ cd struct/ - $ jmol .cif - ``` - - Can you think of two criteria for a high deliverable methane - capacity? - -- Use the `CifData` class to import the structures into your database, e.g. - - ```python - CifData = DataFactory('cif') - cif = CifData(file='/path/to/file.cif') - cif.store() - ``` - -- Use wildcards with the `glob` and `os` modules to get the file paths - of all structures like so: - - ```python - from glob import glob - from os import path - all_structure_files = glob(path.abspath("/path/to/struct/*")) - ``` - -- Keep track of your structures using groups - - ```python - # Use get_or_create to be able to run this multiple times mofs, - created = Group.get_or_create(name='mofs') - mofs.add_nodes([cif1, cif2]) - ``` - -- Finally, you can load your structures from the group using the - [AiiDA query builder](http://aiida-core.readthedocs.io/en/latest/querying/querybuilder/usage.html) - - ```python - qb = QueryBuilder() - qb.append(Group, filters = { 'name':'mofs' }) - mofs = qb.one()[0] - - for cif in mofs.nodes: - print cif - ``` diff --git a/docs/pages/2018_EPFL_molsim/screening/methane-loading.md b/docs/pages/2018_EPFL_molsim/screening/methane-loading.md deleted file mode 100644 index 1eba42ed..00000000 --- a/docs/pages/2018_EPFL_molsim/screening/methane-loading.md +++ /dev/null @@ -1,56 +0,0 @@ -Compute methane loading for one MOF -=================================== - -The average loading of the MOF at a given pressure will be computed -using grand-canonical Monte Carlo (GCMC) simulations with the -[RASPA](https://github.com/numat/RASPA2) code. - -- The RASPA code needs several input parameters, some of which you - will need to figure out - - ```python - parameters = ParameterData(dict={ - "GeneralSettings": { - "SimulationType" : "MonteCarlo", - "NumberOfCycles" : 2000, - "NumberOfInitializationCycles" : 2000, - "PrintEvery" : 2000, - "ChargeMethod" : "Ewald", - "CutOff" : 12.0, - "Forcefield" : "", - "EwaldPrecision" : 1e-6, - "Framework" : 0, - "UnitCells" : " ", - "HeliumVoidFraction" : 0.0, - "ExternalTemperature" : , - "ExternalPressure" : , - }, - "Component": [{ - "MoleculeName" : "methane" - "MoleculeDefinition" : "TraPPE", - "TranslationProbability" : 0.5, - "ReinsertionProbability" : 0.5, - "SwapProbability" : 1.0, - "CreateNumberOfMolecules" :0, - }], - }) - ``` - - - Our simulations are performed under periodic boundary - conditions. This means, we need to make our simulation cell - large enough that a molecule will never interact with a two - periodic copies of any of its neighbors. Given the cutoff radius - of $12$ Angstroms, how often do you need to replicate the unit - cell of the material? - - *Hint:* The CIF files include information on the size of the - unit cell. - - - To make things more interesting, we are going to use different - force fields. Ask your instructor to give you a force field - identifier. - -- You already performed a small GCMC calculation at 10 bar during the - tutorial. Adapt the input file to your needs and run the - calculation. - *Hint:* Once running, the calculation should finish within 5 minutes. diff --git a/docs/pages/2018_EPFL_molsim/screening/ranking.md b/docs/pages/2018_EPFL_molsim/screening/ranking.md deleted file mode 100644 index fc00f147..00000000 --- a/docs/pages/2018_EPFL_molsim/screening/ranking.md +++ /dev/null @@ -1,43 +0,0 @@ -# Ranking the structures - -We've prepared an interactive jupyter app that helps you with ranking -and visualizing the structures. Use at your convenience. - -## Installation - -To get the app please download it using the following link: - -```console -$ wget {{ "/assets/2018_EPFL_molsim/ranking.tar.gz" | absolute_url }} -$ tar -xf ranking.tar.gz -``` - -If you are working with Quantum Mobile, simply place the extracted -folder inside `/project/apps` and it should already work. - -In case you have configured AiiDA on your own computer please install -additional packages: - -```console -$ pip install jupyter matplotlib bokeh plotly ase appmode -$ jupyter nbextension enable --py --sys-prefix appmode -$ jupyter serverextension enable --py --sys-prefix appmode -``` - -## Usage - -To use the application please open - -- `dc_group.ipynb`, if you followed the *quick and simple* route -- `dc_wf.ipynb`, if you followed the *elegant and robust* route - -Switch to the Appmode for the convenience. - -For the users of Quantum Mobile it is enough to launch "Jupyter Apps" icon on -the desktop and chose corresponding notebook from the "Molsim course" app. - -Once you have opened the notebook, specify the group of structures (if -required) and press the "Plot" button. - -We recommend to use the "bokeh" framework as it shows the corresponding -structure pk once you put the cursor on top of a point. diff --git a/docs/pages/2018_EPFL_molsim/screening/screening.md b/docs/pages/2018_EPFL_molsim/screening/screening.md deleted file mode 100644 index b335cf99..00000000 --- a/docs/pages/2018_EPFL_molsim/screening/screening.md +++ /dev/null @@ -1,282 +0,0 @@ -# Screening - -For the screening part of the work, you can choose one of two possible -routes: - -1. **Quick and simple:** Use `for` loops in your scripts to loop over - the structures in your database, submitting in total - 1 (Zeo++) + 2 (Raspa) = 3 calculations per structure. - - This should require very little changes to your python scripts and - is a perfectly valid solution. -2. **Reusable and elegant:** Write an AiiDA `Workchain` that takes a - structure, performs all necessary calculations, and outputs the result. - - This route requires more advanced python concepts and involves a bit - of coding, but makes your workflow more robust and reusable. - -## Quick and simple - -Just use the `QueryBuilder` to load the `CifData` nodes from the AiiDA -database and loop over them. - -Computed properties are automatically linked to `CifData` nodes via -calculation nodes. - -Try `verdi graph generate ` on a zeo++ or RASPA calculation node to -get an overview of the AiiDA graph. - -In order to automatically determine how many unit cells to use in the -simulation, you may use the following function for convenience: - -```python -def multiply_cell(cif, cutoff): - """ Determine number of replica of unit cell. - - Works for cells of arbitrary shape. - - :param cif: CifData object - :param cutoff: cutoff radius of interaction - :returns: String of integers specifying replica of unit cell, - suitable for 'UnitCells' parameter of raspa calculation. - """ - from math import cos, sin, sqrt, pi - import numpy as np - deg2rad=pi/180. - - struct = cif.values.dictionary.itervalues().next() - - a = float(struct['_cell_length_a']) - b = float(struct['_cell_length_b']) - c = float(struct['_cell_length_c']) - - alpha = float(struct['_cell_angle_alpha'])*deg2rad - beta = float(struct['_cell_angle_beta'])*deg2rad - gamma = float(struct['_cell_angle_gamma'])*deg2rad - - # compute cell vectors following https://en.wikipedia.org/wiki/Fractional_coordinates - v = sqrt(1-cos(alpha)**2-cos(beta)**2- cos(gamma)**2+2*cos(alpha)*cos(beta)*cos(gamma)) - cell=np.zeros((3,3)) - cell[0,:] = [a, 0, 0] - cell[1,:] = [b*cos(gamma), b*sin(gamma),0] - cell[2,:] = [c*cos(beta), c*(cos(alpha)-cos(beta)*cos(gamma))/(sin(gamma)), - c*v /sin(gamma)] - cell=np.array(cell) - - # diagonalize the cell matrix - diag = np.diag(cell) - # and computing nx, ny and nz - nx, ny, nz = tuple(int(i) for i in np.ceil(cutoff/diag*2.)) - - #return nx, ny, nz - return "{} {} {}".format(nx, ny, nz) -``` - -## Elegant and robust - -Combine the calculations into a workchain using the AiiDA `WorkChain` -class. Here one should define the list of input types using spec.input() -function and the workflow steps using spec.outline() function. In our -case the workflow takes as input CifData object with structure and the -names of Zeo++ and Raspa codes. - -```python -class DcMethane(WorkChain): - """ Compute deliverable capacity for methane. """ - - @classmethod - def define(cls, spec): - """ Define input, logic and output of Workchain. """ - super(DcMethane, cls).define(spec) - - # First we define the inputs, specifying the type we expect - spec.input("structure", valid_type=CifData, required=True) - spec.input("zeopp_codename", valid_type=Str, required=True) - spec.input("raspa_codename", valid_type=Str, required=True) - - # The outline describes the business logic that defines - # which steps are executed in what order and based on - # what conditions. We will implement each `cls.method` below - spec.outline( - cls.init, - cls.run_geom_zeopp, - cls.run_loading_raspa_5_8, - cls.parse_loading_raspa, - cls.run_loading_raspa_65, - cls.parse_loading_raspa, - cls.extract_results, - ) - - # Here we define the output the Workchain will generate and - # return. Dynamic output allows a variety of AiiDA data nodes - # to be returned - spec.dynamic_output() -``` - -The workchain consists of **7 steps**. - -### Step 1: Prepare input parameters and variables - - ```python - def init(self): - """ - Initialize variables - """ - # Define cutoff for the methane-methane interactions - cutoff = 12.00 - - self.ctx.loading_average = {} - - self.ctx.parameters = {} - # Note: You'll need the multiply_cell function mentioned in section 4.1 - - self.ctx.options = { - "resources": { - "num_machines": 1, - "tot_num_mpiprocs": 1, - "num_mpiprocs_per_machine": 1, - }, - "max_wallclock_seconds": 10 * 60 * 60, # 10 hours - "max_memory_kb": 2000000, # limiting the - "withmpi": False, - } - ``` - -### Step 2: Compute the geometric parameters of the MOFs - -Draw upon how we submitted Zeo++ calculations in section 2. -The main difference here is that the calculation inputs, - such as Code or structure, are provided as a dictionary. - -```python -def run_geom_zeopp(self): - """ Perform a zeo++ calculation. """ - - # Create the input dictionary - NetworkParameters = DataFactory('zeopp.parameters') - sigma = 1.86 - params = { - 'ha': True, - 'res': True, - 'sa': [sigma, sigma, 100000], - 'volpo': [sigma, sigma, 100000], - } - - inputs = { - 'code': : Code.get_from_string(self.inputs.zeopp_codename.value), - 'structure': : self.inputs.structure, - 'parameters': : NetworkParameters(dict=params), - '_options': : self.ctx.options, - } - - # Create the calculation process and launch it - process = ZeoppCalculation.process() - future = submit(process, **inputs) - self.report("pk: {} | Running geometry analysis with zeo++".format(future.pid)) - - return ToContext(zeopp=Outputs(future)) - ``` - -### Step 3,5: Compute the methane loading - -Steps 3 and 5 compute the methane loading in units of [molecules/cell] at -5.8 and 65 bars respectively. Since the same calculation is performed twice -in this workchain, we put the common part of those steps into a function: - -```python -def _run_loading_raspa(self, pressure): - """ Perform a raspa calculation for one pressure. """ - self.ctx.parameters['GeneralSettings']['ExternalPressure'] = pressure - - # Create the input dictionary - inputs = { - 'code' : Code.get_from_string(self.inputs.zeopp_codename.value), - 'structure' : self.inputs.structure, - 'parameters' : NetworkParameters(dict=params), - '_options' : self.ctx.options, - } - - # Create the calculation process and launch it - process = RaspaCalculation.process() - future = submit(process, **inputs) - self.report("pk: {} | Running raspa for the pressure {} [bar]" \ - .format(future.pid, pressure/1e5) - - return ToContext(raspa=Outputs(future)) -``` - -The `run_loading_raspa_5_8` and `run_loading_raspa_65` functions -are defined as follows: - -```python -def run_loading_raspa_5_8(self): - return self._run_loading_raspa(pressure = 5.8e5) - -def run_loading_raspa_65(self): - return self._run_loading_raspa(pressure = 6.5e6) -``` - -### Step 4,6: Extract pressure and methane loading - -Steps 4 and 6 extract pressure and methane loading from the input and -output parameters of the calculation and put them into context (`ctx`) -that is used to store any data that should be persisted between step. - -```python -def parse_loading_raspa(self): - """ Extract the pressure and loading average of the last completed raspa calculation """ - pressure = self.ctx.parameters['GeneralSettings']['ExternalPressure'] - loading_average = self.ctx.raspa["component_0"].dict.loading_absolute_average - self.ctx.loading_average[str(int(pressure))] = loading_average -``` - -Last step stores the selected computed parameters as the output of the -`DcMethane` workchain: - -```python -def extract_results(self): - """ Attach the results of raspa calculation and the initial structure to the outputs """ - dc = self.ctx.loading_average['6500000'] - self.ctx.loading_average['580000'] - - zeopp = self.ctx.zeopp - res = { - 'density' : zeopp['pore_volume_volpo'].get_attr('Density'), - 'density_units' : 'g/cm^3', - 'pore_accesible_volume' : zeopp['pore_volume_volpo'].get_attr('POAV_A^3'), - 'pore_accesible_volume_units' : 'A^3', - 'unitcell_volume' : zeopp['pore_volume_volpo'].get_attr( 'Unitcell_volume'), - 'unitcell_volume_units' : 'A^3', - 'largest_included_sphere' : zeopp['free_sphere_res'].get_attr('Largest_included_sphere'), - 'largest_included_sphere_units' : 'A', - 'accessible_surface_area' : zeopp['surface_area_sa'].get_attr('ASA_m^2/g'), - 'accessible_surface_area_units' : 'm^2/g', - 'deliverable_capacity' : dc, - 'deliverable_capacity_units' : 'molecules/unit cell', - } - self.out("result", ParameterData(dict=res)) - - self.report("Workchain <{}> completed successfully".format(self.calc.pk)) - return -``` - -To submit the calculation please adapt the following script. Please -note, the file containing the `DcMethane` workchain should be accessible -from the python shell. To achieve that just place the file into a folder -listed in `PYTHONPATH` system variable and rename it to -`deliverable_capacity.py`. - -```python -import os -from deliverable_capacity import DcMethane -from aiida.orm.data.cif import CifData -from aiida.orm.data.base import Str -from aiida.work.run import run, submit - -for s in structures: - outputs = submit(DcMethane, structure=s, - zeopp_codename=Str('zeopp@deneb-molsim'), raspa_codename=Str('raspa@deneb-molsim'), - ) -``` - -Where structures is the list of `CifData` nodes stored in your AiiDA -database. diff --git a/docs/pages/2018_EPFL_molsim/tutorial/calculations.rst b/docs/pages/2018_EPFL_molsim/tutorial/calculations.rst deleted file mode 100644 index 729f706e..00000000 --- a/docs/pages/2018_EPFL_molsim/tutorial/calculations.rst +++ /dev/null @@ -1,228 +0,0 @@ -Submit, monitor and debug calculations -====================================== - -The goal of this section is to understand how to create new data in -AiiDA. We will launch a Grand Canonical Monte Carlo simulation and check -its results. While for now we will do it ‘manually', workflows (that we -will learn later in this tutorial) can automate this procedure -considerably. - -Computer setup and configuration --------------------------------- - -For the tutorial, we've created a “virtual supercomputer” on the Amazon -elastic cloud, where you can submit your calculations. - -Please set up the computer as follows: - -.. code:: console - - $ verdi computer setup - At any prompt, type ? to get some help. - ————————————— - => Computer name: aws Creating new computer with name 'aws' - => Fully-qualified hostname: 34.244.10.104 - => Description: AWS instance for tutorial - => Enabled: True - => Transport type: ssh - => Scheduler type: torque - => shebang line at the beginning of the submission script: \#!/bin/console - => AiiDA work directory: /tmp/{username}/aiida~r~un/ - => mpirun command: mpirun -np {tot_num_mpiprocs} - => Default number of CPUs per machine: 2 - => Text to prepend to each command execution: - # This is a multiline input, press CTRL+D on a - # empty line when you finish - # —————————————— - # End of old input. You can keep adding - # lines, or press CTRL+D to store this value - # —————————————— - => Text to append to each command execution: - # This is a multiline input, press CTRL+D on a - # empty line when you finish - # —————————————— - # End of old input. You can keep adding - # lines, or press CTRL+D to store this value - # —————————————— - Computer 'aws' successfully stored in DB. - -At this point, the computer node has been created in the database, see - -.. code:: console - - $ verdi computer list -a - -but it hasn't yet been configured. - -In order to access the computer, download the SSH key - -.. code:: console - - $ wget https://www.dropbox.com/s/.../aiida_tutorial_aiidaaccount?dl=1 -O /home/max/.ssh/aws.pem - -and use it to configure the ``aws`` computer: - -.. code:: console - - $ verdi computer configure aws - Configuring computer 'aws' for the AiiDA user 'aiida@localhost' - Computer aws has transport of type ssh - - Note: to leave a field unconfigured, leave it empty and press [Enter] - - => username = aiida - => port = 22 - => look for keys = True - => key filename = /home/max/.ssh/aws.pem - => timeout = 60 - => allow agent = - => proxy command = - => compress = True - => gssauth = no - => gsskex = no - => gssdelegcreds = no - => gsshost = 34.244.10.104 - => load system hostkeys = True - => key policy = AutoAddPolicy - Configuration stored for your user on computer 'aws'. - -Finally, let aiida test the computer: - -.. code:: console - - $ verdi computer test aws - -Code setup and configuration ----------------------------- - -Next, we need to let AiiDA know about the computer codes available on -our “virtual supercomputer”. - -Let's set up the `RASPA2 `__ code as -follows: - -.. code:: console - - $ verdi code setup - At any prompt, type ? to get some help. - ————————————— - => Label: raspa - => Description: Raspa code for molsim course - => Local: False - => Default input plugin: raspa - => Remote computer name: aws - => Remote absolute path: /home/aiida/.local/bin/simulate - => Text to prepend to each command execution - FOR INSTANCE, MODULES TO BE LOADED FOR THIS CODE: - # This is a multiline input, press CTRL+D on a - # empty line when you finish - # —————————————— - # End of old input. You can keep adding - # lines, or press CTRL+D to store this value - # —————————————— - => Text to append to each command execution: - # This is a multiline input, press CTRL+D on a - # empty line when you finish - # —————————————— - # End of old input. You can keep adding - # lines, or press CTRL+D to store this value - # —————————————— - Code 'raspa' successfully stored in DB. - -The list of codes should now include your new code ``raspa@aws`` - -.. code:: console - - $ verdi computer test aws - -The AiiDA daemon ----------------- - -First of all check that the AiiDA daemon is actually running. The AiiDA -daemon is a program running all the time in the background, checking if -new calculations appear and need to be submitted to the scheduler. The -daemon also takes care of all the necessary operations before the -calculation submission, and after the calculation has completed on the -cluster. Type in the terminal - -.. code:: console - - $ verdi daemon status - -If the daemon is running, the output should look like - -:: - - # Most recent daemon timestamp:0h:00m:26s ago - ## Found 1 process running: - * aiida-daemon[aiida-daemon] RUNNING pid 15044, uptime 3 days, 15:38:41 - -If this is not the case, type in the terminal - -.. code:: console - - $ verdi daemon start - -to start the daemon. - -Creating a new calculation --------------------------- - -To start please `download the AiiDA submission -script <./assets/2018_EPFL_molsim/raspa_submission.zip>`__. To launch a -calculation, you will need to interact with AiiDA mainly in the -``verdi shell``. We strongly suggest you to first try the commands in -the shell, and then copy them in a script “test\_pw.py” using a text -editor. This will be very useful for later execution of a similar series -of commands. - -**The best way to run python scripts using AiiDA functionalities is to -run them in a terminal by means of the command** - -.. code:: console - - $ verdi run - -Every calculation sent to a cluster is linked to a code, which describes -the executable file to be used. Therefore, first load the suitable code: - -.. code:: python - - from aiida.common.example_helpers import test_and_get_code - code = test_and_get_code(codename, expected_code_type='raspa') - -Here ``test_and_get_code`` is an AiiDA function handling all possible -codes, and ``code`` is a class instance provided as ``codename`` (see -the first part of the tutorial for listing all codes installed in your -AiiDA machine). For this example use codename ``raspa@aws``. - -AiiDA calculations are instances of the class ``Calculation``, more -precisely of one of its subclasses, each corresponding to a code -specific plugin (for example, the Raspa plugin). We create a new -calculation using the ``new_calc`` method of the ``code`` object: - -.. code:: python - - calc = code.new_calc() - -This creates and initializes an instance of the ``RaspaCalculation`` -class, the subclass associated with the ``raspa`` plugin. Sometimes, you -might find convenient to annotate information assigning a (short) label -or a (long) description, like: - -.. code:: python - - calc.label='Raspa test' - calc.description='My first AiiDA calc with Raspa' - -This information will be saved in the database for later query or -inspection. - -Now you have to specify the number of machines (a.k.a. cluster nodes) -you are going to run on and the maximum time allowed for the calculation -— this information is passed to the scheduler that handles the queue: - -.. code:: python - - calc.set_resources({'num_machines': 1, 'num_mpiprocs_per_machine':1}) - calc.set_max_wallclock_seconds(30*60) diff --git a/docs/pages/2018_EPFL_molsim/tutorial/preparation.md b/docs/pages/2018_EPFL_molsim/tutorial/preparation.md deleted file mode 100644 index 12ae4bfc..00000000 --- a/docs/pages/2018_EPFL_molsim/tutorial/preparation.md +++ /dev/null @@ -1,26 +0,0 @@ -Preparation -=========== - -Start the Quantum Mobile virtual machine, open a terminal window and -type - -```console -$ workon aiida -``` - -This activates the virtual environment, in which AiiDA is installed. - -Since Quantum Mobile focuses on *ab initio* calculations, it is missing -some aiida plugins we are going to need. Let’s install them (this can -take 3 minutes): - -```console -$ pip install aiida-raspa aiida-zeopp -``` - -Before we start creating data ourselves, we are going to look at an -existing AiiDA database. Let’s import one from the web: - -```console -$ verdi import {{ "/assets/2018_EPFL_molsim/isotherms.aiida" | absolute_url }} -``` diff --git a/docs/pages/2018_EPFL_molsim/tutorial/python-interface.md b/docs/pages/2018_EPFL_molsim/tutorial/python-interface.md deleted file mode 100644 index 358d43f6..00000000 --- a/docs/pages/2018_EPFL_molsim/tutorial/python-interface.md +++ /dev/null @@ -1,89 +0,0 @@ -The AiiDA python interface -========================== - -In this section we will use an interactive python environment with all -the basic AiiDA classes already loaded. There are two variants of this: - -The first is a customized ipython shell where all the AiiDA classes, -methods and functions are accessible. Type in the terminal - -```console -verdi shell -``` - -For everyday AiiDA-based operations, i.e. creating, querying and using -AiiDA objects, the `verdi shell` is probably the best tool. -You would usually use two terminals, one for the -`verdi shell` and one to execute bash commands. - -The second option is based on `jupyter` notebooks and is great for -tutorial purposes. Double click on the `Jupyter Apps` icon on the -Desktop to start a jupyter notebook server. After a few seconds, the -browser will open and display the home app. Click on `File Browser` and -select `New -> Python 2` (top right corner). You are now inside a -jupyter notebook, made of cells where you can type portions of python -code. The code will not be executed until you press `Shift+Enter` from -within a cell. Type in the first cell - -and execute it. This will set exactly the same environment as the -`verdi shell`. The notebook will be automatically saved -upon any modification and when you think you are done, you can export -your notebook in many formats by going to `File -> Download as`. We -suggest you to have a look to the drop-down menus `Insert` and `Cell` -where you will find the main commands to manage the cells of your -notebook. **The `verdi shell` and the -`jupyter` notebook are completely equivalent. Use either -according to your personal convenience.** - -Note: you will still need sometimes to type command-line instructions in -`bash` in the first terminal you opened today. To -differentiate these from the commands to be typed in the -`verdi shell`, the latter will be marked in this document -by a vertical line on the left, like: - -```python -# some verdi shell command -``` - -while command-line instructions in `bash` to be typed on a -terminal will be encapsulated between horizontal lines: - -```console -# some bash command -``` - -Alternatively, to avoid changing terminal, you can execute -`bash` commands within the `verdi shell` or -the notebook adding an exclamation mark before the command itself - -```python -!some bash command -``` - -Loading a node --------------- - -Most AiiDA objects are represented by nodes, identified in the database -by its pk number (an integer). You can access a node using the following -command in the shell: - -```python -node = load_node() -``` - -Load a node using one of the calculation pks visible in the graph you -displayed in the previous section of the tutorial. Then get the energy -of the calculation with the command - -```python -node.out.output_parameters.get_dict() -``` - -You can also type - -```python -node.out. -``` - -and then press `TAB` to see all the possible output results -of the calculation. diff --git a/docs/pages/2018_EPFL_molsim/tutorial/queries.md b/docs/pages/2018_EPFL_molsim/tutorial/queries.md deleted file mode 100644 index 87a796df..00000000 --- a/docs/pages/2018_EPFL_molsim/tutorial/queries.md +++ /dev/null @@ -1,200 +0,0 @@ -Queries in AiiDA: The QueryBuilder {#sec:querybuilder} -================================== - -In this part of the tutorial we will focus on how to query our database -using a querying tool for AiiDA called the *QueryBuilder*. Queries are, -very loosely defined, questions to your database. We will first show you -some simple examples and tasks on how to explore your database. Then we -will proceed to a more concrete exercise on the screening of magnetic -and metallic perovskites.\ - -Task 1 - Introduction to QueryBuilder -------------------------------------- - -| Node & subclasses | Number in DB -|-------------------| -------------- -| Node | 4707 -| StructureData | 621 -| ParameterData | 1338 -| KpointsData | 861 -| UpfData | 99 -| JobCalculation | 448 - - : List of some Node subclasses and how many times they occur in our - test database. - -[fig.types] - -In this task we will use the QueryBuilder to do some basic queries and -understand our database. As a first step we should import our querying -tool, the *QueryBuilder*. - -from aiida.orm.querybuilder import QueryBuilder - -After the above import, we create our first query. To do so, we will -have to instantiate a QueryBuilder instance: - -```python -qb = QueryBuilder() -``` - -Our query is still empty, we have not yet defined what we want to see. -For example, we will ask for all the nodes of our database. This is as -simple as appending the Node class to the query that we construct. - -```python -qb.append(Node) -``` - -At this point, we can finish our query by asking back all nodes and by -typing - -```python -qb.all() # Returns all nodes in the database -``` - -However, this command will return us all the Nodes directly, which may -not be the most wise thing to do considering that is the biggest family -of AiiDA stored objects that we can query. To understand the size of the -result, we can type the following command: - -```python -qb.count() # Returns an integer, the number of nodes in the database -``` - -If you are interested to retrieve a subclass of a node, append that -specific subclass instead of Node: - -```python -CifData = DataFactory('cif') -qb = QueryBuilder() # Creating a new QueryBuilder instance -qb.append(CifData) # Telling the QueryBuilder instance that I want a cif data type -qb.all() # Asking for all the results! -``` - -**Exercise:** - -- Try now to find the number of instances for some subclasses of Node - (e.g. StructureData, ParameterData, etc.) that are stored in your - database. The result should look like . Of course, the numbers can - be different! - -**Comment:** If you are familiar with the SQL (Structured Query -Language) syntax then you may wonder what the issued SQL command is. -This can be easily seen by typing: - -```python -str(qb) -``` - -**Comment:** If you want to get inspired by the available QueryBuilder -options you can just press the *tab* key in an interactive shell (after -typing `qb.`) to see the available options. - -**Comment:** After you run a query, a new `QueryBuilder` instance needs -to be defined if you want to make a new query. - -Task 2 - Projections and filters --------------------------------- - -| Operator | Datatype | Example -|-------------| -------------------------| ------------------------------------ -| == | All | {'==':12} -| in | All | {'in':['FINISHED', 'PARSING']} -| >,<,<=,>= | floats, integers, dates | {'>':5.2} -| like | Chars | {'like':'calculation%'} -| ilike | Chars | {'ilike':'caLculAtioN%'} -| or | | {'or':[{'<':5.3}, {'>':6.3}]} -| and | | {'and':[{'>':5.3}, {'<':6.3}]} - - : Operators currently implemented for all backends. - -[tab.filterops] - -In database language performing a projection means to extract one or -more specific columns from a table. In the AiiDA language this is -equivalent to say that we select what properties a query should return -out of the queried objects. For example, we might be interested only in -the id of a set of nodes (or their creation date, or any stored value). -To this purpose we should suitably instruct a QueryBuilder object by -means of the "project" key. For example, if we would like to get all the -ids of the nodes, we would type the following: - -```python -qb = QueryBuilder() qb.append(Node, project=["id"]) qb.all() -``` - -|---------| -------------------------| -|Entity | Properties | -|---------| -------------------------| -|Node | id, uuid, type, label, description, ctime, mtime| -|Computer | id, uuid, name, hostname, description, enabled, transport_type, scheduler_type| -|User | id, email, first_name, last_name, institution| -|Group | id, uuid, name, type, time, description| -|---------| -------------------------| - - : A selection of entities and some of their properties. - -[tab.properties] - -Please note that if you would like to perform an operation on the *pk* -of a node, you should use the keyword *id* in QueryBuilder queries. - -Most likely, performing a query implies to select only those elements -that fulfill certain criteria. For example, we might want to select all -the calculations that were launched on a specific date. In database -language, this is called "adding a filter" to a query. A filter is a -boolean operator that returns True or False. lists all operators that we -implemented. A selection of entities and some of their properties that -you can use at your projections and filters can be found at table . - -If you want to add filters to your query, you simply add the *filters* -keyword with a dictionary. Suppose you want to know the creation date of -a structure of which you know the uuid: - -```python -qb = QueryBuilder() # Instantiating a new QueryBuilder -qb.append(CifData, # I want structures! - project=["ctime"], # I'm interested in creation time! - filters={"uuid": {"==":"b84e8d4c-908b-45b4-8015-3ace540f7dd6"}} -) # I want the structure with this UUID -qb.all() -``` - -Try it out! There is also the possibility to combine multiple filters on -the same object using the "and" or the "or" keyword in the filter -section. Let's see an example. - -```python -from datetime import datetime, timedelta -qb = QueryBuilder() -qb.append(CifData, - project=["uuid"], # I want to see only the UUID - filters={ "or":[ # First filter is an or statement - { "ctime": {">":datetime.now() - timedelta(days=12) }}, - { "label": "Raspa test" } - ]} -) -qb.all() -``` - -In the above example we added an "or" keyword between the two filters. -The query return every structure in the database that was created in the -last 12 days or is named "graphene". - -**Hints for the exercises:** - -- The operator '>', '<' works with date-type properties with the expected behavior. - -- For your date comparisons you will need to create a `datetime` - object to which you can assign a date of your preference. You will - have to do the necessary import (`from datetime import datetime`) - and create an object by giving a specific date. E.g. - `datetime(2015, 12, 26)`. For further information, you can consult the Python's - online documentation. - -**Exercises:** - -- Write a query that returns all instances of StructureData that have been created after the 1st of January 2016. - -- Write a query that returns all instances of Group whose name starts with "tutorial". diff --git a/docs/pages/2018_EPFL_molsim/tutorial/verdi-commands.md b/docs/pages/2018_EPFL_molsim/tutorial/verdi-commands.md deleted file mode 100644 index 048ae703..00000000 --- a/docs/pages/2018_EPFL_molsim/tutorial/verdi-commands.md +++ /dev/null @@ -1,269 +0,0 @@ -# Using the verdi command line - -This part of the tutorial will help you familiarize with the command -line utility `verdi`, one of the most common ways to -interact with AiiDA. `verdi` with its subcommands enables a -variety of operations such as inspecting the status of ongoing or -terminated calculations, showing the details of calculations, computers, -codes, or data structures, access the input and the output of a -calculation, etc. Similar to the `bash` shell, verdi command support Tab -compltion. Try right now to type `verdi` in a terminal and -tap Tab twice to have a list of subcommands. Whenever you need the -explanation of a command type `verdi help` or add -`-h` flag if you are using any of the `verdi` -subcommands. Finally, fields enclosed in angular brackets, such as -``, are placeholders to be replaced by the actual value of that -field (an integer, a string, etc...). - -## The list of calculations - -Let us try our first `verdi` commands. Type in the terminal - -```console -verdi calculation list -``` - -(Note: the first time you run this command, it might take some seconds -as it is the first time you are accessing the database in the virtual -machine. Following calls will be faster). This will print the list of -ongoing calculations, which should be empty. The first output line -should look like - -```console -# Last daemon state_updater check: 0h:00m:18s ago (at 17:17:26 on 2016-05-31) -``` - -In order to print a list with all calculations that finished correctly -in the AiiDA database, you can use the `-a/--all-states` -and `-A/--all-users` flag as follows: - -```console -verdi calculation list –all-states –all-users -``` - -Another very typical option combination allows to get calculations in -*any* state (flag `-a`) d in the past -`NUM` days (`-p `): e.g., for calculation -in the past 1 day: `verdi calculation list -p1 -a`. - -Each row of the output identifies a calculation and shows several -information about it. For a more detailed list of properties, choose one -row by noting down its pk number and type in the terminal - -```console -verdi calculation show -``` - -The output depends on the specific pk chosen and should inform you about -the input nodes (e.g. initial structure, settings etc.), the output -nodes (e.g. output parameters, etc.). An example of RASPA calculation -(pk=3006) output is provided below - -```console - ----------- ------------------------------------ - type RaspaCalculation - pk 3006 - uuid b09ee4b0-8bfe-4083-9371-09b47bf98dce - label - description - ctime 2018-05-07 01:31:29.906303+00:00 - mtime 2018-05-07 10:33:39.871720+00:00 - computer [1] deneb-lsmo - code raspa - ----------- ------------------------------------ - ##### INPUTS: - Link label PK Type - ------------ ---- ------------- - parameters 2757 ParameterData - structure 2886 CifData - ##### OUTPUTS: - Link label PK Type - ----------------- ---- ------------- - remote_folder 3314 RemoteData - retrieved 1132 FolderData - output_parameters 1131 ParameterData - component_0 1130 ParameterData -``` - -## A typical AiiDA graph - -Note that pk number shown in the examples may be different for your -database. - -AiiDA stores in the database the inputs required by a calculation as -well as the its outputs. - -![Dependency graph of a Raspa calculation.]({{ site.baseurl}}/assets/2018_EPFL_molsim/raspa_sample_graph.png "Dependency graph of a Raspa calculation.") - -You can create a similar graph for any calculation node by using the -utility `verdi graph `. For example, before -you obtained information (in text form) for `pk=3006`. To visualize -similar information in graph(ical) form, run (replacing -`` with your number): - -```console -verdi graph -``` - -This command creates the file ``.dot that can be rendered by means -of the utility `dot`. Convert it to PDF and have a look: - -```console -dot -Tpdf -o .pdf .dot -evince .pdf -``` - -Spend some time to familiarize with the graph structure. Identify the -root node (highlighted in blue) and trace back the structure used as an -input. This is an example of a Raspa calculation. We will now inspect -the different elements of this graph. - -## Inspecting the nodes of a graph - -### ParameterData and Calculations - -Now, let us have a closer look to the some of the nodes appearing in the -graph. Choose the node of the type `ParameterData` with input link name -`parameters` (ex. pk=2757) and type in the terminal: - -```console -verdi data parameter show -``` - -A `ParameterData` contains a dictionary (i.e., key–value pairs), stored -in the database in a format ready to be queried (we will learn how to -run queries later on in this tutorial). The command above will print the -content dictionary, containing the parameters used to define the input -file for the calculation. You can compare the dictionary with the -content of the raw input file to Raspa (that was d by AiiDA) via -the command - -```console -verdi calculation inputcat -``` - -where you substitute the pk of the calculation node (e.g. pk=3006). -Check the consistency of the parameters written in the input file and -those stored in the ParameterData node. Even if you don’t know the -meaning of the input flags of a Raspa calculation, you should be able to -see how the input dictionary has been converted into the input file. - -The previous command just printed the content of the “default” input -file `simulation.input`. To see a list of all the files used to run a -calculation (input file, submission script, etc.) type instead - -```console -verdi calculation inputls -``` - -(Adding a `--color` flag allows you to easily distinguish files from -folders by a different coloring). - -Once you know the name of the file you want to visualize, you can call -the `verdi calculation inputcat` command specifying the -path. For instance, to see the submission script, you can do: - -```console -verdi calculation inputcat -p ~a~iidasubmit.sh -``` - -### CifData - -Now let us focus on CifData objects, such as node pk=2886 of the graph. -A CifData object contains a crystal structure and can be inspected -interactively using viewers like `jmol` or `vesta`. Type in the terminal - -```console -verdi data cif show –format jmol 2886 -``` - -to show the selected structure. - -### Codes and computers - -Let us focus now on the nodes of type `code`. A code represents (in the -database) the actual executable used to run the calculation. Find the pk -of such a node in the graph and type - -```console -verdi code show -``` - -The command prints information on the plugin used to interface the code -to AiiDA, the remote machine on which the code is executed, the path of -its executable, etc. To have a list of all available codes type - -```console -verdi code list -a -A -``` - -Among the entries of the output you should also find the code just -shown. - -Similarly, the list of computers on which AiiDA can submit calculations -is accessible by means of the command - -```console -verdi computer list -a -``` - -(`-a` shows all computers, also the one imported in your -database but that you did not configure, i.e., to which you don’t have -access). Details about each computer can be obtained by the command - -```console -verdi computer show -``` - -Now you have the tools to answer the question: - -What is the scheduler installed on the computer where the calculations -of the graph have run? - -### Calculation results - -The results of a calculation can be accessed directly from the -calculation node. Type in the terminal - -```console -verdi calculation res -``` - -which will print the output dictionary of the “scalar” results parsed by -AiiDA at the end of the calculation. Note that this is actually a -shortcut for - -```console -verdi data parameter show -``` - -where `pk2` refers to the ParameterData node attached as an output of -the calculation node, with link name `output_parameters`. - -By looking at the output of the command, what is the averate total -energy of the calculation having pk=3006? - -One can access the component-specific output parameters in the same way -(using pk = 1130 that corresponds to the component 0, methane) - -```console -verdi data parameter show -``` - -Similarly to what you did for the calculation inputs, you can access the -output files via the commands - -```console -verdi calculation outputls -``` - -and - -```console -verdi calculation outputcat -p -``` - -Use the latter to verify that the average total energy that you have -found in the last step has been extracted correctly from the output file -(Hint: filter the lines containing the string “Total energy”, e.g. using -`grep Total energy: -A 8`, to isolate the relevant lines). diff --git a/docs/pages/2018_PRACE_MaX/index.rst b/docs/pages/2018_PRACE_MaX/index.rst deleted file mode 100644 index 5b15dead..00000000 --- a/docs/pages/2018_PRACE_MaX/index.rst +++ /dev/null @@ -1,55 +0,0 @@ -+-----------------+-------------------------------------------------------------------+ -| Related resources | -+=================+===================================================================+ -| Virtual Machine | `Quantum Mobile 18.04.0-tutorial`_ (`installation instructions`_) | -+-----------------+-------------------------------------------------------------------+ -| python packages | `aiida-core 1.0.0a1`_, `aiida-quantumespresso 3.0.0a1`_ | -+-----------------+-------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.2.1`_ | -+-----------------+-------------------------------------------------------------------+ - -.. _Quantum Mobile 18.04.0-tutorial: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/quantum_mobile_aiida_tutorial_May_2018.ova -.. _installation instructions: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/INSTALL_aiida_tutorial_May_2018.txt -.. _aiida-core 1.0.0a1: https://github.com/aiidateam/aiida_core/releases/tag/v1.0.0a1 -.. _aiida-quantumespresso 3.0.0a1: https://github.com/aiidateam/aiida-quantumespresso/releases/tag/v3.0.0a1 -.. _Quantum Espresso 6.2.1: https://github.com/QEF/q-e/releases/tag/qe-6.2.1 - -The `PRACE-MaX Tutorial on high-throughput computations: general methods -and applications using AiiDA `__ -was held from May 30th to June 1st, 2018 at the Cineca HPC facility in -Bologna, Italy. - - -PRACE-MaX tutorial on high-throughput computations -================================================== - -The instructions below have been converted from the `original LaTeX source -`__. -See also the `original PDF `_. - -Sections --------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - Preliminaries <./sections/preliminaries> - ./sections/verdi_cmdline - ./sections/verdi_shell - ./sections/calculations - ./sections/querybuilder - ./sections/workflows - ./sections/bands - -Appendices ----------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/appendix_input_validation - ./sections/appendix_restarting_calculations - ./sections/appendix_queries - ./sections/appendix_workflow_logic diff --git a/docs/pages/2018_PRACE_MaX/notebooks/.gitignore b/docs/pages/2018_PRACE_MaX/notebooks/.gitignore deleted file mode 100644 index c7ec5d9b..00000000 --- a/docs/pages/2018_PRACE_MaX/notebooks/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -querybuilder-tutorial.ipynb -querybuilder-solutions.ipynb diff --git a/docs/pages/2018_PRACE_MaX/notebooks/querybuilder-template.ipynb b/docs/pages/2018_PRACE_MaX/notebooks/querybuilder-template.ipynb deleted file mode 100644 index c350b5ee..00000000 --- a/docs/pages/2018_PRACE_MaX/notebooks/querybuilder-template.ipynb +++ /dev/null @@ -1,1013 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AiiDA's QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "hide_input": false - }, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from datetime import datetime, timedelta\n", - "import numpy as np\n", - "from aiida import load_dbenv, is_dbenv_loaded\n", - "from matplotlib import gridspec, pyplot as plt\n", - "if not is_dbenv_loaded():\n", - " load_dbenv()\n", - "from aiida.orm import load_node, Node, Group, Computer, User\n", - "from aiida.orm import CalculationFactory, DataFactory\n", - "from aiida.orm.calculation.job import JobCalculation\n", - "from aiida.orm.querybuilder import QueryBuilder\n", - "\n", - "PwCalculation = CalculationFactory('quantumespresso.pw')\n", - "StructureData = DataFactory('structure')\n", - "KpointsData = DataFactory('array.kpoints')\n", - "ParameterData = DataFactory('parameter')\n", - "UpfData = DataFactory('upf')\n", - "def plot_results(query_res):\n", - " \"\"\"\n", - " :param query_res: The result of an instance of the QueryBuilder\n", - " \"\"\"\n", - " smearing_unit_set,magnetization_unit_set,pseudo_family_set = set(), set(), set()\n", - " # Storing results:\n", - " results_dict = {}\n", - " for pseudo_family, formula, smearing, smearing_units, mag, mag_units in query_res:\n", - " if formula not in results_dict:\n", - " results_dict[formula] = {}\n", - " # Storing the results:\n", - " results_dict[formula][pseudo_family] = (smearing, mag)\n", - " # Adding to the unit set:\n", - " smearing_unit_set.add(smearing_units)\n", - " magnetization_unit_set.add(mag_units)\n", - " pseudo_family_set.add(pseudo_family)\n", - "\n", - " # Sorting by formula:\n", - " sorted_results = sorted(results_dict.items())\n", - " formula_list = zip(*sorted_results)[0]\n", - " nr_of_results = len(formula_list)\n", - "\n", - " # Checks that I have not more than 3 pseudo families.\n", - " # If more are needed, define more colors\n", - " #pseudo_list = list(pseudo_family_set)\n", - " if len(pseudo_family_set) > 3:\n", - " raise Exception('I was expecting 3 or less pseudo families')\n", - "\n", - " colors = ['b', 'r', 'g']\n", - "\n", - " # Plotting:\n", - " plt.clf()\n", - " fig=plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None)\n", - " gs = gridspec.GridSpec(2,1, hspace=0.01, left=0.1, right=0.94)\n", - "\n", - " # Defining barwidth\n", - " barwidth = 1. / (len(pseudo_family_set)+1)\n", - " offset = [-0.5+(0.5+n)*barwidth for n in range(len(pseudo_family_set))]\n", - " # Axing labels with units:\n", - " yaxis = (\"Smearing energy [{}]\".format(smearing_unit_set.pop()),\n", - " \"Total magnetization [{}]\".format(magnetization_unit_set.pop()))\n", - " # If more than one unit was specified, I will exit:\n", - " if smearing_unit_set:\n", - " raise Exception('Found different units for smearing')\n", - " if magnetization_unit_set:\n", - " raise Exception('Found different units for magnetization')\n", - " \n", - " # Making two plots, upper for the smearing, the lower for magnetization\n", - " for index in range(2):\n", - " ax=fig.add_subplot(gs[index])\n", - " for i,pseudo_family in enumerate(pseudo_family_set):\n", - " X = np.arange(nr_of_results)+offset[i]\n", - " Y = np.array([thisres[1][pseudo_family][index] for thisres in sorted_results])\n", - " ax.bar(X, Y, width=0.2, facecolor=colors[i], edgecolor=colors[i], label=pseudo_family)\n", - " ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15*index+5)\n", - " ax.set_xlim(-0.5, nr_of_results-0.5)\n", - " ax.set_xticks(np.arange(nr_of_results))\n", - " if index == 0:\n", - " plt.setp(ax.get_yticklabels()[0], visible=False)\n", - " ax.xaxis.tick_top()\n", - " ax.legend(loc=3, prop={'size': 18})\n", - " else:\n", - " plt.setp(ax.get_yticklabels()[-1], visible=False)\n", - " for i in range(0, nr_of_results, 2):\n", - " ax.axvspan(i-0.5, i+0.5, facecolor='y', alpha=0.2)\n", - " ax.set_xticklabels(list(formula_list),rotation=90, size=14, ha='center')\n", - " plt.show()\n", - "\n", - "def generate_query_graph(qh, out_file_name):\n", - "\n", - " def draw_vertice_settings(idx, vertice, **kwargs):\n", - " \"\"\"\n", - " Returns a string with all infos needed in a .dot file to define a node of a graph.\n", - " :param node:\n", - " :param kwargs: Additional key-value pairs to be added to the returned string\n", - " :return: a string\n", - " \"\"\"\n", - " if 'calculation' in vertice['type']:\n", - " shape = \"shape=polygon,sides=4\"\n", - " elif 'code' in vertice['type']:\n", - " shape = \"shape=diamond\"\n", - " else:\n", - " shape = \"shape=ellipse\"\n", - " filters = kwargs.pop('filters', None)\n", - " additional_string = \"\"\n", - " if filters:\n", - " additional_string += '\\nFilters:'\n", - " for k,v in filters.items():\n", - " additional_string += \"\\n {} : {}\".format(k,v)\n", - "\n", - "\n", - " label_string = \" ('{}')\".format(vertice['tag'])\n", - "\n", - " labelstring = 'label=\"{} {}{}\"'.format(\n", - " vertice['type'], #.split('.')[-2] or 'Node',\n", - " label_string,\n", - " additional_string)\n", - " #~ return \"N{} [{},{}{}];\".format(idx, shape, labelstring,\n", - " return \"{} [{},{}];\".format(vertice['tag'], shape, labelstring)\n", - " nodes = {v['tag']:draw_vertice_settings(idx, v, filters=qh['filters'][v['tag']]) for idx, v in enumerate(qh['path'])}\n", - " links = [(v['tag'], v['joining_value'], v['joining_keyword']) for v in qh['path'][1:]]\n", - "\n", - " with open('temp.dot','w') as fout:\n", - " fout.write(\"digraph G {\\n\")\n", - " for l in links:\n", - " fout.write(' {} -> {} [label=\" {}\"];\\n'.format(*l))\n", - " for _, n_values in nodes.items():\n", - " fout.write(\" {}\\n\".format(n_values))\n", - "\n", - " fout.write(\"}\\n\")\n", - " import os\n", - " os.system('dot temp.dot -Tpng -o {}'.format(out_file_name))\n", - "\n", - "def store_formula_in_extra():\n", - " qb = QueryBuilder()\n", - " qb.append(StructureData, filters={'extras':{'!has_key':'formula'}})\n", - " for structure, in qb.all():\n", - " structure.set_extra('formula', structure.get_formula(mode='count'))\n", - "\n", - "store_formula_in_extra()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction to the QueryBuilder\n", - "As you will use `AiiDA` to run your calculations, the database that stores all the data and the provenance, will quickly grow to be very large. So in order to find a needle that we might be looking for in this big haystack, we need an efficient excavation and search tool. The tool that `AiiDA` provides to do exactly this is called the `QueryBuilder`. The `QueryBuilder` acts as the gatekeeper to your database, that you can ask questions about the contents of your database (also referred to as queries), by specifying what it is that you are looking for. In this part of the tutorial, we will focus on how to use the `QueryBuilder` to make these queries and understand/use the results.\n", - "\n", - "In order to use the `QueryBuilder`, we first need to import it. We can accomplish this by executing the `import` statement in the following cell. Go ahead and select the next cell, and press `Shift+Enter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from aiida.orm.querybuilder import QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we can ask the `QueryBuilder` questions about our database, we first need to create an instance of it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an instance of our `QueryBuilder` which we named `qb`, we are ready to start asking it about the contents of our database. For example, we may want to know exactly how many nodes there are in our database. To tell `qb` that we are interested in all the occurrences of the `Node` class in our database, we `append` it to the list of objects it should find. The method is called `append`, because, as we will see later, you can append to a `QueryBuilder` instance consecutively, as if you had a list. \n", - "What we are doing is querying a graph, and for every vertice of the graph in our subquery, we will use one `append` call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb.append(Node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now narrowed down the scope of `qb` to just the nodes that are present in our database. To learn how many nodes there are exactly, all we have to do is to ask `qb` to count them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb.count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now as you may have learned in previous sections of the tutorial, nodes come in different kinds and flavors. For example, all our crystal structures that we have stored in the database, are saved in a node that is of the type `StructureData`. If instead of all the nodes, rather we would like to count only the structure nodes, we simply tell our `QueryBuilder` to narrow its scope to only objects of the `StructureData` type. Since we are creating a new query, we have to create a new instance of the `QueryBuilder`. In the next cell, we have typed part of the code to count all the structure nodes. See if you can finish the line with the comment, to tell the `QueryBuilder` that you are only interested in `StructureData` nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "qb.append() # How do we finish this line to tell the query builder to count only the structure nodes?\n", - "qb.count()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.count()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of just counting how many structure nodes we have, we may also actually want to see some of them. This is as easy as telling our `QueryBuilder` that we are not interested in the `count` but rather that we want to retrieve `all` the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this command is very literal and does in fact retrieve **all** the structure nodes that are stored in your database, which may become very slow if your database becomes very large. One solution is to simply tell the `QueryBuilder` that we are for example only interested in 5 structure nodes. This can be done with the `limit` method as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to simply use the concept of array slicing, native to python, to specify a subset of the total return set to be returned. Notice that this example can be very slow in big databases. When you want performance, use the functionality native to the QueryBuilder!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb.limit(None)\n", - "qb.all()[:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to know a little bit more about the retrieved structure nodes, we can loop through our results. This allows one for example to print the formula of the structures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "for structure, in qb.all():\n", - " print structure.get_formula()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just a simple example how we can employ the `QueryBuilder` to get details about the contents of our database. We have now seen simple queries for the `Node` and `StructureData` classes of nodes, but the same rules apply to all the `AiiDA` node classes. For example we may want to count the number of entries for each of the node classes in the following list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "class_list = [Node, StructureData, KpointsData, ParameterData, UpfData]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the tools we have learned so far, we can build a table of the number of occurrences of each of these node classes that are stored in our database. We simply loop over the `class_list` and create a `QueryBuilder` for each and count the entries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "for class_name in class_list:\n", - " qb = QueryBuilder()\n", - " qb.append(class_name)\n", - "#TUT_USER_START\n", - " print # Finish this line to print the results!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - " print '{:>15} | {:6}'.format(class_name.__name__, qb.count())\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, you should see something like the following, where of course the numbers may differ for your database\n", - "\n", - "Class name | Entries\n", - "---------------|--------\n", - " Node | 10273 \n", - " StructureData | 271 \n", - " KpointsData | 953 \n", - " ParameterData | 2922 \n", - " UpfData | 85 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Projection and filters\n", - "Up till now we have always asked the `QueryBuilder` to return the entire node, of whatever class of nodes that we specified. However, we might not necessarily be interested in all the node's properties, but rather just a select set or even just a single property. We can tell the `QueryBuilder` which properties we would like returned, by asking it to **project** those properties in the result. For example, we may only want to get the uuid's of a set of nodes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['uuid'])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We inform the `QueryBuilder` that we are only interested in the `uuid` property of the `Node` class by specifing the `project` keyword in the `append` call. Note that the value that we assign to `project` is a list, as we may want to specify more than one property. See if you can get the `QueryBuilder` to return *both* the `id` and the `uuid` of the first 5 `Node`'s in the following cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_USER_START\n", - "qb.append(Node, project=)#? What should the value be for the project key\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Node, project=['id', 'uuid'])\n", - "#TUT_SOLUTION_END\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To give you an idea of the various properties that you can project for some of the `AiiDA` node classes you can consult the following table.\n", - "Note that this is by no means an exhaustive list\n", - "\n", - "Class | Properties\n", - "---------|-----------\n", - "Node | `id`, `uuid`, `type`, `label`, `description`, `ctime`, `mtime`\n", - "Computer | `id`, `uuid`, `name`, `hostname`, `description`, `enabled`, `transport_type`, `scheduler_type`\n", - "User | `id`, `email`, `first_name`, `last_name`, `institution`\n", - "Group | `id`, `uuid`, `name`, `type`, `time`, `description`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same properties can also be used to *filter* for specific nodes in your database. Up till now, we only ever asked to return the `QueryBuilder` all the instances of a certain type of node, or at best a limited number of those. But we might be interested in a very specific node. For example, we may have the `id` of a certain node and we would like to know when it was created and last modified. We can tell the `QueryBuilder` to select nodes that only match that criterion, by telling it to **filter** based on that property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['ctime', 'mtime'], filters={'id': {'==': 1}})\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the syntax of the `filters` keyword. The value is a dictionary, where the keys indicate the node property that it is supposed to operate on, in this case the `id` property. The value of that key is again itself a dictionary, where the key indicates the logical operator `==` and the value corresponds to the value of the property.\n", - "\n", - "You may have multiple criteria that you want to filter for, in which case you can use the logical `or` and `and` operators. Let's say, for example, that you want the `QueryBuilder` to retrieve all the `StructureData` nodes that have a certain `label` **and** where created no longer than 10 days ago. You can translate this criterion by making use of the `and` operator which allows you to specify multiple filters that all have to be satisfied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(\n", - " Node, \n", - " filters={\n", - " 'and': [\n", - " {'ctime': {'>': datetime.now() - timedelta(days=12)}},\n", - " {'label': {'==':'graphene'}}\n", - " ]\n", - " }\n", - ")\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will have noticed that the `>` operator, and its related operators, can work with python `datetime` objects. These are just a few of the operators that `QueryBuilder` understands. Below you find a table with the various logical operators that you can use\n", - "\n", - "Operator | Data type | Example | Description\n", - "---------------------|-----------------------|------------------------------------|------------------\n", - "`==` | all | `{'==': '12'}` | equality operator\n", - "`in` | all | `{'in':['FINISHED', 'PARSING']}` | member of a set\n", - "`<`, `>`, `<=`, `>=` | float, int, datetime | `{'>': 5.2}` | size comparison operator\n", - "`like` | char, str | `{'like': 'calculation%'}` | string comparison, `%` is wildcard\n", - "`ilike` | char, str | `{'ilike': 'caLCulAtion%'}` | string comparison, capital insensitive\n", - "`or` | | `{'or': [{'<': 5.3}, {'>': 6.3}]}` | logical or operator\n", - "`and` | | `{'and': [{'>=': 2}, {'<=': 6}]}` | logical and operator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, try to write a query below that will retrieve all `Group` nodes whose `name` property starts with the string `tutorial`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Write your query here\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'name': {'like': 'tutorial%'}})\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining relationships between query clauses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how we can use the `QueryBuilder` to search the database for entries of a specific node type, potentially projecting only specific properties and filtering for certain property values. However, our nodes do not live in a vacuum, but they are often related to one another. Especially, there will be interesting relationships between different types of nodes. Therefore we would like to be able to search for nodes, based on a certain relationship that they might have with other nodes. Consider for example that you have a `StructureData` node that was produced by some calculation. How would we be able to retrieve that calculation?\n", - "\n", - "To accomplish this, we need to be able to tell the `QueryBuilder` what the relationship is between the nodes that we are interested in. If we take the example of the structure node but abstracting it to all structure nodes, we could do the following to find all the structure nodes that were generated as an output by a `PwCalculation` node." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are looking for descendants of `PwCalculation` nodes, so we `append` it to a `QueryBuilder` instance. In the future, we need to be able to reference to this clause and therefore we give it a `tag` with the `tag` keyword." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb.append(StructureData, output_of='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal was to find `StructureData` nodes, so we `append` that to the `qb`. However, we didn't want to find just any `StructureData` nodes; they had to be an output of `PwCalculation`. Note how we expressed this relation by the `output_of` keyword and using the `tag` name `calculation` that we had just assigned in the previous `append` statement.\n", - "Now all we have to do is execute the query to retrieve our structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we did can be visualized schematically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query1.png')\n", - "Image(filename='query1.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `output_of` keyword is but one of many potential relationships that exist between the various `AiiDA` nodes, that are implemented in the `QueryBuilder`. The table below gives an overview of the implemented relationships, which nodes they are defined for and which relation it implicates." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entity from\t| Entity to\t| Relationship | Explanation\n", - "------------|-----------|---------------|------------\n", - "Node | Node | input_of | One node as input of another node\n", - "Node | Node | output_of | One node as output of another node\n", - "Node | Node | ancestor_of | One node as the ancestor of another node\n", - "Node | Node | descendant_of | One node as descendant of another node\n", - "Group | Node | group_of | The group of a node\n", - "Node | Group | member_of | The node is a member of a group\n", - "Computer | Node | computer_of | The computer of a node\n", - "Node | Computer | has_computer | The node of a computer\n", - "User | Node | creator_of | The creator of a node is a user\n", - "Node | User | created_by | The node was created by a user" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, see if you can write a query that will return all the `UpfData` nodes that are a member of a `Group` whose name starts with the string `SSSP`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'name': {'like': 'SSSP%'}}, tag='group')\n", - "qb.append(UpfData, member_of='group')\n", - "qb.all()\n", - "#TUT_SOLUTION_END\n", - "# Here I also visualize what's going on:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query2.png')\n", - "Image(filename='query2.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Attributes and extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In section 2, we showed you how you can `project` specific properties of a `Node` and gave a list of properties that a `Node` instance possesses. Since then, we have come across a lot of different `AiiDA` data nodes, such as `StructureData` and `UpfData`, that were secretly `Node`'s in disguise. Or to put it correctly, as `AiiDA` employs the object-oriented programming paradigm, both `StructureData` and `UpfData` are examples of subclasses of the `Node` class and therefore inherit its properties. That means that whatever property a `Node` has, both `StructureData` and `UpfData` will have too. However, there is a semantic difference between a `StructureData` node and a `UpfData`, and so we may want to add a property to one that would not make sense for the other. To solve this, `AiiDA` introduces the concept of `attributes`. These are similar to properties, except that they are specific to the `Node` type that they are attached to. This allows you to add an `attribute` to a certain node, without having to change the implementation of all the others.\n", - "\n", - "For example, the `ParameterData` nodes that are generated as output of `PwCalculation`'s may have an attribute named `wfc_cutoff`. To project for this particular `attribute`, one can use exactly the same syntax as shown in section 2 for the regular `Node` properties, and one has to only prepend `attributes.` to the attribute name. Demonstration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='pw')\n", - "qb.append(ParameterData, output_of='pw', project=[\"attributes.wfc_cutoff\"])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not every `ParameterData` node has to have this attribute, in which case the `QueryBuilder` will return the python keyword `None`. Similar to the `attributes`, nodes also can have `extras`, which work in the same way, except that `extras` are mutable, which means that their value can be changed even after a node instance has been stored.\n", - "\n", - "If you are not sure which attributes a given node has, you can use the `get_attrs()` method to simply retrieve them all. It will return a dictionary with all the attributes the node has." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation)\n", - "node, = qb.first()\n", - "node.get_attrs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The element type symbol of a pseudopotential, that is represented by a `UpfData` node, is stored in the `element` attribute. Using the knowledge that filtering on attributes works exactly as for normal node properties, see if you can write a query that will search your database for pseudopotentials for silicon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_SOLUTION_START\n", - "qb.append(UpfData, filters={'attributes.element': {'==': 'Si'}})\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**For more exercises on relationships and attributes/extras, have a look at the appendix of the provided PDF.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 5. A small high-throughput study" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The following section assumes that a specific dataset is present in your current AiiDA profile. If you are not running this script on the Virtual Machine of the AiiDA tutorial, this script will not produce the desired output.\n", - "You can download the Virtual Machine image at www.aiida.net/tutorial along with the tutorial text.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part of the tutorial, we will focus on how to systematically retrieve, query and analyze the results of\n", - "multiple calculations using AiiDA. We know you’re able to do this yourself, but to save time, a set of calculations\n", - "have already been done with AiiDA for you on 57 perovskites, using three different pseudopotential families (LDA,\n", - "PBE and PBESOL, all from GBRV 1.2). These calculations are spin-polarized (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. The idea of this part of the tutorial is to “screen” for magnetic and metallic perovskites in a “high-throughput” way. As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. Once more check the list of groups in your database by typing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "!verdi group list -A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The calculations needed for this task were put in three different groups whose names start with \"tutorial\" (one for each pseudopotential family). The main task is to make a plot showing, for all perovskites and for each pseudopotential family, the total magnetization and the -T S contribution from the smearing to the total energy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start building the query\n", - "So we first of all need to instantiate a QueryBuilder instance. We `append` the groups of interest, which means that we select only groups that start with the string `tutorial_`. We can execute the query after this append (this will not affect the final results) and check whether we have retrieved 3 groups." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiating QB:\n", - "qb = QueryBuilder()\n", - "# Appending the groups I care about:\n", - "qb.append(Group, filters={'name':{'like':'tutorial_%'}}, project='name', tag='group')\n", - "# Visualize:\n", - "print \"Groups:\", ', '.join([g for g, in qb.all()])\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query3.png')\n", - "Image(filename='query3.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the calculations that are members of each group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# I want every PwCalculation that is a member of the specified groups:\n", - "#TUT_USER_START\n", - "qb.append(PwCalculation, tag='calculation', member_of=) # Complete the function call with the correct relationship-tag!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(PwCalculation, tag='calculation', member_of='group')\n", - "#TUT_SOLUTION_END\n", - "#Visualize\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query4.png') \n", - "Image(filename='query4.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the structures that are input of the calculation. \n", - "We extend the query to include the structures that are input of the calculations that match the query so far.\n", - "This means that we `append` StructureData, and defining the relationship with the calculation with corresponding keyword.\n", - "For simplicity the formulas have been added in the extras of each structure node under the key `formula`. \n", - "The function that does this is called `store_formula_in_extra` and can be found in the second cell. It also uses the QueryBuilder! Project the formula, stored in the extras under the key `formula`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(StructureData, project=, tag='structure', input_of=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(StructureData, project=['extras.formula'], tag='structure', input_of='calculation')\n", - "#TUT_SOLUTION_END\n", - "# Visualize:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query5.png')\n", - "Image(filename='query5.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the output of the calculation\n", - "Every successful PwCalculation has in output a ParameterData instance that stores the results as key-value pairs. You can find these pairs among the attributes. To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. For convenience, the units are also added as key/value pairs (with the same key name, but with _units appended). \n", - "Extend the query so that also the output ParameterData of each calculation is returned. Project only\n", - "the attributes relevant to your analysis.\n", - "\n", - "Project:\n", - "\n", - "* The smearing contribution and the units\n", - "* The magnetization and the units." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(ParameterData,tag='results', project=['attributes.energy_smearing',...], output_of=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(ParameterData,tag='results',\n", - " project=['attributes.energy_smearing', 'attributes.energy_smearing_units',\n", - " 'attributes.total_magnetization', 'attributes.total_magnetization_units',\n", - " ], output_of='calculation'\n", - " )\n", - "#TUT_SOLUTION_END\n", - "\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query6.png') \n", - "Image(filename='query6.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print the query results\n", - "We can print the results to see if everything worked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "results = qb.all()\n", - "for item in results:\n", - " print ', '.join(map(str, item))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results\n", - "Getting a long list is not always helpful. We prepared a function that visualizes the results of the query:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "plot_results(results)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/pages/2018_PRACE_MaX/sections/appendix_input_validation.md b/docs/pages/2018_PRACE_MaX/sections/appendix_input_validation.md deleted file mode 100644 index 8b62e419..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/appendix_input_validation.md +++ /dev/null @@ -1,73 +0,0 @@ -Calculation input validation -============================ - -This appendix shows additional ways to debug possible errors with QE, how to use a useful tool that we included in AiiDA to validate the input to Quantum ESPRESSO (and possibly suggest the correct name to mispelled keywords) - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. Let’s check for example this input dictionary, where we inserted two mistakes: - -``` python -parameters_dict = { - "CTRL": { - "calculation": "scf", - "restart_mode": "from_scratch", - }, - "SYSTEM": { - "nat": 2, - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } -} -``` - -The two mistakes in the dictionary are the following. First, we wrote a wrong namelist name (’CTRL’ instead of ’CONTROL’). Second, we inserted the number of atoms explicitly: while that is how the number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system: in fact this information is already contained in the StructureData. Modify the script with this ParameterData. In this case, we use a tool (the input validator that we provide in AiiDA) to check the input file before submitting. Therefore, the behavior will be slightly different from the previous example: the plugin will check for some keys and will refuse to submit the calculation. This kind of mistakes is revealed by either - -- Submitting a calculation. You will see the calculation ending up in the SUBMISSIONFAILED status. Check therefore the logs to recognize the source of the error. - -- Submitting a test. You will not be able to successfully create the test and the traceback will guide you to the problem. - -Over the time this kind of trivial mistakes can be annoying, but they can be avoided with a utility function that checks the “grammar” of the input parameters. In your script, after you defined the parameters\_dict, you can validate it with the command (note that you need to pass also the input crystal structure, `s`, to allow the validator to perform all needed checks): - -``` python -PwCalculation = CalculationFactory('quantumespresso.pw') -validated_dict = PwCalculation.input_helper(parameters_dict, structure=s) -parameters = ParameterData(dict=validated_dict) -``` - -The `input_helper` method will check for the correctness of the input parameters. If misspelling or incorrect keys are detected, the method raises an exception, which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, without providing the namelists (useful if you don’t remember where to put some variables in the input file). Hence, you can provide to `input_helper()` a dictionary like this one: - -``` python -parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, -} -validated_dict = PwCalculation.input_helper( - parameters_dict, structure=s, flat_mode=True) -``` - -If you print the `validated_dict`, it will look like: - -``` python -{ - "CONTROL": { - "calculation": "scf", - "tstress": True, - "tprnfor": True, - }, - "SYSTEM": { - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } - } -``` diff --git a/docs/pages/2018_PRACE_MaX/sections/appendix_queries.rst b/docs/pages/2018_PRACE_MaX/sections/appendix_queries.rst deleted file mode 100644 index 8420eff1..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/appendix_queries.rst +++ /dev/null @@ -1,102 +0,0 @@ -Queries in AiiDA - Examples and Exercises -========================================= - -Optional exercises on relationships (Task 3) --------------------------------------------- - -**Hint for the exercises:** - -- You can have projections on properties of more than one entity in - your query. You just have to add the *project* key (specifying the - list of properties that you want to project) along with the - corresponding entity when you append it. - -**Exercises:**   Try to write the following queries: - -- Find all descendants of a StructureData with a certain uuid. Print - both the StructureData and the descendant. - -- Find all the FolderData created by a specific user. - -Optional exercises on attributes and extras (Task 4) ----------------------------------------------------- - -**Hint for the exercises:** - -- You can easily order or limit the number of the results by using the - *order_by()* and *limit()* methods of the QueryBuilder. For example, - we can order all our job calculation by their *id* and limit the - result to the first 10 as follows: - - .. code:: python - - qb = QueryBuilder() - qb.append(JobCalculation, tag='calc') - qb.order_by({'calc':'id'}) - qb.limit(10) - qb.all() - -**Exercises:** - -- Write a code snippet that informs you how many pseudopotentials you - have for each element. - -- Smearing contribution to the total energy for calculations: - - 1. Write a query that returns the smearing contribution to the energy - stored in some instances of ParameterData. - - 2. Extend the previous query to also get the input structures. - - 3. Which structures have a smearing contribution to the energy - smaller or equal to -0.02? - -Summarizing what we learned by now - An example ------------------------------------------------ - -At this point you should be able to do queries with projections, joins -and filters. Moreover, you saw how to apply filters and projections even -on attributes and extras. Let’s discover the full power of the -QueryBuilder with a complex graph query that allows you to project -various properties from different nodes and apply different filters and -joins. - -Imagine that you would like to get the smearing energy for all the -calculations that have finished and have a Sn\ :sub:`2`\ O\ :sub:`3` as input. -Moreover, besides from the smearing energy, you would like to -print the units of this energy and the formula of the structure that was -given to the calculation. The graphical representation of this query can -be seen in :numref:`fig_qb_example_2_2018` and the actual query follows: - -.. _fig_qb_example_2_2018: -.. figure:: ../../../assets/2018_PRACE_MaX/qb_example_2.png - :width: 50% - :align: center - - Complex graph query. - -.. code:: python - - qb = QueryBuilder() - qb.append( - StructureData, - project=["extras.formula"], - filters={"extras.formula":"Sn2O3"}, - tag="structure" - ) - qb.append( - Calculation, - tag="calculation", - output_of="structure" - ) - qb.append( - ParameterData, - tag="results", - filters={"attributes.energy_smearing":{"<=":-0.0001}}, - project=[ - "attributes.energy_smearing", - "attributes.energy_smearing_units", - ], - output_of="calculation" - ) - qb.all() diff --git a/docs/pages/2018_PRACE_MaX/sections/appendix_restarting_calculations.md b/docs/pages/2018_PRACE_MaX/sections/appendix_restarting_calculations.md deleted file mode 100644 index a15dbc81..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/appendix_restarting_calculations.md +++ /dev/null @@ -1,77 +0,0 @@ -Restarting calculations -======================= - -Up to now, we have only presented cases in which we were passing wrong input parameters to the calculations, which required us to modify the input scripts and relaunch calculations from scratch. There are several other scenarios in which, more generally, we need to restart calculations from the last step that they have executed. For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to restart and/or modify a calculation that has run previously. As an example, let us first submit a total energy calculation using a parameters dictionary of the form: - -``` python -parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, -} -``` - -and submit the calculation with this input. In this case, we set a very low number of self consistent iterations (3), too small to be able to reach the desired accuracy of 10\(^{-14}\): therefore the calculation will not reach a complete end and will be flagged in a FAILED state. However, there is no mistake in the parameter dictionary. - -Now, create a new script file, where you will try to restart and correct the input dictionary. We first load the calculation that has just failed (let’s call it `c1`) - -``` python -old_calc = load_node(PK) -``` - -(take care of using the correct PK). Then, create a new Builder `builder` which is set to reuse all inputs from the previous step, with a few adaptations to the input parameters that might be needed by the code to properly deal with restarts. - -``` python -from aiida_quantumespresso.utils.restart import create_restart_pw -builder = create_restart_pw( - old_calc, - use_output_structure=False, - restart_from_beginning=False, - force_restart=True) -``` - -The flag usage (most of them are optional) is: - -- `use_output_structure`: if True and `old_calc` has an output structure, the new calculation will use it as input; - -- `restart_from_beginning`: if False the new calculation will start from the charge density of `old_calc`, it will start from the beginning otherwise; - -- `force_restart`: if True, the new calculation will be created even if `old_calc` is not in a FINISHED job state. - -Since this calculation has exactly the same parameters of before, we have to modify the input parameters and increase `electron_maxstep` to a larger value. To this aim, let’s load the dictionary of values and change it - -``` python -old_parameters = builder.parameters -parameters_dict = old_parameters.get_dict() -parameters_dict['ELECTRONS']['electron_maxstep'] = 100 -``` - -Note that you cannot modify the `old_parameters` object: it has been used by calculation `c1` and is saved in the database; hence a modification would break the provenance. We have to create a new ParameterData and pass it to c2: - -``` python -ParameterData = DataFactory('parameter') -new_parameters = ParameterData(dict=parameters_dict) -builder.parameters = new_parameters -``` - -Now you can launch the new calculation - -``` python -from aiida.work.run import submit -new_calc = submit(builder) -print new_calc.pk -``` - -that this time can proceed until the end and return converged total energy. Using the restart method, the script is much shorter than the one needed to launch a new one from scratch: you didn’t need to define pseudopotentials, structures and k-points, which are the same as before. You can indeed inspect the new calculation to check that now it actually completed successfully. diff --git a/docs/pages/2018_PRACE_MaX/sections/appendix_workflow_logic.md b/docs/pages/2018_PRACE_MaX/sections/appendix_workflow_logic.md deleted file mode 100644 index 94cc904b..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/appendix_workflow_logic.md +++ /dev/null @@ -1,101 +0,0 @@ -More workflow logic: while loops and conditional statements -=========================================================== - -In the previous sections, you have been introduced to WorkChains, and the reason for using them over “standard” workfunctions (i.e., functions decorated with `@wf`). - -However, in the example of Sec. [sec:workchainsimple], the `spec.outline` was quite simple, with a “static” sequence of two steps. Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). To support this scenario, the `spec.outline` can support logic: *while* loops and *if/elif/else* blocks. The simplest way to explain it is to show an example: - -``` python -from aiida.work.workchain import if_, while_ - -spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), -) -``` - -that would *roughly* correspond, in a python syntax, to: - -``` python -s1() -if isA(): - s2() -elif isB(): - s3() -else: - s4() -s5() -while condition(): - s6() -``` - -The only constraint is that condition functions (in the example above `isA`, `isB` and `condition`) must be class methods that returns `True` or `False` depending on whether the condition is met or not. - -A suggestion on how to write new workchains: Use the outline to help you in designing the logic. First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. Then, define one by one the methods. For example, we have prepared a simple workfunction to optimize the lattice parameter of silicon efficiently using a Newton’s algorithm on the energy derivative, i.e. the pressure \(p=-dE/dV\). You can find it the code at `tutorial_scripts/pressure_convergence.py`. The outline looks like this: - -``` python -spec.outline( - cls.init, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.report -) -``` - -This outline already roughly explains the algorithm: after an initialization (`init`) and putting the first step (number zero) in the ctx (`put_step0_in_ctx`), a function to move to the next step is called (`move_next_step`). This is iterated while a given convergence criterion is not met (`not_converged`). Finally, some reporting is done, including returning some output nodes (`report`). - -If you are interested in the details of the algorithm, you can inspect the file. The main ideas are described here: - -init -Generate a `pw.x` calculation for the input structure (with volume \(V\)), and one for a structure where the volume is \(V+4\text{\AA}^3\) (just to get a closeby volume). Store the results in the context as `r0` and `r1` - -put\_step0\_in\_ctx -Store in the context \(V\), \(E(V)\) and \(dE/dV\) for the first calculation `r0` - -move\_next\_step -This is the most important function. Calculate \(V\), \(E(V)\) and \(dE/dV\) for `r1`. Also, estimate \(d^2E/dV^2\) from the finite difference of the first derivative of `r0` and `r1` (helper functions to achieve this are provided). Get the \(a\), \(b\) and \(c\) coefficients of a parabolic fit \(E=aV^2 + bV + c\) and estimated the expected minimum of the EOS function as the minimum of the fit \(V_0=-b/2a\). Finally, replace `r0` with `r1` in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume \(V_0\), that will be stored in the context replacing `r1`. In this way, at the next iteration `r0` and `r1` will contain the latest two simulations. Finally, at each step some relevant information (coefficients \(a\), \(b\) and \(c\), volumes, energies, energy derivatives, ...) are stored in a list called `steps`. This whole list is stored in the context because it provides quantities to be preserved between different workfunction steps. - -not\_converged -Return `True` if convergence has not been achieved yet. Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold (`volume_tolerance`). - -report -This is the final step. Mainly, we return the output nodes: `steps` with the list of results at each step, and `structure` with the final converged structure. - -The results returned in `steps` can be used to represent the evolution of the minimisation algorithm. A possible way to visualize it is presented in Fig. [fig:convpressure], obtained with an initial lattice constant of \(a_{\text{lat}} = 5.2\text{\AA}\). - -![[fig:convpressure]Example of results of the convergence algorithm presented in Sec. [sec:convpressure]. The bottom plot is a zoom near the minimum. The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step.]({{ site.baseurl}}/assets/2018_PRACE_MaX/convergence_pressure) - -9 P. Giannozzi et al., J.Phys. Cond. Matt. 29, 465901 (2017). S. R. Bahn and K. W. Jacobsen, Comput. Sci. Eng., 4, 56-66 (2002). S. Ping Ong et al., Comput. Mater. Sci. 68, 314-319 (2013). K.F. Garrity, J.W. Bennett, K.M. Rabe and D. Vanderbilt, Comput. Mater. Sci. 81, 446 (2014). G. Prandini, A. Marrazzo, I. E. Castelli, N. Mounet, N. Marzari, A Standard Solid State Pseudopotentials (SSSP) library optimized for accuracy and efficiency (Version 1.0, data download), Materials Cloud Archive (2018), [doi:10.24435/materialscloud:2018.0001/v1](http://doi.org/10.24435/materialscloud:2018.0001/v1). Crystallographic Open Database (COD), . - -[1] The string provided to the `DataFactory` encodes both the location and the name of the required class according to some specific rules. - -[2] if you set the structure incorrectly, for example with overlapping atoms, it is very likely that any DFT code will fail! - -[3] We purposefully do not provide advanced commands for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks already exist (such as ASE or pymatgen). - -[4] - -[5] - -[6] - -[7] However, to avoid duplication of KpointsData, you should first learn how to query the database, therefore we will ignore this duplication issue for now. - -[8] For JobCalculations (i.e., calculations that are submitted to a remote computer through a scheduler) there is an additional “Job state” (last column of the output of `verdi calculation list`) that can either be FINISHED if all went well, or one of the possible failure states (FAILED, PARSINGFAILED, SUBMISSIONFAILED, RETRIEVALFAILED). These states are represented as a Finished state (third column of `verdi calculation list`, with a zero/non-zero error code depending if they finished/did not finish correctly). This latter state is more general than just JobCalculations and also applies to workflows, as we will see later in the tutorial. - -[9] In simple (or even simplified) words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form `@decorating_function_name` on the line just before the `def` line of the decorated function. If you want to know more, there are many online resources explaining python decorators. - -[10] If you are curious: the two links have the same label, but are of different *link\_type*: one is a **create** link, that keeps track of the calculation that actually generated the node. Instead the other one is of type **return**, stating that the workfunction, beside creating that node, also returned it as an output. Calculation 5002 instead only returned the node but it did not generate it, therefore there is only one link between it and the final `StructureData`. diff --git a/docs/pages/2018_PRACE_MaX/sections/bands.rst b/docs/pages/2018_PRACE_MaX/sections/bands.rst deleted file mode 100644 index 29fe3c20..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/bands.rst +++ /dev/null @@ -1,46 +0,0 @@ -A real-world WorkChain: computing a band structure -================================================== - -**Note**: *If you still have enough time, you might want to check first -Appendix [sec:convpressure] before continuing with this section.* - -As a final demonstration of the power of WorkChains in AiiDA, we want to -give a demonstration of a WorkChain that we have written that will take -a structure as its only input and will compute its band structure. All -of the steps that would normally have to be done manually by the -researcher, choosing appropriate pseudopotentials, energy cutoffs, -k-points meshes, high-symmetry k-point paths and performing the various -calculation steps, are performed automatically by the WorkChain. - -The demonstration of the workchain will be performed in a Jupyter -notebook. To run it, follow the instructions that were given for the -querybuilder notebook in section [sec:querybuilder]. The only difference -is that instead of selecting the notebook in the ``querybuilder`` -directory, go to ``pw/bandstructure`` instead and choose the -``bandstructure.ipynb`` notebook. There you will find some example -structures that are loaded from COD, through the importer integrated -within AiiDA. Note that the required time to calculate the bandstructure -for these example structures ranges from 3 minutes to almost half an -hour, given that the virtual machine is running on a single core with -minimal computational power. It is not necessary to run these examples -as it may take too long to complete. For reference, the expected output -band structures are plotted in Fig.[fig:workchainbandstructures]. - -.. image:: /assets/2018_PRACE_MaX/bandstructures/Al_bands.png - :scale: 48 % - -.. image:: /assets/2018_PRACE_MaX/bandstructures/GaAs_bands.png - :scale: 48 % - -.. image:: /assets/2018_PRACE_MaX/bandstructures/CaF2_bands.png - :scale: 48 % - -.. image:: /assets/2018_PRACE_MaX/bandstructures/hBN_bands.png - :scale: 48 % - -Electronic band structures of four different crystal structures computed -with AiiDA’s PwBandsWorkChain - -*The following appendices consist of optional exercises, and are -mentioned in earlier parts of the tutorial. Go through them only if you -have time.* diff --git a/docs/pages/2018_PRACE_MaX/sections/calculations.md b/docs/pages/2018_PRACE_MaX/sections/calculations.md deleted file mode 100644 index dd8a5f98..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/calculations.md +++ /dev/null @@ -1,286 +0,0 @@ -# Submit, monitor and debug calculations - -The goal of this section is to understand how to create new data in AiiDA. We will launch a total energy calculation and check its results. We will introduce intentionally some common mistakes along the process of defining and submitting a calculation and we will explain you how to recognize and correct them. While this debugging is done here ‘manually’, workflows (that we will learn later in this tutorial) can automate this procedure considerably. For computing the DFT energy of the silicon crystal (with a PBE functional) we will use Quantum ESPRESSO , in particular the PWscf code (`pw.x`). Besides the AiiDA-core package, a number of plugins exist for many different codes. These are listed in the [AiiDA plugin registry](https://aiidateam.github.io/aiida-registry/)[4]. In particular, the “aiida-quantumespresso” plugin (already installed in your machine) provides a very extensive set of plugins, covering most (if not all) the functionalities of the underlying codes. - -## The AiiDA daemon - -First of all, check that the AiiDA daemon is actually running. The AiiDA daemon is a program running all the time in the background, checking if new calculations appear and need to be submitted to the scheduler. The daemon also takes care of all the necessary operations before the calculation submission, and after the calculation has completed on the cluster. Type in the terminal - -```console -verdi daemon status -``` - -If the daemon is running, the output should look like - -```console - Profile: default - Daemon is running as PID 1650 since 2018-05-16 16:26:04 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 1653 8.225 0 2018-05-16 16:26:04 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers -``` - -If this is not the case, type in the terminal - -```console -verdi daemon start -``` - -to start the daemon. - -## Creating a new calculation - -To launch a calculation, you will need to interact with AiiDA mainly in the `verdi shell`. We strongly suggest you to first try the commands in the shell, and then copy them in a script “test\_pw.py” using a text editor. This will be very useful for later execution of a similar series of commands. - -**Note**: The best way to run python scripts using AiiDA functionalities is to run them in a terminal by means of the command - -```console - verdi run -``` - -Every calculation sent to a cluster is linked to a code, which describes the executable file to be used. Therefore, first load the suitable code: - -``` python - code = Code.get_from_string() -``` - -Here `Code` is the general AiiDA class handling all possible codes, and `code` is a class instance tagged as `` (see the first part of the tutorial for listing all codes installed in your AiiDA machine). You might also want to list only the codes that define a default calculation plugin for the pw.x code of Quantum ESPRESSO. You can do this with the following command: - -```console -verdi code list -p quantumespresso.pw -``` - -Pick the correct codename, that might look like, e.g. `qe-pw-6.2.1@localhost`. - -Once run, AiiDA calculations are instances of the class `Calculation`, more precisely of one of its subclasses, each corresponding to a code specific plugin (for example, the PWscf plugin). You have already seen `Calculation` classes in the previous sections. - -However, to create a new calculation, rather than manually creating a new class, the suggested way is to use a `Builder`, that helps in setting the various calculation inputs and parameters, and provides TAB-completion. - -To obtain a new builder, we can use the `get_builder` method of the `code` object: - -``` python - builder = code.get_builder() -``` - -This returns a builder that helps in setting up the inputs for the `PwCalculation` class (associated to the `quantumespresso.pw` plugin, i.e. the default plugin for the code you chose before). - -As the first step, you can assign a (short) label or a (long) description to the calculation that you are going to create, that you might find convenient in the future. This can be achieved with: - -``` python - builder.label = "PW test" - builder.description = "My first AiiDA calc with Quantum ESPRESSO on BaTiO3" -``` - -This information will be saved in the database for later query or inspection. Note that you can press TAB after writing `builder.` to see all available inputs. - -Now you have to specify the number of machines (a.k.a. cluster nodes) you are going to run on and the maximum time allowed for the calculation. These general calculation options, that are independent of the code or plugin, but rather mainly passed later to the scheduler that handles the queue, are all grouped under “builder.options”: - -``` python - builder.options.resources = {'num_machines': 1} - builder.options.max_wallclock_seconds = 30*60 -``` - -Just like the normal inputs, these builder options are also TAB-completed. Type “builder.options.” and hit the TAB button to see the list of available options. - -### Preparation of inputs - -Quantum ESPRESSO requires an input file containing Fortran namelists and variables, plus some cards sections (the documentation is available [online](http://www.quantum-espresso.org/wp-content/uploads/Doc/INPUT_PW.html)[5]). The Quantum ESPRESSO plugin of AiiDA requires quite a few nodes in input, which are documented [online](http://aiida-core.readthedocs.io/en/latest/plugins/quantumespresso/pw.html)[6]. Here we will instruct our calculation with a minimal configuration for computing the energy of silicon. We need: - -1. Pseudopotentials -2. a structure -3. the k-points -4. the input parameters - -We leave the parameters as the last thing to setup and start with structure, k-points, and pseudopotentials. - -Use what you learned in the previous section and define these two kinds of objects in this script. Define in particular a silicon structure and a 2\(\times\)2\(\times\)2 mesh of k-points. Notice that if you just copy and paste the code that you executed previously, you will create duplicated information in the database (i.e. every time you will execute the script, you will create another StructureData, another KpointsData, …). In fact, you already have the opportunity to re-use an already existing structure.[7] Use therefore a combination of the bash command `verdi data structure list` and of the shell command `load_node()` to get an object representing the structure created earlier. - -### Attaching the input information to the calculation - -So far we have defined (or loaded) some of the input data, but we haven’t instructed the calculation to use them. To do this, let’s just set the appropriate attributes of the builder (we assume here that you created the structure and k-points AiiDA nodes before and called them `structure` and `kpoints`, respectively): - -``` python - builder.structure = structure - builder.kpoints = kpoints -``` - -Note that you can set in the builder both stored and unstored nodes. AiiDA will take care of storing the unstored nodes upon submission. Otherwise, if you decide not to submit, nothing will be stored in the database. - -Moreover, PWscf also needs information on the pseudopotentials, specified by UpfData objects. This is set by storing a dictionary in “builder.pseudo”, with keys being the kind names, and value being the UpfData pseudopotential nodes. To simplify the task of choosing pseudopotentials, we can however use a helper function that automatically returns this dictionary picking the pseudopotentials from a given UPF family. - -You can list the preconfigured families from the command line: - -```console - verdi data upf listfamilies -``` - -Pick the one you configured earlier or one of the `SSSP` families that we provide, and link it to the calculation using the command: - -``` python - from aiida.orm.data.upf import get_pseudos_from_structure - builder.pseudo = get_pseudos_from_structure(structure, '') -``` - -### Preparing and debugging input parameters - -The last thing we miss is a set of parameters (i.e. cutoffs, convergence thresholds, etc…) to launch the Quantum ESPRESSO calculation. This part requires acquaintance with Quantum ESPRESSO and, very often, this is the part to tune when a calculation shows a problem. Let’s therefore use this part of the tutorial to learn how to debug problems, and **let’s introduce errors intentionally**. Note also that some of the problems we will investigate appear the first times you launch calculations and can be systematically avoided by using workflows. - -Let’s define a set of input parameters for Quantum ESPRESSO, preparing a dictionary of the form: - -``` python -parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, -} -``` - -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, except for an invalid key called “mickeymouse”. When Quantum ESPRESSO receives an unrecognized key (even when you misspell one) its behavior is to stop almost immediately. By default, the AiiDA plugin will not validate your input and simply pass it over. Therefore let’s pass this dictionary to the calculation and observe this unsuccessful behavior. - -As done before, load the ParameterData class - -``` python - ParameterData = DataFactory("parameter") -``` - -and create an instance of the class containing all the input parameters you just defined - -``` python - parameters = ParameterData(dict=parameters_dict) -``` - -Finally, set the parameters in the builder - -``` python - builder.parameters = parameters -``` - -### Simulate submission - -At this stage, you have recreated in memory (it’s not yet stored in the database) the input of the graph shown in Fig. [fig:graph]a, whereas the outputs will be created later by the daemon. - -In order to check how AiiDA creates the actual input files for the calculation, we can simulate the submission process with the (otherwise optional) command - -``` python - builder.submit_test() -``` - -This creates a folder of the form `submit_test/[date]-0000[x]` in the current directory. Check (in your second terminal) the input file `aiida.in` within this folder, comparing it with the content of the input data nodes you created earlier, and that the ‘pseudo’ folder contains the needed pseudopotentials. You can also check the submission script `_aiidasubmit.sh` (the scheduler that is installed on the machine is Torque, so AiiDA creates the files with the proper format for this scheduler). Note: you cannot correct the input file from the “submit\_test” folder: you have to correct the script and re-execute it; the files created by `submit_test()` are only for final inspection. - -### Storing and submitting the calculation - -Up to now the calculation `calc` is kept in memory and not in the database. We will now submit it, that will implicitly create a `PwCalculation` class, store it in the database, store also all its inputs parameters, k-points, structure, and properly link them. To submit it, run - -``` python - from aiida.work.launch import submit - calc = submit(builder) -``` - -`calc` will now be the stored `PwCalculation`, already submitted to the daemon. The calculation has now a \`\`database primary key" or `pk` (an integer ID) to the calculation (typing `calc.pk` will print this number). Moreover, it also gets a universally-unique ID (`UUID`), visible with `calc.uuid` that does not change even upon sharing the data with collaborators (while the `pk` will change in that case). - -Now that the calculation is stored, you can also attach any additional attributes of your choice, which are called “extra” and defined in as key-value pairs. For example, you can add an extra attribute called `element`, with value `Si` through - -``` python - calc.set_extra("element","Si") -``` - -You will see later the advantage of doing so for querying. - -In the meantine, as soon as you submitted your calculation, the daemon picked it up and started to perform all the operations to do the actual submission, going through input file generation, submission to the queue, waiting for it to run and finish, retrieving the output files, parsing them, storing them in the database and setting the state of the calculation to `Finished`. - -**N.B.** If the daemon is not running the calculation will remain in the `NEW` state until when you start it. - -### Checking the status of the calculation - -You can check the calculation status from the command line: - -```console - verdi calculation list -``` - -Note that `verdi` commands can be slow in this tutorial when the calculation is running (because you just have one CPU which is also used by the PWscf calculation). - -By now, it is possible that the calculation you submitted has already finished, and therefore that you don’t see any calculation in the output. In fact, by default, the command only prints calculations that are still being handled by the daemon, i.e. those with a state that is not `FINISHED` yet[8]. - -To see also (your) calculations that have finished (and limit those only to the one created in the past day), use instead - -```console - verdi calculation list -a -p1 -``` - -as explained in the first section. - -To inspect the list of input files generated by the AiiDA (this can be done even when the calculation did not finish yet), type - -```console - verdi calculation inputls -c -``` - -with `pk_number` the pk number of your calculation. This will show the contents of the input directory (`-c` prints directories in colour). Then you can also check the content of the actual input file with - -```console - verdi calculation inputcat | less -``` - -## Troubleshooting - -After all this work the calculation should end up in a FAILED Job state (last column of `verdi calculation list`), and correspondingly the error code near the \`\`Finished" status of the State should be non-zero (400 for FAILED calculations). This was expected, since we used an invalid key in the input parameters. Situations like this happen (probably often...) in real life, so we built in AiiDA the tools to traceback the problem source and correct it. - -A first way to proceed is the manual inspection of the output file of PWscf. You can visualize it with: - -```console - verdi calculation outputcat | less -``` - -This can be a good primer for problem inspection. For something more compact, you can also try to inspect the calculation log (from AiiDA): - -```console - verdi calculation logshow -``` - -If the calculation has encountered a mistake, this log shows a handful of warnings coming from various processes, such as the daemon, the parser of the output or the scheduler on the cluster. In production runs, errors will mostly come from an unexpected termination of the PWscf calculation. The most programmatic way to handle these errors is to inspect the warnings key by loading the calculation object, say `calc`, and the using the following method: - -``` python -calc.res.warnings -``` - -This will print a list of strings reporting errors experienced during the execution, that can be easily read in python (and thus addressed programmatically), but are also reported in the calculation log. With any of these three methods you can understand that the problem is something like an ‘invalid input key’, which is exactly what we did. - -Let’s use a parameters dictionary that actually works. Modify the script `test_pw.py` script modifying the parameter dictionary as - -``` python -parameters_dict = { - "CONTROL": {"calculation": "scf", - }, - "SYSTEM": {"ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": {"conv_thr": 1.e-6, - } - } -``` - -If you launch the modified script by typing - -```console - verdi run test_pw.py -``` - -you should now be able to see a calculation reaching successfully the FINISHED state. Now you can access the results as you have seen earlier. For example, note down the pk of the calculation so that you can load it in the `verdi shell` and check the total energy with the commands: - -``` python -calc=load_node() -calc.res.energy -``` diff --git a/docs/pages/2018_PRACE_MaX/sections/preliminaries.md b/docs/pages/2018_PRACE_MaX/sections/preliminaries.md deleted file mode 100644 index 1ee369ae..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/preliminaries.md +++ /dev/null @@ -1,85 +0,0 @@ -# Preliminaries - -## Instructions to SSH to the Amazon EC2 instance - -You should have received an IP address from the instructors, and two files with a private and a public SSH key (`aiida_tutorial_NUM` and `aiida_tutorial_NUM.pub`), where `NUM` is an integer. These allow you to connect to an Amazon EC2 instance (different for each participant of the tutorial). To connect via `ssh` to this machine follow the steps below, depending on the computer you have. - -**Note!** *If you decide to work in pairs, one of the two people should discard his email. The other person should forward his email to the colleague, and both should then use the same virtual machine IP and account (ssh key). In this case, you will be both using the same account, so be careful not to delete the work of your colleague.* - -### Linux and Mac - -- If needed, create a `.ssh` directory in your home (`mkdir ~/.ssh`), and set its permissions: `chmod 700 ~/.ssh` -- Copy in this `.ssh` directory the two files `aiida_tutorial_NUM` and `aiida_tutorial_NUM.pub` -- Set the correct permissions on the private key: `chmod 600 ~/.ssh/aiida_tutorial_NUM` (then check with `ls -l` that the permissions of this file are now `-rw-------`). -- Create (or modify if it already exists) the `config` file in your `.ssh` directory, adding the following lines: - - ```console - Host aiidatutorial - Hostname IP_ADDRESS - User aiida - IdentityFile ~/.ssh/aiida_tutorial_NUM - LocalForward 8888 localhost:8888 - ``` - - where you have to replace `IP_ADDRESS` with the IP address provided to you. - -- You can then `ssh` to the Amazon EC2 instance from the terminal, using simply - - ```console - ssh -X -C aiidatutorial - - ``` - - (connecting with `-X` — note that sometimes `-Y` is needed instead — will allow you to run graphical programs such as xmgrace or gnuplot interactively, even if they might not be very responsive as the Amazon virtual machines are in Ireland). - -### Windows - -- Install PuTTY. - -- Run PuTTYGen, load the `aiida_tutorial_NN` private key (button `"Load"`). remember to choose to show “All files (\*.\*)” in the window, and select the file without any extension (Type: File). - -- In the same window, click on “Save private Key”, and save the key with the name `aiida_tutorial_NN.ppk`. - -- Run Pageant: it will add a new icon near the clock, in the bottom right of your screen. - -- Right click on this Pageant icon, and click on “View Keys”. - -- Click on `"Add key"` and select the `aiida_tutorial_NN.ppk` you saved a few steps above. - -- Run PuTTY, put the given IP address as hostname. Write `aiidatutorial` in Saved Sessions and click `Save`. Go to Connection \(\to\) Data and put `aiida` as autologin username. Under Connection, go to SSH \(\to\) Tunnels, type `8888` in the `Source Port` box and `localhost:8888` in `Destination` and click `Add`. Click on `Save` again on the Session screen. - -- Now select `aiidatutorial` from the session list, click `Load` and, finally, `open`. - -## Everybody: connect to the machine and start jupyter - -Before starting the tutorial, connect via SSH to the Amazon machine as explained above (the Amazon machine already contains a pre-configured AiiDA installation and some test data for this tutorial). - -## Before starting - -Once connected to your machine, type in the remote terminal - -```console - workon aiida -``` - -This will enable the virtual environment in which AiiDA is installed, allowing you to use AiiDA. Now type in the same bash - -```console - jupyter notebook --no-browser -``` - -This will run a server with a web application called `jupyter`, which is used to create interactive python notebooks. To connect to this application, copy the URL that has been printed to the terminal (it will be something like `http://localhost:8888/?token=2a3ba37cd1...`) and paste it into the URL bar of a web browser. You will see a list of folders: these are folders on the remote Amazon computer. We will use `jupyter` in section [sec:querybuilder] and optionally in other sections as well. - -Now launch an identical `ssh` connection (again, as explained above) in another terminal, and type `workon aiida` here too. This terminal is the one you will actually use in this tutorial. - -Note: Since the port listening is set to a specific port (8888) in the section [sec:sshintro], you have to make sure on the server the Jupiter notebook is running on the port 8888. Otherwise, use an alternative port for listening. - -A final note: for details on AiiDA that may not be fully explained here, you can refer to the full AiiDA documentation, available online at . - -## Troubleshooting tips (in case you have issues later) - -- If you get an error like `ImportError: No module named aiida` or `No command ’verdi’ found` double check that you have loaded the virtual environment with `workon aiida` before launching python, ipython or the jupyter server. - -- If your browser cannot connect to the jupyter instance, check that you have correctly configured SSH tunneling/forwarding as described above. Also note that you should run the jupyter server from the terminal connected to the Amazon machine, while the web browser should be opened locally on your laptop or worstation. - -- The Jupyter Notebook officially supports the latest stable versions of Chrome, Safari and Firefox. See for more information on broswer compatibility (and update your browser if it is too old). diff --git a/docs/pages/2018_PRACE_MaX/sections/querybuilder.rst b/docs/pages/2018_PRACE_MaX/sections/querybuilder.rst deleted file mode 100644 index 1cddb7bd..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/querybuilder.rst +++ /dev/null @@ -1,46 +0,0 @@ -Queries in AiiDA: The Querybuilder -================================== - -This part of the tutorial is provided only in interactive mode through a -Jupyter notebook, which you will be able to run in your browser. To -accomplish this we first need to start the Jupyter server, if you didn’t -do it already at the very beginning of the tutorial. First make sure you -are connected to the virtual machine with local forwarding enabled, as -described in section [sec:sshintro]. Then, on the virtual machine, first -make sure your are in the ``aiida`` virtual environment: - -.. code:: console - - workon aiida - -If the virtual environment is successfully loaded, your prompt should be -prefixed with ``(aiida)``. To finally launch the Jupyter server, execute -the following commands: - -.. code:: console - - cd ~/examples/aiida-demos/tutorial/ - jupyter notebook --no-browser - -If all went well, you should now be able to open up a browser on your -local machine and point it to the following address -``http://localhost:8888/?token=2a3ba3...`` (replace the token with the -one printed on output by the previous command). This should now show you -a directory navigator. - -To open the notebook, click on ``querybuilder`` and then select the file -``tutorial.ipynb``. Note that there is also a ``solution.ipynb``, which is a -copy of the same notebook, but which contains the solutions to all the -exercises. You can use this version at your own discretion if you get stuck at -some point (but we suggest that you try not to look at it at first). - -See below for a rendered version of the notebook, that can also be downloaded -from :download:`here <../notebooks/querybuilder-tutorial.ipynb>`. - -.. toctree:: - - QueryBuilder Notebook <../notebooks/querybuilder-tutorial.ipynb> - -The solutions can also be downloaded from -:download:`here <../notebooks/querybuilder-solutions.ipynb>`: but again, -try not to use them at first! diff --git a/docs/pages/2018_PRACE_MaX/sections/verdi_cmdline.rst b/docs/pages/2018_PRACE_MaX/sections/verdi_cmdline.rst deleted file mode 100644 index 16710b75..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/verdi_cmdline.rst +++ /dev/null @@ -1,444 +0,0 @@ -Verdi command line -================== - -This part of the tutorial will help to familiarize you with the -command-line utility ``verdi``, one of the most common ways to interact -with AiiDA. ``verdi`` with its subcommands enables a variety of -operations such as inspecting the status of ongoing or terminated -calculations, showing the details of calculations, computers, codes, or -data structures, access the input and the output of a calculation, etc. -Similar to the ``bash`` shell, verdi command support Tab completion. Try -right now to type ``verdi``, followed by a space, in a terminal and tap -Tab twice to have a list of subcommands. Whenever you need the -explanation of a command type ``verdi help`` or add ``-h`` flag if you -are using any of the ``verdi`` subcommands. Finally, fields enclosed in -angular brackets, such as ````, are placeholders to be replaced by -the actual value of that field (an integer, a string, etc...). - -The list of calculations ------------------------- - -Let us try our first ``verdi`` commands. Type in the terminal - -.. code:: console - - verdi calculation list - -(Note: the first time you run this command, it might take a few seconds -as it is the first time you are accessing the database in the virtual -machine. Subsequent calls will be faster). This will print the list of -ongoing calculations, which should be empty. The first output line -should look like - -.. code:: console - - PK Creation State Type Computer Job state - ---- ---------- ------- ------ ---------- ----------- - - Total results: 0 - - Info: last time an entry changed state: never - -In order to print a list with all calculations that finished correctly -in the AiiDA database, you can use the ``-s/--states`` flag as follows: - -.. code:: console - - verdi calculation list --states FINISHED - -Another very typical option combination allows to get calculations in -*any* state (flag ``-a``) generated in the past ``NUM`` days -(``-p ``): e.g., for calculation in the past 1 day: -``verdi calculation list -p1 -a``. Since you have not yet run any -calculations at the virtual machine that you currently use and all the -existing calculations were imported and belong to a different user, you -can type (flag ``-A`` shows the calculations of all the users): - -.. code:: console - - verdi calculation list -A --states IMPORTED - -Each row of the output identifies a calculation and shows some -information about it. For a more detailed list of properties, choose one -row by noting down its PK (primary key) number (first column of the -output) and type in the terminal - -.. code:: console - - verdi calculation show - -The output depends on the specific pk chosen and should inform you about -the input nodes (e.g. pseudopotentials, kpoints, initial structure, -etc.), and output nodes (e.g. output structure, output parameters, -etc.). - -**PKs/IDs vs. UUIDs**: Beside the (integer) PK, very convenient to -reference a calculation or data node in your database, every node has a -UUID (Universally Unique ID) to identify it, that is preserved even when -you share some nodes with coworkers—while the PK will most likely -change. You can see the UUID in the output of ``verdi calculation show`` -or ``verdi node show``. Moreover, if you have already a UUID and you -want to get the corresponding PK in your database, you can use -``verdi node show -u ``, as we are going to do now. - -Let us now consider the node with -``UUID = ce81c420-7751-48f6-af8e-eb7c6a30cec3``, which identifies a -relaxation of a BaTiO(\_3) unit cell run with Quantum Espresso ``pw.x``. -You can check the information on this node and get the PK with: - -.. code:: console - - $ verdi node show -u ce81c420-7751-48f6-af8e-eb7c6a30cec3 - Property Value - ------------- ------------------------------------ - type PwCalculation - pk 4235 - uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 - label - description - ctime 2014-10-27 17:51:21.781045+00:00 - mtime 2018-05-16 11:19:39.848446+00:00 - process state - finish status - computer [2] daint - code pw-SVN-piz-daint - - Inputs PK Type - ---------- ---- ------------- - parameters 4236 ParameterData - kpoints 4526 KpointsData - pseudo_Ba 966 UpfData - pseudo_Ti 4315 UpfData - settings 4529 ParameterData - pseudo_O 4342 UpfData - structure 436 StructureData - - Outputs PK Type - ----------------------- ---- ------------- - output_kpoints 3665 KpointsData - output_parameters 3670 ParameterData - output_structure 3666 StructureData - retrieved 3668 FolderData - output_trajectory_array 265 ArrayData - remote_folder 1977 RemoteData - -*Keep in mind that you can also use just a part (beginning) of the UUID, -as long as it is unique, to show the node information information.* For -example, to display the above information, you could also type -``verdi node show -u ce81c420``. In what follows, we are going to -mention only the prefixes of the UUIDs since they are sufficient to -identify the correct node. - -A typical AiiDA graph ---------------------- - -AiiDA stores inputs required by a calculation as well as the its outputs -in the database. These objects are connected in a graph that looks like -Fig. [fig:graph]. We suggest that you have a look to the figure before -going ahead. - -.. figure:: /assets/2018_PRACE_MaX/graph/graph-inputonly.png - :width: 100% - - Graph with all inputs (data, circles; and code, diamond) to - the Quantum Espresso calculation (square) that you will create in - Sec. [sec:qe] of this tutorial. - -.. figure:: /assets/2018_PRACE_MaX/graph/graph-full.png - :width: 100% - - Same as above, but also with the outputs that the daemon will create and - connect automatically. - The RemoteData node is created during submission and can be thought as a - symbolic link to the remote folder in which the calculation runs on the - cluster. The other nodes are created when the calculation has finished, - after retrieval and parsing. The node with linkname “retrieved” contains - the raw output files stored in the AiiDA repository; all other nodes are - added by the parser. Additional nodes (symbolized in gray) can be added - by the parser (e.g., an output StructureData if you performed a - relaxation calculation, a TrajectoryData for molecular dynamics, …). - -You can create a similar graph for any calculation node by using the -utility ``verdi graph generate ``. For example, before you obtained -information (in text form) for ``UUID = ce81c420``. To visualize similar -information in graph(ical) form, run (replacing ```` with the PK of -the node): - -.. code:: console - - verdi graph generate - -This command will create the file ``.dot`` that can be rendered by -means of the utility ``dot``. If you now type - -.. code:: console - - dot -Tpdf -o .pdf .dot - -you will create a pdf file ``.pdf``. You can open this file on the -Amazon machine by using ``evince`` or, if you feel that the ssh -connection is too slow, copy it via ``scp`` to your local machine. To do -so, if you are using Linux/Mac OS X, you can type in your *local* -machine: - -.. code:: console - - scp aiidatutorial: - -and then open the file. Alternatively, you can use graphical software to -achieve the same, for instance: WinSCP on Windows, Cyberduck on the Mac, -or the “Connect to server” option in the main menu after clicking on the -desktop for Ubuntu. - -Spend some time to familiarize yourself with the graph structure. Choose -the root node (highlighted in blue) and trace back the parent -calculation which produced the structure used as an input. This is an -example of a Quantum ESPRESSO pw.x calculation, where the input -structure was actually obtained as the output of a previous calculation. -We will now inspect the different elements of this graph. - -Inspecting the nodes of a graph -------------------------------- - -ParameterData and Calculations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Now, let us have a closer look at the some of the nodes appearing in the -graph. Choose the node of the type ``ParameterData`` with input link -name ``parameters`` (to double check, it should have UUID ``d1bbe1ea``) -and type in the terminal: - -.. code:: console - - verdi data parameter show - -A ``ParameterData`` contains a dictionary (i.e., key–value pairs), -stored in the database in a format ready to be queried (we will learn -how to run queries later on in this tutorial). The command above will -print the content dictionary, containing the parameters used to define -the input file for the calculation. You can compare the dictionary with -the content of the raw input file to Quantum ESPRESSO (that was -generated by AiiDA) via the command - -.. code:: console - - verdi calculation inputcat - -where you substitute the pk of the calculation node. Check the -consistency of the parameters written in the input file and those stored -in the ParameterData node. Even if you don’t know the meaning of the -input flags of a Quantum ESPRESSO calculation, you should be able to see -how the input dictionary has been converted to Fortran namelists. - -The previous command just printed the content of the “default” input -file ``aiida.in``. To see a list of all the files used to run a -calculation (input file, submission script, etc.) instead type - -.. code:: console - - verdi calculation inputls - -(Adding a ``--color`` flag allows you to easily distinguish files from -folders by a different coloring). - -Once you know the name of the file you want to visualize, you can call -the ``verdi calculation inputcat`` command specifying the path. For -instance, to see the submission script, you can do: - -.. code:: console - - verdi calculation inputcat -p _aiidasubmit.sh - -StructureData -~~~~~~~~~~~~~ - -Now let us focus on StructureData objects, representing a crystal -structure. We can consider for instance the input structure to the -calculation we were considering before (it should have UUID prefix -``3a4b1270``). Such objects can be inspected interactively by means of -an atomic viewer such as the one provided by ``ase``. AiiDA however -supports several other viewers such as ``xcrysden``, ``jmol``, and -``vmd``. Type in the terminal - -.. code:: console - - verdi data structure show --format ase - -to show the selected structure (it will take a few seconds to appear, -and you can rotate the view with the right mouse button—if you receive -some errors, make sure you started your SSH connection with the ``-X`` -or ``-Y`` flag). - -Alternatively, especially if showing them interactively is too slow over -SSH, you can export the content of a structure node in various popular -formats such as ``xyz`` or ``xsf``. This is achieved by typing in the -terminal - -.. code:: console - - verdi data structure export --format xsf > .xsf - -You can open the generated ``xsf`` file and observe the cell and the -coordinates. Then, you can then copy ``.xsf`` from the Amazon -machine to your local one and then visualize it, e.g. with xcrysden (if -you have it installed): - -.. code:: console - - xcrysden --xsf .xsf - -Codes and computers -~~~~~~~~~~~~~~~~~~~ - -Let us focus now on the nodes of type ``code``. A code represents (in -the database) the actual executable used to run the calculation. Find -the pk of such a node in the graph and type - -.. code:: console - - verdi code show - -The command prints information on the plugin used to interface the code -to AiiDA, the remote machine on which the code is executed, the path of -its executable, etc. To show a list of all available codes type - -.. code:: console - - verdi code list - -If you want to show all codes, including hidden ones and those created -by other users, use ``verdi code list -a -A``. Now, among the entries of -the output you should also find the code just shown. - -Similarly, the list of computers on which AiiDA can submit calculations -is accessible by means of the command - -.. code:: console - - verdi computer list -a - -(``-a`` shows all computers, also the one imported in your database but -that you did not configure, i.e., to which you don’t have access). -Details about each computer can be obtained by the command - -.. code:: console - - verdi computer show - -Now you have the tools to answer the question: - -What is the scheduler installed on the computer where the calculations -of the graph have run? - -Calculation results -~~~~~~~~~~~~~~~~~~~ - -The results of a calculation can be accessed directly from the -calculation node. Type in the terminal - -.. code:: console - - verdi calculation res - -which will print the output dictionary of the “scalar” results parsed by -AiiDA at the end of the calculation. Note that this is actually a -shortcut for - -.. code:: console - - verdi data parameter show - -where ``pk2`` refers to the ParameterData node attached as an output of -the calculation node, with link name ``output_parameters``. - -By looking at the output of the command, what is the Fermi energy of the -calculation with UUID prefix ``ce81c420``? - -Similarly to what you did for the calculation inputs, you can access the -output files via the commands - -.. code:: console - - verdi calculation outputls - -and - -.. code:: console - - verdi calculation outputcat - -Use the latter to verify that the Fermi energy that you have found in -the last step has been extracted correctly from the output file (Hint: -filter the lines containing the string “Fermi”, e.g. using ``grep``, to -isolate the relevant lines). - -The results of calculations are stored in two ways: ``ParameterData`` -objects are stored in the database, which makes querying them very -convenient, whereas ``ArrayData`` objects are stored on the disk. Once -more, use the command ``verdi data array show `` to know the Fermi -energy obtained from calculation with UUID prefix ``ce81c420`` (you need -to use, this time, the PK of the output ArrayData of the calculation, -with link name ``output_trajectory_array``). As you might have realized -the difference now is that the whole series of values of the Fermi -energy calculated after each relax/vc-relax step are stored. (The choice -of what to store in ``ParameterData`` and ``ArrayData`` nodes is made by -the parser of ``pw.x`` implemented in the ``aiida-quantumespresso`` -plugin.) - -(Optional section) Comments -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -AiiDA offers the possibility to attach comments to a calculation node, -in order to be able to remember more easily its details. Node with UUID -prefix ce81c420 has no comment already defined, but you can add a very -instructive one by typing in the terminal - -.. code:: console - - verdi comment add -c "vc-relax of a BaTiO3 done with QE pw.x" - -Now, if you ask for a list of all comments associated to that -calculation by typing - -.. code:: console - - verdi comment show - -the comment that you just added will appear together with some useful -information such as its creator and creation date. We let you play with -the other options of ``verdi comment`` command to learn how to update or -remove comments. - -AiiDA groups of calculations ----------------------------- - -In AiiDA, calculations (and more generally nodes) can be organized in -groups, which are particularly useful to assign a set of calculations or -data to a common project. This allows you to have quick access to a -whole set of calculations with no need for tedious browsing of the -database or writing complex scripts for retrieving the desired nodes. -Type in the terminal - -.. code:: console - - verdi group list - -to show a list of the groups that already exist in the database. Choose -the PK of the group named ``tutorial_pbesol`` and look at the -calculations that it contains by typing - -.. code:: console - - verdi group show - -In this case, we have used the name of the group to organize -calculations according to the pseudopotential that has been used to -perform them. Among the rows printed by the last command you will be -able to find the calculation we have been inspecting until now. - -If, instead, you want to know all the groups to which a specific node -belomngs, you can run - -.. code:: console - - verdi group list --node diff --git a/docs/pages/2018_PRACE_MaX/sections/verdi_shell.md b/docs/pages/2018_PRACE_MaX/sections/verdi_shell.md deleted file mode 100644 index 0569ad01..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/verdi_shell.md +++ /dev/null @@ -1,305 +0,0 @@ -# Verdi shell and AiiDA objects - -In this section we will use an interactive ipython environment with all the basic AiiDA classes already loaded. We propose two realizations of such a tool. The first consist of a special ipython shell where all the AiiDA classes, methods and functions are accessible. Type in the terminal - -```console - verdi shell -``` - -For all the everyday AiiDA-based operations, i.e. creating, querying and using AiiDA objects, the `verdi shell` is probably the best tool. In this case, we suggest that you use two terminals, one for the `verdi shell` and one to execute bash commands. - -The second option is based on `jupyter` notebooks and is probably most suitable to the purposes of our tutorial. Go to the browser where you have opened `jupyter` and click `New` \(\to\) `Python 2` (top right corner). This will open an ipython notebook based on cells where you can type portions of python code. The code will not be executed until you press `Shift+Enter` from within a cell. Type in the first cell - -``` python - %aiida -``` - -and execute it. This will set exactly the same environment as the `verdi shell`. The notebook will be automatically saved upon any modification and when you think you are done, you can export your notebook in many formats by going to `File` \(\to\) `Download as`. We suggest you to have a look to the drop-down menus `Insert` and `Cell` where you will find the main commands to manage the cells of your notebook. **The `verdi shell` and the `jupyter` notebook are completely equivalent. Use either according to your personal preference.** - -Note: you will still need sometimes to type command-line instructions in `bash` in the first terminal you opened today. To differentiate these from the commands to be typed in the `verdi shell`, the latter will be marked in this document by a vertical line on the left, like: - -``` python - some verdi shell command -``` - -while command-line instructions in `bash` to be typed on a terminal will be encapsulated between horizontal lines: - -```console - some bash command -``` - -Alternatively, to avoid changing terminal, you can execute `bash` commands within the `verdi shell` or the notebook adding an exclamation mark before the command itself - -``` python - !some bash command -``` - -## Loading a node - -Most AiiDA objects are represented by nodes, identified in the database by its pk number (an integer). You can access a node using the following command in the shell: - -``` python - node = load_node(PK) -``` - -Load a node using one of the calculation pks visible in the graph you displayed in the previous section of the tutorial. Then get the energy of the calculation with the command - -``` python - node.res.energy -``` - -You can also type - -``` python - node.res. -``` - -and then press `TAB` to see all the possible output results of the calculation. - -## Loading different kinds of nodes - -### Pseudopotentials - -From the graph displayed in Section [sec:aiida_graph], find the pk of the barium pseudopotential file (LDA). Load it and verify that it describes barium. Type - -``` python - upf = load_node(PK) - upf.element -``` - -All methods of `UpfData` are accessible by typing `upf.` and then pressing `TAB`. - -### k-points - -A set of k-points in the Brillouin zone is represented by an instance of the `KpointsData` class. Choose one from the graph of Section [sec:aiida_graph], load it as `kpoints` and inspect its content: - -``` python - kpoints.get_kpoints_mesh() -``` - -Then get the full (explicit) list of k-points belonging to this mesh using - -``` python - kpoints.get_kpoints_mesh(print_list=True) -``` - -If you incurred in a `AttributeError`, it means that the kpoints instance does not represent a regular mesh but rather a list of k-points defined by their crystal coordinates (typically used when plotting a band structure). In this case, get the list of k-points coordinates using - -``` python - kpoints.get_kpoints() -``` - -If you prefer Cartesian (rather than crystal) coordinates, type - -``` python - kpoints.get_kpoints(cartesian=True) -``` - -For later use in this tutorial, let us try now to create a kpoints instance, to describe a regular \(2\times2\times2\) mesh of k-points, centered at the Gamma point (i.e. without offset). This can be done with the following commands: - -``` python - from aiida.orm.data.array.kpoints import KpointsData - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh,kpoints_mesh,kpoints_mesh]) - kpoints.store() -``` - -The import performed in the first line is however unpractical as it requires to remember the exact location of the module containing the KpointsData class. Instead, it is easier to use the `DataFactory` function instead of an explicit import. - -``` python - KpointsData = DataFactory("array.kpoints") -``` - -This function loads the appropriate class defined in a string (here `array.kpoints`).[1] Therefore, `KpointsData` is not a class instance, but the kpoints class itself! - -### Parameters - -Nested dictionaries with individual parameters, as well as lists and arrays, are represented in AiiDA with `ParameterData` objects. Get the PK and load the input parameters of a calculation in the graph of Section [sec:aiida_graph]. Then display its content by typing - -``` python - params.get_dict() -``` - -where `params` is the `ParameterData` node you loaded. Modify the dictionary content so that the wave-function cutoff is now set to 20 Ry. Note that you cannot modify an object already stored in the database. To save the modification, you must create a new ParameterData object. Similarly to what discussed before, first load the `ParameterData` class by typing - -``` python - ParameterData = DataFactory('parameter') -``` - -Then an instance of the class (i.e. the parameter object that we want to create) is created and initialized by the command - -``` python - new_params = ParameterData(dict=YOUR_DICT) -``` - -where `YOUR_DICT` is the modified dictionary. Note that the parameter object is not yet stored in the database. In fact, if you simply type `new_params` in the verdi shell, you will be prompted with a string notifying you the “unstored” status. To save an entry in the database corresponding to the `new_params` object, you need to type a last command in the verdi shell: - -``` python - new_params.store() -``` - -### Structures - -Find a structure in the graph of Section [sec:aiida_graph] and load it. Display its chemical formula, atomic positions and species using - -``` python - structure.get_formula() - structure.sites -``` - -where `structure` is the structure you loaded. If you are familiar with ASE and PYMATGEN, you can convert this structure to those formats by typing - -``` python - structure.get_ase() - structure.get_pymatgen() -``` - -Let’s try now to define a new structure to study, specifically a silicon crystal. In the `verdi shell`, define a cubic unit cell as a \(3\times3\) matrix, with lattice parameter \(a_{lat}=5.4\) Å: - -``` python - alat = 5.4 - the_cell = [[alat/2,alat/2,0.],[alat/2,0.,alat/2],[0.,alat/2,alat/2]] -``` - -**Note**: Default units for crystal structure cell and coordinates in AiiDA are Å. - -Structures in AiiDA are instances of `StructureData` class: load it in the verdi shell - -``` python - StructureData = DataFactory("structure") -``` - -Now, initialize the class instance (i.e. is the structure we want to study) by the command - -``` python - structure = StructureData(cell=the_cell) -``` - -which sets the cubic cell defined before. From now on, you can access the cell with the command - -``` python - structure.cell -``` - -Finally, append each of the 2 atoms of the cell command. You can do it using commands like - -``` python - structure.append_atom(position=(alat/4.,alat/4.,alat/4.),symbols="Si") -``` - -for the first ‘Si’ atom. Repeat it for the other atomic site \(\left(0,0,0\right)\). You can access and inspect[2] the structure sites with the command - -``` python - structure.sites -``` - -If you make a mistake, start over from `structure = StructureData(cell=the_cell)`, or equivalently use -`structure.clear_kinds()` to remove all kinds (atomic species) and sites. Alternatively, AiiDA structures can also be converted directly from ASE  structures using[3] - -``` python - from ase.lattice.spacegroup import crystal - ase_structure = crystal('Si', [(0,0,0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90],primitive_cell=True) - structure=StructureData(ase=ase_structure) -``` - -Now you can store the new structure object in the database with the command: - -``` python - structure.store() -``` - -Finally, we can also import the silicon structure from an external (online) repository such as the Crystallography Open Database : - -``` python -from aiida.tools.dbimporters.plugins.cod import CodDbImporter -importer = CodDbImporter() -for entry in importer.query(formula='Si',spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print "Formula", structure.get_formula() - print "Unit cell volume: ", structure.get_cell_volume() -``` - -In that case two duplicate structures are found for Si. - -## Accessing inputs and outputs - -Load again the calculation node used in Section [loadnode]: - -``` python - calc = load_node(PK) -``` - -Then type - -``` python - calc.inp. -``` - -and press `TAB`: you will see all the link names between the calculation and its input nodes. You can use a specific linkname to access the corresponding input node, e.g.: - -``` python - calc.inp.structure -``` - -You can use the `inp` method multiple times in order to browse the graph. For instance, if the input structure node that you just accessed is the output of another calculation, you can access the latter by typing - -``` python - calc2 = calc.inp.structure.inp.output_structure -``` - -Here `calc2` is the `PwCalculation` that produced the structure used as an input for `calc`. - -Similarly, if you type: - -``` python - calc2.out. -``` - -and then `TAB`, you will list all output link names of the calculation. One of them leads to the structure that was the input of `calc` we loaded previously: - -``` python - calc2.out.output_structure -``` - -Note that links have a single name, that was assigned by the calculation that used the corresponding input or produced the corresponding output, as illustrated in Fig. [fig:graph]. - -For a more programmatic approach, you can get a list of the inputs and outputs of a node, say `calc`, with the methods - -``` python - calc.get_inputs() - calc.get_outputs() -``` - -Alternatively, you can get a dictionary where the keys are the link names and the values are the linked objects, with the methods - -``` python - calc.get_inputs_dict() - calc.get_outputs_dict() -``` - -Note: You will sometime see entries in the dictionary with names like `output_kpoints_3511`. These exist because standard python dictionaries require unique key names while link labels may not be unique. Therefore, we use the link label plus the PK separated by underscores. - -## Pseudopotential families - -Pseudopotentials in AiiDA are grouped in “families” that contain one single pseudo per element. We will see how to work with UPF pseudopotentials (the format used by Quantum ESPRESSO and some other codes). -Download and untar the SSSP  pseudopotentials via the commands: - -```console - wget https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz -``` - -Then you can upload the whole set of pseudopotentials to AiiDA by to the following `verdi` command: - -```console -verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' -``` - -In the command above, `SSSP_efficiency_pseudos` is the folder containing the pseudopotentials, ’SSSP’ is the name given to the family and the last argument is its description. -Finally, you can list all the pseudo families present in the database with - -```console - verdi data upf listfamilies -``` diff --git a/docs/pages/2018_PRACE_MaX/sections/workflows.rst b/docs/pages/2018_PRACE_MaX/sections/workflows.rst deleted file mode 100644 index cdc955e9..00000000 --- a/docs/pages/2018_PRACE_MaX/sections/workflows.rst +++ /dev/null @@ -1,767 +0,0 @@ -.. rstcheck: ignore-language=python - -AiiDA Workflows -=============== - -The aim of the last part of this tutorial is to introduce the concept of -workflows in AiiDA. - -In this section, we will ask you to: - -1. Understand how to keep the provenance when running small python - scripts to convert one data object into another (postprocessing, - preparation of inputs, …) - -2. Understand how to represent simple python functions in the AiiDA - database - -3. Learn how to write a simple workflow in AiiDA (without and with - remote calculation submission) - -4. Learn how to write a workflow with checkpoints: this means that, even - if your workflows requires external calculations to start, them and - their dependences are managed through the daemon. While you are - waiting for the calculations to complete, you can stop and even - shutdown the computer in which AiiDA is running. When you restart, - the workflow will continue from where it was. - -5. (optional) Go a bit deeper in the syntax of workflows with - checkpoints (WorkChain), e.g. implementing a convergence workflow - using ``while`` loops. - -A note: this is probably the most “complex” part of the tutorial. We -suggest that you try to understand the underlying logic behind the -scripts, without focusing too much on the details of the workflows -implementation or the syntax. If you want, you can then focus more on -the technicalities in a second reading. - -Introduction ------------- - -The ultimate aim of this section is to create a workflow to calculate -the equation of state of silicon. This is a very common task for an *ab -initio* researcher. An equation of state consists in calculating the -total energy (E) as a function of the unit cell volume (V). The minimal -energy is reached at the equilibrium volume (V^{}). Equivalently, the -equilibrium is defined by a vanishing pressure (p=-dE/dV). In the -vicinity of the minimum, the functional form of the equation of state -can be approximated by a parabola. Such an approximation greatly -simplifies the calculation of the bulk modulus, that is proportional to -the second derivative of the energy (d:sup:`2E/dV`\ 2) (a more advanced -treatment requires fitting the curve with, e.g., the Birch–Murnaghan -expression). - -The process of calculating an equation of state puts together several -operations. First, we need to define and store in the AiiDA database the -basic structure of, e.g., bulk Si. Next, one has to define several -structures with different lattice parameters. Those structures must be -connected between them in the database, in order to ensure that their -provenance is recorded. In other words, we want to be sure that in the -future we will know that if we find a bunch of rescaled structures in -the database, they all descend from the same one. How to link two nodes -in the database in a easy way is the subject of Sec. [sec:provenancewf]. - -In the following sections, the newly created structures will then serve -as an input for total energy calculations performed, in this tutorial, -with Quantum ESPRESSO. This task is very similar to what you have done -in the previous part of the tutorial. Finally, you will fit the -resulting energies as a function of volume to get the bulk modulus. As -the EOS task is very common, we will show how to automate its -computation with workflows, and how to deal with both serial and -parallel (i.e., independent) execution of multiple tasks. Finally, we -will show how to introduce more complex logic in your workflows such as -loops and conditional statements (Sec. [sec:convpressure]), with an -example on a convergence loop to find iteratively the minimum of an EOS. - -[sec:provenancewf]Workfunctions: a way to generalize provenance in AiiDA ------------------------------------------------------------------------- - -.. figure:: /assets/2018_PRACE_MaX/workfunctions.png - :width: 100% - - Typical graphs created by using a workfunction. (a) The workfunction - ``create_structure`` takes a ``Str`` object as input and returns a single - ``StructureData`` object which is used as input for the workfunction - ``rescale`` together with a ``Float`` object. This latter workfunction - returns another ``StructureData`` object, defining a crystal having the - rescaled lattice constant. (b) Graph generated by nesting workfunctions. - A wrapper workfunction ``create_rescaled`` calls serially - ``create_structure`` and ``rescale``. This relationship is stored via - ``CALL`` links. - -Imagine to have a function that takes as input a string of the name of a -chemical element and generates the corresponding bulk structure as a -``StructureData`` object. The function might look like this (you will -find this function in the folder -``/home/aiida/tutorial_scripts/create_rescale.py`` on your virtual -machine): - -.. code:: python - - def create_diamond_fcc(element): - """ - Workfunction to create the crystal structure of a given element. - For simplicity, only Si and Ge are valid elements. - :param element: The element to create the structure with. - :return: The structure. - """ - import numpy as np - elem_alat= { - "Si": 5.431, # Angstrom - "Ge": 5.658, - } - - # Validate input element - symbol = str(element) - if symbol not in elem_alat.keys(): - raise ValueError("Valid elements are only Si and Ge") - - # Create cel starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - the_cell = np.array([[0., 0.5, 0.5], - [0.5, 0., 0.5], - [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - StructureData = DataFactory("structure") - structure = StructureData(cell=the_cell) - structure.append_atom(position=(0., 0., 0.), symbols=str(element)) - structure.append_atom(position=(0.25*alat, 0.25*alat, 0.25*alat), - symbols=str(element)) - return structure - -For the equation of state you need another function that takes as input -a ``StructureData`` object and a rescaling factor, and returns a -``StructureData`` object with the rescaled lattice parameter (you will -find this function in the same file ``create_rescale.py`` on your -virtual machine): - -.. code:: python - - def rescale(structure, scale): - """ - Workfunction to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - the_ase = structure.get_ase() - new_ase = the_ase.copy() - new_ase.set_cell(the_ase.get_cell() * float(scale), scale_atoms=True) - new_structure = DataFactory('structure')(ase=new_ase) - return new_structure - -In order to generate the rescaled starting structures, say for five -different lattice parameters you would combine the two functions. Enter -the following commands in the ``verdi shell`` from the -``tutorial_scripts`` folder. - -.. code:: python - - from create_rescale import create_diamond_fcc, rescale - - s0 = create_diamond_fcc("Si") - rescaled_structures = [rescale(s0, factor) for factor - in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and store them in the database: - -.. code:: python - - s0.store() - for struct in rescaled_structures: - struct.store() - -Run the commands above to store all the structures. - -As expected, all the structures that you have created are not linked in -any manner as you can verify via the ``get_inputs()/get_outputs()`` -methods of the StuctureData class. Instead, you would like these objects -to be connected as sketched in Fig. [Fig:workfunctions]a. Now that you -are familiar with AiiDA, you know that the way to connect two data nodes -is through a calculation. In order to “wrap” python functions and -automate the generation of the needed links, in AiiDA we provide you -with what we call “workfunctions”. A normal function can be converted to -a workfunction by using the ``@workfunction`` decorator[9] that takes -care of storing the execution as a calculation and adding the links -between the input and output data nodes. - -In our case, what you need to do is to modify the two functions as -follows (note that we import ``workfunction`` as ``wf`` to be shorter, -but this is not required). You can do it in the file -``create_rescale.py``: - -.. code:: python - - # Add this import - from aiida.work import workfunction as wf - - # Add decorators - @wf - def create_diamond_fcc(element): - ... - ... - - @wf - def rescale(structure, scale): - ... - ... - -*Important*: when you use workfunctions, you have to make sure that -their input and output are actually Data nodes, so that they can be -stored in the database. AiiDA objects such as ``StructureData``, -ParameterData, etc. carry around information about their provenance as -stored in the database. This is why we must use the special -database-storable types Float, Str, etc. as shown in the snippet below. - -Try now to run the following script: - -.. code:: python - - from aiida.orm.data.base import Float, Str - from create_rescale import create_diamond_fcc, rescale - - s0 = create_diamond_fcc(Str("Si")) - rescaled_structures = [rescale(s0,Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and check now that the output of ``s0`` as well as the input of the -rescaled structures point to an intermediate ProcessCalculation node, -representing the execution of the workfunction, see -Fig. [Fig:workfunctions]. - -For instance, you can check that the output links of ``s0`` are the five -``rescale`` calculations: - -.. code:: python - - s0.get_outputs() - -which outputs - -.. code:: python - - [, - , - , - , - ] - -and the inputs of each ProcessCalculation (“rescale”) are obtained with: - -.. code:: python - - for s in s0.get_outputs(): - print s.get_inputs() - -that will return - -.. code:: python - - [0.98, ] - [0.99, ] - [1.0, ] - [1.1, ] - [1.2, ] - -Workfunction nesting -~~~~~~~~~~~~~~~~~~~~ - -One key advantage of workfunctions is that they can be nested, namely, a -workfunction can invoke workfunctions inside its definition, and this -“call” relationship will also be automatically recorded in the database. -As an example, let us combine the two previously defined workfunctions -by means of a wrapper workfunction called “create\_rescaled” that takes -as input the element and the rescale factor. - -Type in your shell (or modify the functions defined in -``create_rescale.py`` and then run): - -.. code:: python - - @wf - def create_rescaled(element, scale): - """ - Workfunction to create and immediately rescale - a crystal structure of a given element. - """ - s0 = create_diamond_fcc(element) - return rescale(s0,scale) - -and create an already rescaled structure by typing - -.. code:: python - - s1 = create_rescaled(element=Str("Si"), scale=Float(0.98)) - -Now inspect the input links of ``s1``: - -.. code:: python - - In [6]: s1.get_inputs() - Out[6]: - [, - , - ] - -The object ``s1`` has three incoming links, corresponding to *two* -different calculations as input (in this case, pks 5002 and 5005). These -correspond to the calculations “create\_rescaled” and “rescale” as shown -in Fig. [Fig:workfunctions]b. It is normal that calculation 5005 has two -links, don’t worry about that[10]. To see the “call” link, inspect now -the outputs of the calculation appearing only once in the list. Write -down its ```` (in general, it will be different from 5002), then in -the shell load the corresponding node and inspect the outputs: - -.. code:: python - - In [12]: p1 = load_node() - In [13]: p1.get_outputs_dict() - -You should be able to identify the two \`\`children" calculations as -well as the final structure (you will see the calculations linked via -CALL links: these are calculation-to-calculation links representing the -fact that ``create_rescaled`` called two sub-workfunctions). The -graphical representation of what you have in the database should match -Fig. [Fig:workfunctions]b. - -[sec:sync] Run a simple workflow --------------------------------- - -Let us now use the workfunctions that we have just created to build a -simple workflow to calculate the equation of state of silicon. We will -consider five different values of the lattice parameter obtained -rescaling the experimental minimum, (a=5.431~), by a factor in ([0.96, -0.98, 1.0, 1.02, 1.04]). We will write a simple script that runs a -series of five calculations and at the end returns the volume and the -total energy corresponding to each value of the lattice parameter. For -your convenience, besides the functions that you have written so far in -the file ``create_rescale.py``, we provide you with some other utilities -to get the correct pseudopotential and to generate a pw input file, in -the module ``common_wf.py`` which has been put in the -``tutorial_scripts`` folder. - -We have already created the following script named -``simple_sync_workflow.py``, which you are free to look at but please go -through the lines carefully and make sure you understand them. If you -decide to create your own new script, make sure to also place it in the -folder ``tutorial_scripts``, otherwise the imports won’t work. - -Besides the functions in the local folder - -.. code:: python - - from create_rescale import create_diamond_fcc, rescale - from common_wf import generate_scf_input_params - -you need to import few further AiiDA classes and functions: - -.. code:: python - - from aiida.work import run, Process - from aiida.work import workfunction as wf - from aiida.orm.data.base import Str, Float - from aiida.orm import CalculationFactory, DataFactory - -The only imported function that deserves an explanation is ``run``. For -the time being, you just need to know that it is a function that needs -to be used to execute a new workflow. The actual body of the script is -the following. We suggest that you first have a careful look at it -before running it. - -.. code:: python - - # Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' - PwCalculation = CalculationFactory('quantumespresso.pw') - - scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) - labels = ["c1", "c2", "c3", "c4", "c5"] - - @wf - def run_eos_wf(codename, pseudo_family, element): - print "Workfunction node identifiers: {}".format(Process.current().calc) - s0 = create_diamond_fcc(Str(element)) - - calcs = {} - for label, factor in zip(labels, scale_facs): - s = rescale(s0, Float(factor)) - inputs = generate_scf_input_params(s, str(codename), Str(pseudo_family)) - print "Running a scf for {} with scale factor {}".format(element, factor) - result = run(PwCalculation, **inputs) - print "RESULT: {}".format(result) - calcs[label] = get_info(result) - - eos = [] - for label in labels: - eos.append(calcs[label]) - - # Return information to plot the EOS - ParameterData = DataFactory("parameter") - retdict = { - 'initial_structure': s0, - 'result': ParameterData(dict={'eos_data': eos}) - } - - return retdict - -If you look into the previous snippets of code, you will notice that the -way we submit a QE calculation is slightly different from what you have -seen in the first part of the tutorial. The following: - -.. code:: python - - result = run(PwCalculation, **inputs) - -runs in the current python session (without the daemon), waits for its -completion and returns the output in the user-defined variable -``result``. The latter is a dictionary whose values are the output nodes -generated by the calculation, with the link labels as keys. For example, -once the calculation is finished, in order to access the total energy, -we need to access the ParameterData node which is linked via the -“output\_parameters” link (see again Fig. 1 of Day 1 Tutorial, to see -inputs and outputs of a Quantum ESPRESSO calculation). Once the right -node is retrieved as ``result[output_parameters]``, we need to get the -``energy`` attribute. The global operation is achieved by the command - -.. code:: python - - result['output_parameters'].dict.energy - -As you see, the function ``run_eos_wf`` has been decorated as a -workfunction to keep track of the provenance. Finally, in order to get -the ```` associated to the workfunction (and print on the screen for -our later reference), we have used the following command to get the node -corresponding to the ProcessCalculation: - -.. code:: python - - from aiida.work import Process - print Process.current().calc - -To run the workflow it suffices to call the function ``run_eos_wf`` in a -python script providing the required input parameters. For simplicity, -we have included few lines at the end of the script that invoke the -function with a static choice of parameters: - -.. code:: python - - def run_eos(codename='pw-5.1@localhost', pseudo_family='GBRV_lda', element="Si"): - return run_eos_wf(Str(codename), Str(pseudo_family), Str(element)) - - if __name__ == '__main__': - run_eos() - -Run the workflow by running the following command from the -``tutorial_scripts`` directory: - -.. code:: console - - verdi run simple_sync_workflow.py - -and write down the ```` of the ProcessCalculation printed on screen -at execution. - -The command above locks the shell until the full workflow has completed -(we will see in a moment how to avoid this). While the calculation is -running, you can use (in a different shell) the command -``verdi work list`` to show ongoing and finished workfunctions. You can -“grep” for the ```` you are interested in. Additionally, you can use -the command ``verdi work status `` to show the tree of the -sub-workfunctions called by the root workfunction with a given ````. - -Wait for the calculation to finish, then call the function -``plot_eos()`` that we provided in the file ``common_wf.py`` to plot -the equation of state and fit it with a Birch–Murnaghan equation. - -[sec:wf-multiple-calcs]Run multiple calculations ------------------------------------------------- - -You should have noticed that the calculations for different lattice -parameters are executed serially, although they might perfectly be -executed in parallel because their inputs and outputs are not connected -in any way. In the language of workflows, these calculations are -executed in a synchronous (or blocking) way, whereas we would like to -have them running *asynchronously* (i.e., in a non-blocking way, to run -them in parallel). One way to achieve this to submit the calculation to -the daemon using the ``submit`` function. Make a copy of the script -``simple_sync_workflow.py`` that we worked on in the previous section -and name it ``simple_submit_workflow.py``. To make the new script work -asynchronously, simply change the following subset of lines: - -.. code:: python - - from aiida.work import run - [...] - for label, factor in zip(labels, scale_facs): - [...] - result = run(PwCalculation, **inputs) - calcs[label] = get_info(result) - [...] - eos = [] - for label in labels: - eos.append(calcs[label]) - -replacing them with - -.. code:: python - - from aiida.work import submit - from time import sleep - [...] - for label, factor in zip(labels, scale_facs): - [...] - calcs[label] = submit(PwCalculation, **inputs) - [...] - # Wait for the calculations to finish - for calc in calcs.values(): - while not calc.is_finished: - sleep(1) - - eos = [] - for label in labels: - eos.append(get_info(calcs[label].get_outputs_dict())) - -The main differences are: - -- ``run`` is replaced by ``submit`` - -- The return value of ``submit`` is not a dictionary describing the - outputs of the calculation, but it is the calculation node for that - submission. - -- Each calculation starts in the background and calculation nodes are - added to the ``calc`` dictionary. - -- At the end of the loop, when all calculations have been launched with - ``submit``, another loop is used to wait for all calculations to - finish before gathering the results as the final step. - -In the next section we will show you another way to achieve this, which -has the added bonus that it introduces checkpoints in the workfunction, -from which the calculation can be resumed should it be interrupted. - -After applying the modifications, run the script. You will see that all -calculations start at the same time, without waiting for the previous -ones to finish. - -If in the meantime you run ``verdi work status ``, all five -calculations are already shown as output. Also, if you run -``verdi calculation list``, you will see how the calculations are -submitted to the scheduler. - -[sec:workchainsimple]Workchains, or how not to get lost if your computer shuts down or crashes ----------------------------------------------------------------------------------------------- - -The simple workflows that we have used so far have been launched by a -python script that needs to be running for the whole time of the -execution, namely the time in which the calculations are submitted, and -the actual time needed by Quantum ESPRESSO to perform the calculation -and the time taken to retrieve the results. If you had killed the main -python process during this time, the workflow would not have terminated -correctly. Perhaps you have kill the calculation and you experienced the -unpleasant consequences: intermediate calculation results are -potentially lost and it is extremely difficult to restart a workflow -from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way -to insert checkpoints, where the main code defining a workflow can be -stopped (you can even shut down the machine on which AiiDA is running!). -We call these workfunctions with checkpoints “workchains” because, as -you will see, they basically amount to splitting a workfunction in a -chain of steps. Each step is then ran by the daemon, in a way similar to -the remote calculations. - -The basic rules that allow you to convert your workfunction-based script -to a workchain-based one are listed in Table [Tab:wf2frag], which focus -on the code used to perform the calculation of an equation of state. The -modifications needed are put side-to-side to allow for a direct -comparison. In the following, when referencing a specific part of the -code we will refer to the line number appearing in Table [Tab:wf2frag]. - -\|c\|c\| & Workchains & - -.. code:: console - - from aiida.work.workchain import WorkChain, ToContext - # ... - - class EquationOfState(WorkChain): - @classmethod - def define(cls, spec): - super(EquationOfState, cls).define(spec) - spec.input('element', valid_type=Str) - spec.input('code', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.outline( - cls.run_pw, - cls.return_results, - ) - - - def run_pw(self): - # ... - self.ctx.s0 = create_diamond_fcc(Str(self.inputs.element)) - - - calcs = {} - for label, factor in zip(labels, scale_facs): - s = rescale(self.ctx.s0,Float(factor)) - inputs = generate_scf_input_params( - s, str(self.inputs.code), self.inputs.pseudo_family) - # ... - future = self.submit(PwCalculation, **inputs) - calcs[label] = future - - # Ask the workflow to continue when the results are ready - # and store them in the context - return ToContext(**calcs) - - def return_results(self): - eos = [] - for label in labels: - eos.append(get_info(self.ctx[label].get_outputs_dict())) - - # Return information to plot the EOS - ParameterData = DataFactory('parameter') - retdict = { - 'initial_structure': self.ctx.s0, - 'result': ParameterData(dict={'eos_data': eos}) - } - for link_name, node in retdict.iteritems(): - self.out(link_name, node) - -- Instead of using decorated functions you need to define a class, - inheriting from a prototype class called ``WorkChain`` that is - provided by AiiDA (line 4) - -- Within your class you need to implement a ``define`` classmethod that - always takes ``cls`` and ``spec`` as inputs. (lines 6–7). Here you - specify the main information on the workchain, in particular: - - - the *inputs* that the workchain expects. This is obtained by means - of the method, which provides as the key feature the automatic - validation of the input types via the ``valid_type`` argument - (lines 8–10). The same holds true for outputs, as you can use the - ``spec.output()`` method to state what output types are expected - to be returned by the workchain. Both ``spec.input()`` and - ``spec.output()`` methods are optional, and if not specified, the - workchain will accept any set of inputs and will not perform any - check on the outputs, as long as the values are database storable - AiiDA types. - - - the ``outline`` consisting in a list of “steps” that you want to - run, put in the right sequence (lines 11–14). This is obtained by - means of the method ``spec.outline()`` which takes as input the - steps. *Note*: in this example we just split the main execution in - two sequential steps, that is, first ``run_pw`` then - ``return_results``. However, more complex logic is allowed, as - will be explained in the Sec. [sec:convpressure]. - -- You need to split your main code into methods, with the names you - specified before into the outline (``run_pw`` and ``return_results`` - in this example, lines 17 and 35). Where exactly should you split the - code? Well, the splitting points should be put where you would - normally block the execution of the script for collecting results in - a standard workfunction, namely whenever you call the method - ``.result()``. Each method should accept only one parameter, - ``self``, e.g. ``def step_name(self)``. - -- You will notice that the methods reference the attribute ``ctx`` - through ``self.ctx``, which is called the *context* and is inherited - from the base class ``WorkChain``. A python function or workfunction - normally just stores variables in the local scope of the function. - For instance, in the example of the subsection [sec:sync], you stored - the ``calc_results`` in the ``eos`` list, that was a local variable. - In workchains, instead, to preserve variables between different - steps, you need to store them in a special dictionary called - *context*. As explained above, the context variable ``ctx`` is - inherited from the base class ``WorkChain``, and at each step method - you just need to update its content. AiiDA will take care of saving - the context somewhere between workflow steps (on disk, in the - database, …, depending on how AiiDA was configured). For your - convenience, you can also access the value of a context variable as - ``self.ctx.varname`` instead of ``self.ctx[’varname’]`` (see e.g. - lines 19, 24, 38, 43). - -- Any submission within the workflow should not call the normal ``run`` - or ``submit`` functions, but ``self.submit`` to which you have to - pass the Process class, and a dictionary of inputs (line 28). - -- The submission in line 28, returns a future and not the actual - calculation, because at that point in time we have only just launched - the calculation to the daemon and it is not yet completed. Therefore - it literally is a “future” result. Yet we still need to add these - futures to the context, so that in the next step of the workchain, - when the calculations are in fact completed, we can access them and - continue the work. To do this, we can use the ``ToContext`` class. - This class takes a dictionary, where the values are the futures and - the keys will be the names under which the corresponding calculations - will be made available in the context when they are done. See line 33 - how the ``ToContext`` object is created and returned from the step. - By doing this, the workchain will implicitly wait for the results of - all the futures you have specified, and then call the next step *only - when all futures have completed*. - -- *Return values*: While in a normal workfunction you attach output - nodes to the ``FunctionCalculation`` by invoking the *return* - statement, in a workchain you need to call - ``self.out(link_name, node)`` for each node you want to return (line - 46-47). Of course, if you have already prepared a dictionary of - outputs, you can just use the following syntax: - - .. code:: python - - self.out_many(retdict) # Keys are link names, value the nodes - - The advantage of this different syntax is that you can start emitting - output nodes already in the middle of the execution, and not - necessarily at the very end as it happens for normal functions - (*return* is always the last instruction executed in a function). - Also, note that once you have called ``self.out(link_name, node)`` on - a given ``link_name``, you can no longer call ``self.out()`` on the - same ``link_name``: this will raise an exception. - -Inspect the example in the table that compares the two versions of -workfunctions to understand in detail the different syntaxes. - -Finally, the workflow has to be run. For this you have to use the -function ``run`` passing as arguments the ``EquationOfState`` class and -the inputs as key-value arguments. For example, you can execute - -.. code:: python - - run(EquationOfState, element=Str('Si'), code=Str('qe-pw-6.2.1@localhost'), - pseudo_family=Str('GBRV_lda')) - -While the workflow is running, you can check (in a different terminal) -what is happening to the calculations using ``verdi calculation list``. -You will see that after a few seconds the calculations are all submitted -to the scheduler and can potentially run at the same time. - -**Note:** You will see warnings that say -``Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint``, -these are generated by the ``PwCalculation`` which is unable to save a -checkpoint because it is not in a so called ‘importable path’. Simply -put this means that if AiiDA were to try and reload the class it -wouldn’t know which file to find it in. To get around this you could -simply put the workchain in a different file that is in the ‘PYTHONPATH’ -and then launch it by importing it in your launch file, this way AiiDA -knows where to find it next time it loads the checkpoint. - -As an additional exercise (optional), instead of running the main -workflow (``EquationOfState``), try to submit it. Note that the file -where the WorkChain is defined will need to be globally importable (so -the daemon knows how to load it) and you need to launch it (with -``submit``) from a different python file. The easiest way to achieve -this is typically to embed the workflow inside a python package. - -**Note:** As good practice, you should try to keep the steps as short as -possible in term of execution time. The reason is that the daemon can be -stopped and restarted only between execution steps and not if a step is -in the middle of a long execution. - -Finally, as an optional exercise if you have time, you can jump to the -Appendix [sec:convpressure], which shows how to introduce more complex -logic into your WorkChains (if conditionals, while loops etc.). The -exercise will show how to realize a convergence loop to obtain the -minimum-volume structure in a EOS using the Newton’s algorithm. diff --git a/docs/pages/2019_IIT_Mandi_India/index.rst b/docs/pages/2019_IIT_Mandi_India/index.rst deleted file mode 100644 index 5a9e8fe4..00000000 --- a/docs/pages/2019_IIT_Mandi_India/index.rst +++ /dev/null @@ -1,84 +0,0 @@ -.. _Mandi 2019 Homepage: - -2019, IIT Mandi, Mandi, India -============================= - -+-----------------+--------------------------------------------------------------------------------+ -| Related resources | -+=================+================================================================================+ -| Virtual Machine | `Quantum Mobile 19.09.0`_ | -+-----------------+--------------------------------------------------------------------------------+ -| python packages | `aiida-core 1.0.0b6`_, `aiida-quantumespresso 3.0.0a5`_, `aiidalab 19.08.0a1`_ | -+-----------------+--------------------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.4.1`_ | -+-----------------+--------------------------------------------------------------------------------+ - -.. _Quantum Mobile 19.09.0: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.09.0 -.. _aiida-core 1.0.0b6: https://pypi.org/project/aiida-core/1.0.0b6 -.. _aiida-quantumespresso 3.0.0a5: https://pypi.org/project/aiida-quantumespresso/3.0.0a5 -.. _aiidalab 19.08.0a1: https://pypi.org/project/aiidalab/19.8.0a1 -.. _Quantum Espresso 6.4.1: https://github.com/QEF/q-e/releases/tag/qe-6.4.1 - -These are the hands-on materials from the 3-day AiiDA tutorial |tutorial_name| from October 9-11, 2019. - -While participants of the tutorial used virtual machines on a cloud service, you can follow the tutorial on your own computer using the Quantum Mobile VirtualBox image linked above. -The image already contains all the required software. - -Getting started ---------------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/setup - ./sections/first_taste - -Sections --------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/verdi_cmdline - ./sections/verdi_shell - ./sections/calculations - ./sections/querybuilder - ./sections/workflows - -Appendices ----------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/appendix_computer_code_setup - ./sections/appendix_structure_data - ./sections/appendix_upf_data - ./sections/appendix_input_validation - ./sections/appendix_restarting_calculations - ./sections/appendix_queries - ./sections/appendix_workflow_logic - -Acknowledgements ----------------- - -The |tutorial_name| was made possible by support from IIT Mandi and MARVEL, and kindly hosted by IIT Mandi. - -.. image:: sponsors/iit_mandi.jpg - :target: http://iitmandi.ac.in/ - :width: 90px - -.. image:: sponsors/marvel.png - :target: http://nccr-marvel.ch - :width: 105px - -.. image:: sponsors/epfl.png - :target: https://epfl.ch - :width: 105px - -.. |tutorial_name| raw:: html - - National Workshop on Writing reproducible workflows for computational materials science using AiiDA diff --git a/docs/pages/2019_IIT_Mandi_India/notebooks/bandstructure.ipynb b/docs/pages/2019_IIT_Mandi_India/notebooks/bandstructure.ipynb deleted file mode 100644 index c1e2dc00..00000000 --- a/docs/pages/2019_IIT_Mandi_India/notebooks/bandstructure.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A real world workchain example: electronic band structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%aiida\n", - "\n", - "from datetime import datetime, timedelta\n", - "\n", - "from aiida.tools.dbimporters.plugins.cod import CodDbImporter\n", - "from aiida.engine import launch\n", - "\n", - "PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating the electronic band structure with an AiiDA workchain\n", - "This tutorial will show how useful a workchain can be in performing a well defined task, such as computing and visualizing the electronic band structure for a simple crystal structure. The goal of this tutorial is not to show you the intricacies of the actual workchain itself, but rather to serve as an example that workchains can simplify standard workflows in computational materials science. The workchain that we will use here will employ Quantum Espresso's pw.x code to calculate the charge densities for several crystal structures and compute a band structure from those. Many choices that normally face the researcher before being able to perform this calculation, such as the selection of suitable pseudo potentials, energy cutoff values, k-point grids and k-point paths along high symmetry points, are now performed automatically by the workchain. All that remains for the user to do is to simply define a structure, pass it to the workchain and sit back!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we import the crystal structure of Al as an example, and run the PwBandStructureWorkChain for that structure. The estimated run time is noted in a comment in the calculation cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading the COD importer so we can directly import structure from COD id's\n", - "importer = CodDbImporter()\n", - "# Make sure here to define the correct codename that corresponds to the pw.x code installed on your machine of choice\n", - "codename = 'qe-6.4.1-pw@localhost'\n", - "code = Code.get_from_string(codename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Importing example crystal structures from COD to AiiDA structure objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Al COD ID='9008460'\n", - "structure_Al = importer.query(id='9008460')[0].get_aiida_structure()\n", - "\n", - "structure_Al.get_formula()\n", - "\n", - "# The following structure can be used instead of Al, but will take much longer on the AWS machine.\n", - "# CaF2 COD ID='1000043' -- approximately 1/2 hour to run\n", - "# h-BN COD ID='9008997' -- approximately 45 mins to run\n", - "# GaAs COD ID='9008845' -- approximately 2 hours to run " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now we run the bandstructure workchain for the selected structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bandstructure workchain follows the following protocol:\n", - "* Determine the primitive cell of the input structure\n", - "* Run a vc-relax to relax the structure\n", - "* Refine the symmetry of the relaxed structure to ensure the primitive cell is used and run a self-consistent field calculation on it\n", - "* Run a non self-consistent field band structure calculation along a path of high symmetry k-points determined by [seekpath](http://materialscloud.org/tools/seekpath)\n", - "\n", - "Numerical parameters for the default 'theos-ht-1.0' protocol are determined as follows: \n", - "* Suitable pseudopotentials and energy cutoffs are automatically searched from the [SSSP library](http://materialscloud.org/sssp) installed on your machine (it uses the efficiency version 1.1).\n", - "* K-point mesh is selected to have a minimum k-point density of 0.2 Å-1\n", - "* A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations\n", - "\n", - "In case the pseudopotentials are not installed, they can be downloaded in a terminal as:\n", - "\n", - " wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz\n", - " tar -zxvf SSSP_efficiency_pseudos.tar.gz\n", - " \n", - "The protocol looks for a UPF file with a specific hash code, that is unique for each different file. \n", - "You can check that you have the right\n", - "one by performing a search in the database:\n", - "\n", - " qb=QueryBuilder()\n", - " qb.append(UpfData, filters={'attributes.md5':{'==':'cfc449ca30b5f3223ec38ddd88ac046d'}})\n", - " len(qb.all())\n", - "\n", - "'md5' is a searchable attribute of the pseudopotential data object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fermi_energy = results['scf_parameters'].dict.fermi_energy\n", - "results['band_structure'].show_mpl(y_origin=fermi_energy, plot_zero_axis=True)\n", - "\n", - "print(\"\"\"Final crystal symmetry: {spacegroup_international} (number {spacegroup_number})\n", - "Extended Bravais lattice symbol: {bravais_lattice_extended}\n", - "The system has inversion symmetry: {has_inversion_symmetry}\"\"\".format(\n", - " **results['seekpath_parameters'].get_dict()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to use a different pseudopotential family (or version of the family) (for instance [SSSP v1.0](https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz) instead of the default SSSP v1.1) you can pass an additional parameter when calling the WorkChain, as follows:\n", - " \n", - " protocol = Dict(dict={\n", - " 'name':'theos-ht-1.0', \n", - " 'modifiers': {\n", - " 'pseudo' : 'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - "\n", - "(note that only some values are accepted for pseudo, that you can find [here](https://github.com/aiidateam/aiida-quantumespresso/blob/b02250146576eb573ccb45d05047075f54853f9d/aiida_quantumespresso/utils/protocols/pw.py#L24))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al,\n", - " protocol=Dict(dict={\n", - " 'name':'theos-ht-1.0',\n", - " 'modifiers': {\n", - " 'pseudo':'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/pages/2019_IIT_Mandi_India/notebooks/querybuilder-template.ipynb b/docs/pages/2019_IIT_Mandi_India/notebooks/querybuilder-template.ipynb deleted file mode 100644 index 65dd70ae..00000000 --- a/docs/pages/2019_IIT_Mandi_India/notebooks/querybuilder-template.ipynb +++ /dev/null @@ -1,973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AiiDA's QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from datetime import datetime, timedelta\n", - "import numpy as np\n", - "from aiida import load_profile\n", - "from matplotlib import gridspec, pyplot as plt\n", - "load_profile()\n", - "from aiida.orm import load_node, Node, Group, Computer, User, CalcJobNode, Code\n", - "from aiida.plugins import CalculationFactory, DataFactory\n", - "\n", - "PwCalculation = CalculationFactory('quantumespresso.pw')\n", - "StructureData = DataFactory('structure')\n", - "KpointsData = DataFactory('array.kpoints')\n", - "Dict = DataFactory('dict')\n", - "UpfData = DataFactory('upf')\n", - "\n", - "def plot_results(query_res):\n", - " \"\"\"\n", - " :param query_res: The result of an instance of the QueryBuilder\n", - " \"\"\"\n", - " smearing_unit_set,magnetization_unit_set,pseudo_family_set = set(), set(), set()\n", - " # Storing results:\n", - " results_dict = {}\n", - " for pseudo_family, formula, smearing, smearing_units, mag, mag_units in query_res:\n", - " if formula not in results_dict:\n", - " results_dict[formula] = {}\n", - " # Storing the results:\n", - " results_dict[formula][pseudo_family] = (smearing, mag)\n", - " # Adding to the unit set:\n", - " smearing_unit_set.add(smearing_units)\n", - " magnetization_unit_set.add(mag_units)\n", - " pseudo_family_set.add(pseudo_family)\n", - "\n", - " # Sorting by formula:\n", - " sorted_results = sorted(results_dict.items())\n", - " formula_list = next(zip(*sorted_results))\n", - " nr_of_results = len(formula_list)\n", - "\n", - " # Checks that I have not more than 3 pseudo families.\n", - " # If more are needed, define more colors\n", - " #pseudo_list = list(pseudo_family_set)\n", - " if len(pseudo_family_set) > 3:\n", - " raise Exception('I was expecting 3 or less pseudo families')\n", - "\n", - " colors = ['b', 'r', 'g']\n", - "\n", - " # Plotting:\n", - " plt.clf()\n", - " fig=plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None)\n", - " gs = gridspec.GridSpec(2,1, hspace=0.01, left=0.1, right=0.94)\n", - "\n", - " # Defining barwidth\n", - " barwidth = 1. / (len(pseudo_family_set)+1)\n", - " offset = [-0.5+(0.5+n)*barwidth for n in range(len(pseudo_family_set))]\n", - " # Axing labels with units:\n", - " yaxis = (\"Smearing energy [{}]\".format(smearing_unit_set.pop()),\n", - " \"Total magnetization [{}]\".format(magnetization_unit_set.pop()))\n", - " # If more than one unit was specified, I will exit:\n", - " if smearing_unit_set:\n", - " raise ValueError('Found different units for smearing')\n", - " if magnetization_unit_set:\n", - " raise ValueError('Found different units for magnetization')\n", - " \n", - " # Making two plots, the top one for the smearing, the bottom one for the magnetization\n", - " for index in range(2):\n", - " ax=fig.add_subplot(gs[index])\n", - " for i,pseudo_family in enumerate(pseudo_family_set):\n", - " X = np.arange(nr_of_results)+offset[i]\n", - " Y = np.array([thisres[1][pseudo_family][index] for thisres in sorted_results])\n", - " ax.bar(X, Y, width=0.2, facecolor=colors[i], edgecolor=colors[i], label=pseudo_family)\n", - " ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15*index+5)\n", - " ax.set_xlim(-0.5, nr_of_results-0.5)\n", - " ax.set_xticks(np.arange(nr_of_results))\n", - " if index == 0:\n", - " plt.setp(ax.get_yticklabels()[0], visible=False)\n", - " ax.xaxis.tick_top()\n", - " ax.legend(loc=3, prop={'size': 18})\n", - " else:\n", - " plt.setp(ax.get_yticklabels()[-1], visible=False)\n", - " for i in range(0, nr_of_results, 2):\n", - " ax.axvspan(i-0.5, i+0.5, facecolor='y', alpha=0.2)\n", - " ax.set_xticklabels(list(formula_list),rotation=90, size=14, ha='center')\n", - " plt.show()\n", - "\n", - "def generate_query_graph(qh, out_file_name):\n", - "\n", - " def draw_vertice_settings(idx, vertice, **kwargs):\n", - " \"\"\"\n", - " Returns a string with all infos needed in a .dot file to define a node of a graph.\n", - " :param node:\n", - " :param kwargs: Additional key-value pairs to be added to the returned string\n", - " :return: a string\n", - " \"\"\"\n", - " if vertice['entity_type'].startswith('process'):\n", - " shape = \"shape=polygon,sides=4\"\n", - " elif vertice['entity_type'].startswith('data.code'):\n", - " shape = \"shape=diamond\"\n", - " else:\n", - " shape = \"shape=ellipse\"\n", - " filters = kwargs.pop('filters', None)\n", - " additional_string = \"\"\n", - " if filters:\n", - " additional_string += '\\nFilters:'\n", - " for k,v in filters.items():\n", - " additional_string += \"\\n {} : {}\".format(k,v)\n", - "\n", - "\n", - " label_string = \" ('{}')\".format(vertice['tag'])\n", - "\n", - " labelstring = 'label=\"{} {}{}\"'.format(\n", - " vertice['entity_type'], #.split('.')[-2] or 'Node',\n", - " label_string,\n", - " additional_string)\n", - " #~ return \"N{} [{},{}{}];\".format(idx, shape, labelstring,\n", - " return \"{} [{},{}];\".format(vertice['tag'], shape, labelstring)\n", - " nodes = {v['tag']:draw_vertice_settings(idx, v, filters=qh['filters'][v['tag']]) for idx, v in enumerate(qh['path'])}\n", - " links = [(v['tag'], v['joining_value'], v['joining_keyword']) for v in qh['path'][1:]]\n", - "\n", - " with open('temp.dot','w') as fout:\n", - " fout.write(\"digraph G {\\n\")\n", - " for l in links:\n", - " fout.write(' {} -> {} [label=\" {}\"];\\n'.format(*l))\n", - " for _, n_values in nodes.items():\n", - " fout.write(\" {}\\n\".format(n_values))\n", - "\n", - " fout.write(\"}\\n\")\n", - " import os\n", - " os.system('dot temp.dot -Tpng -o {}'.format(out_file_name))\n", - "\n", - "def store_formula_in_extra():\n", - " from aiida.orm import QueryBuilder\n", - " qb = QueryBuilder()\n", - " qb.append(StructureData, filters={'extras':{'!has_key':'formula'}})\n", - " for structure, in qb.all():\n", - " structure.set_extra('formula', structure.get_formula(mode='count'))\n", - "\n", - "store_formula_in_extra()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction to the QueryBuilder\n", - "As you will use `AiiDA` to run your calculations, the database that stores all the data and the provenance will quickly grow to be very large. To help you find the needle that you might be looking for in this big haystack, we need an efficient search tool. `AiiDA` provides a tool to do exactly this: the `QueryBuilder`. The `QueryBuilder` acts as the gatekeeper to your database, to whom you can ask questions about the contents of your database (also referred to as queries), by specifying what are looking for. In this part of the tutorial, we will focus on how to use the `QueryBuilder` to make these queries and understand/use the results.\n", - "\n", - "In order to use the `QueryBuilder`, we first need to import it. We can accomplish this by executing the `import` statement in the following cell. Go ahead and select the next cell, and press `Shift+Enter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida.orm import QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we can ask the `QueryBuilder` questions about our database, we first need to create an instance of it (i.e., we create a new query):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an instance of our `QueryBuilder` which we named `qb`, we are ready to start asking about the content of our database. For example, we may want to know exactly how many nodes there are in our database. To tell `qb` that we are interested in all the occurrences of the `Node` class in our database, we `append` it to the list of objects it should find. \n", - "\n", - "*Note*: The method is called `append` because, as we will see later, you can append multiple nodes to a `QueryBuilder` instance consecutively to search in the graph, as if you had a list: what we are doing is querying a graph, and for every vertice of the graph in our subquery, we will use one `append` call. But we'll see this use better in a few steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(Node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now narrowed down the scope of `qb` to just the nodes that are present in our database. To learn how many nodes there are exactly, all we have to do is to ask `qb` to count them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now as you may have learned in previous sections of the tutorial, nodes come in different kinds and flavors. For example, all our crystal structures that we have stored in the database are saved in nodes that are of the type `StructureData`. If instead of all the nodes, we would rather like to count only the crystal structure nodes, we simply tell our `QueryBuilder` to narrow its scope only to objects of type `StructureData`. Since we want to create a new independent query, we must create a new instance of the `QueryBuilder`. In the next cell, we have typed part of the code to count all the structure nodes. See if you can finish the line with the comment, to tell the `QueryBuilder` that you are only interested in `StructureData` nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "qb.append() # How do we finish this line to tell the query builder to count only the structure nodes?\n", - "qb.count()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.count()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of just counting how many structure nodes we have, we may also actually want to see some of them. This is as easy as telling our `QueryBuilder` that we are not interested in the `count` but rather that we want to retrieve `all` the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this command is very literal and does in fact retrieve **all** the structure nodes that are stored in your database, which may be very slow if your database becomes very large. One solution is to simply tell the `QueryBuilder` that we are, for example, only interested in 5 structure nodes. This can be done with the `limit` method as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to simply use the concept of array slicing, native to python, to specify a subset of the total return set to be returned. Notice that this example can be very slow in big databases. When you want performance, use the functionality native to the QueryBuilder like `limit`, that limit the number of results directly at the database level!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(None)\n", - "qb.all()[:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to know a little bit more about the retrieved structure nodes, we can loop through our results. This allows you, for instance, to print the formula of the structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "for structure, in qb.all():\n", - " print(structure.get_formula())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just a simple example how we can employ the `QueryBuilder` to get details about the contents of our database. We have now seen simple queries for the `Node` and `StructureData` classes, but the same rules apply to all the `AiiDA` node classes. For example we may want to count the number of entries for each of the node classes in the following list:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class_list = [Node, StructureData, KpointsData, Dict, UpfData, Code]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the tools we have learned so far, we can build a table of the number of occurrences of each of these node classes that are stored in our database. We simply loop over the `class_list` and create a `QueryBuilder` for each and count the entries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for class_name in class_list:\n", - " qb = QueryBuilder()\n", - " qb.append(class_name)\n", - "#TUT_USER_START\n", - " print() # Finish this line to print the results!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - " print('{:>15} | {:6}'.format(class_name.__name__, qb.count()))\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, you should see something like the following, where of course the numbers may differ for your database\n", - "\n", - "Class name | Entries\n", - "---------------|--------\n", - " Node | 10273 \n", - " StructureData | 271 \n", - " KpointsData | 953 \n", - " Dict | 2922 \n", - " UpfData | 85 \n", - " Code | 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Projection and filters\n", - "Up until now we have always asked the `QueryBuilder` to return the node class. However, we might not necessarily be interested in all the node's properties, but rather just a selected set or even just a single property. We can tell the `QueryBuilder` which properties we would like to be returned, by asking it to **project** those properties in the result. For example, we may only want to get the `uuid`s of a set of nodes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['uuid'])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By using the `project` keyword in the `append` call, we are informing the `QueryBuilder` that we are only interested in the `uuid` property of the `Node` class. Note that the value that we assign to `project` is a list, since we may want to specify more than one property. See if you can get the `QueryBuilder` to return *both* the `id` and the `uuid` of the first 5 `Node`s in the following cell.\n", - "\n", - "**Note**: in the context of the QueryBuilder, the `id` is the `pk` of the node." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_USER_START\n", - "qb.append(Node, project=)#? What should the value be for the project key\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Node, project=['id', 'uuid'])\n", - "#TUT_SOLUTION_END\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To give you an idea of the various properties that you can project for some of the `AiiDA` node classes you can consult the following table.\n", - "Note that this is by no means an exhaustive list:\n", - "\n", - "Class | Properties\n", - "---------|-----------\n", - "Node | `id`, `uuid`, `node_type`, `label`, `description`, `ctime`, `mtime`\n", - "Computer | `id`, `uuid`, `name`, `hostname`, `description`, `transport_type`, `scheduler_type`\n", - "User | `id`, `email`, `first_name`, `last_name`, `institution`\n", - "Group | `id`, `uuid`, `label`, `type_string`, `time`, `description`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same properties can also be used to *filter* for specific nodes in your database. Indeed, up until now, we only asked the `QueryBuilder` to return *all* the instances of a certain type of node, or at best a limited number of those (without specifying which ones). But in general we might be interested in a very specific node. For example, we may have the `id` of a certain node and we would like to know when it was created and last modified. We can tell the `QueryBuilder` to select nodes that only match that criterion, by telling it to **filter** based on that property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['ctime', 'mtime'], filters={'id': {'==': 1}})\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the syntax of the `filters` keyword. The value is a dictionary, where the keys indicate the node property that it is supposed to operate on, in this case the `id` property. The value of that key is again itself a dictionary, where the key indicates the logical operator `==` and the value corresponds to the value of the property.\n", - "\n", - "You may have multiple criteria that you want to filter for, in which case you can use the logical `or` and `and` operators. Let's say, for example, that you want the `QueryBuilder` to retrieve all the `StructureData` nodes that have a certain `label` **and** were created no longer than 12 days ago. You can translate this criterion by making use of the `and` operator which allows you to specify multiple filters that all have to be satisfied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(\n", - " Node, \n", - " filters={\n", - " 'and': [\n", - " {'ctime': {'>': datetime.now() - timedelta(days=12)}},\n", - " {'label': {'==':'graphene'}}\n", - " ]\n", - " }\n", - ")\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will have noticed that the `>` operator, and its related operators, can work with python `datetime` objects. These are just a few of the operators that `QueryBuilder` understands. Below you find a table with some of the logical operators that you can use:\n", - "\n", - "Operator | Data type | Example | Description\n", - "---------------------|-----------------------|------------------------------------|------------------\n", - "`==` | all | `{'==': '12'}` | equality operator\n", - "`in` | all | `{'in':['FINISHED', 'PARSING']}` | member of a set\n", - "`<`, `>`, `<=`, `>=` | float, int, datetime | `{'>': 5.2}` | size comparison operator\n", - "`like` | char, str | `{'like': 'calculation%'}` | string comparison, `%` is wildcard\n", - "`ilike` | char, str | `{'ilike': 'caLCulAtion%'}` | string comparison, capital insensitive\n", - "`or` | | `{'or': [{'<': 5.3}, {'>': 6.3}]}` | logical or operator\n", - "`and` | | `{'and': [{'>=': 2}, {'<=': 6}]}` | logical and operator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, try to write a query below that will retrieve all `Group` nodes whose `label` property starts with the string `tutorial`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your query here\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'tutorial%'}})\n", - "qb.limit(5)\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining relationships between query clauses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how we can use the `QueryBuilder` to search the database for entries of a specific node type, potentially projecting only specific properties and filtering for certain property values. However, our nodes do not live in a vacuum, but they are part of a graph and thus are linked one another. Therefore, we typically want to be able to search for nodes based on a certain relationship that they might have with other nodes. Consider for example that you have a `StructureData` node that was produced by some calculation. How would we be able to retrieve that calculation?\n", - "\n", - "To accomplish this, we need to be able to tell the `QueryBuilder` what the relationship is between the nodes that we are interested in. With the `QueryBuilder`, we can do the following to find all the structure nodes that have been created as an output by a `PwCalculation` process.\n", - "\n", - "**IMPORTANT NOTE**: In the graph, we are not looking for a `PwCalculation` process (since processes do not live in the graph, as you have learnt). We are actually looking for a `CalcJobNode` whose `process_type` column indicates that it was run by a `PwCalculation` process. Since this is a very common pattern, the `QueryBuilder` allows to directly append the `PwCalculation` process class as a shortcut, but it internally unwraps this into a query for a `CalcJobNode` with the appropriate filter on the `process_type`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are looking for pairs of nodes, we need to `append` the second node as well to the `QueryBuilder` instance. In the next line, to specify the relationship between the nodes, we need to be able to reference back to the `CalcJobNode` that is matched by the previous clause: therefore, we give it a `tag` with the `tag` keyword." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(StructureData, with_incoming='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal was to find `StructureData` nodes, so we `append` that to the `qb`. However, we didn't want to find just any `StructureData` nodes; they had to be an output of `PwCalculation`. \n", - "\n", - "Note how we expressed this relation by the `with_incoming` keyword, because we want a `StructureData` node having an *incoming* link from the `CalcJobNode` referenced by the `calculation` tag (i.e., the `StructureData` must be an *output* of the calculation).\n", - "\n", - "Now all we have to do is execute the query to retrieve our structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we did can be visualized schematically, thanks to a little tool we have written for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query1.png')\n", - "Image(filename='query1.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `with_incoming` keyword is only one of many potential relationships that exist between the various `AiiDA` nodes and that are implemented in the `QueryBuilder`. The table below gives an overview of the implemented relationships, which nodes they are defined for and what relation it implicates. The full documentation can be found [on this page of the AiiDA documentation](https://aiida-core.readthedocs.io/en/latest/querying/querybuilder/append.html#joining-entities)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entity from\t| Entity to\t| Relationship | Explanation\n", - "------------|-----------|------------------|------------\n", - "Node | Node | with_outgoing | One node as input of another node\n", - "Node | Node | with_incoming | One node as output of another node\n", - "Node | Node | with_descendants | One node as the ancestor of another node\n", - "Node | Node | with_ancestors | One node as descendant of another node\n", - "Group | Node | with_node | The group of a node\n", - "Node | Group | with_group | The node is a member of a group\n", - "Computer | Node | with_node | The computer of a node\n", - "Node | Computer | with_computer | The node of a computer\n", - "User | Node | with_node | The creator of a node is a user\n", - "Node | User | with_user | The node was created by a user" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, see if you can write a query that will return all the `UpfData` nodes that are a member of a `Group` whose name starts with the string `SSSP`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'SSSP%'}}, tag='group')\n", - "qb.append(UpfData, with_group='group')\n", - "qb.all()\n", - "#TUT_SOLUTION_END\n", - "# Here I also visualize what's going on:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query2.png')\n", - "Image(filename='query2.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Attributes and extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In section 2, we showed you how you can `project` specific properties of a `Node` and gave a list of properties that a `Node` instance possesses. Since then, we have come across a lot of different `AiiDA` data nodes, such as `StructureData` and `UpfData`, that were secretly `Node`'s in disguise. Or to put it correctly, as `AiiDA` employs the object-oriented programming paradigm, both `StructureData` and `UpfData` are examples of subclasses of the `Node` class and therefore inherit its properties. That means that whatever property a `Node` has, both `StructureData` and `UpfData` will have too. However, there is a semantic difference between a `StructureData` node and a `UpfData`, and so we may want to add a property to one that would not make sense for the other. To solve this, `AiiDA` introduces the concept of `attributes`. These are similar to properties, except that they are specific to the `Node` type that they are attached to. This allows you to add an `attribute` to a certain node, without having to change the implementation of all the others.\n", - "\n", - "For example, the `Dict` nodes that are generated as output of `PwCalculation`'s may have an attribute named `wfc_cutoff`. To project for this particular `attribute`, one can use exactly the same syntax as shown in section 2 for the regular `Node` properties, and one has to only prepend `attributes.` to the attribute name. Demonstration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='pw')\n", - "qb.append(Dict, with_incoming='pw', project=[\"attributes.wfc_cutoff\"])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not every `Dict` node has to have this attribute, in which case the `QueryBuilder` will return the python keyword `None`. Similar to the `attributes`, nodes also can have `extras`, which work in the same way, except that `extras` are mutable, which means that their value can be changed even after a node instance has been stored.\n", - "\n", - "If you are not sure which attributes a given node has, you can use the `.attributes` property to simply retrieve them all. It will return a dictionary with all the attributes the node has.\n", - "\n", - "Note that a node also has a number of additional methods. For instance, you can do `list(node.attributes_keys())` to get only the attribute keys, or `node.get_attribute('wfc_cutoff')` to get the value of a single attribute (these two variants are more efficient if the node has a lot of attributes and you don't need all data). Similarly, for extras, you have `node.extras`, `list(node.extras_keys())`, and `node.get_attribute('SOME_EXTRA_KEY')`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation)\n", - "node, = qb.first()\n", - "node.attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chemical-element symbol of a pseudopotential, that is represented by a `UpfData` node, is stored in the `element` attribute. Using the knowledge that filtering on attributes works exactly as for normal node properties, see if you can write a query that will search your database for pseudopotentials for silicon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_SOLUTION_START\n", - "qb.append(UpfData, filters={'attributes.element': {'==': 'Si'}})\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**For more exercises on relationships and attributes/extras, have a look at the appendix section on queries.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 5. A small high-throughput study" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The following section assumes that a specific dataset is present in your current AiiDA profile. If you are not running this script on the Virtual Machine of the AiiDA tutorial, this script will not produce the desired output.\n", - "You can download the Virtual Machine image from [https://aiida-tutorials.readthedocs.io](https://aiida-tutorials.readthedocs.io) along with the tutorial text (choose the correct version of the tutorial, depending on which version of AiiDA you want to try).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part of the tutorial, we will focus on how to systematically retrieve, parse and analyze the results of\n", - "multiple calculations using AiiDA. We know you’re able to do this yourself, but to save time, a set of calculations\n", - "have already been done with AiiDA for you on 57 perovskites, using three different pseudopotential families (LDA,\n", - "PBE and PBESOL, all from GBRV 1.2).\n", - "\n", - "**Note**: if you are not following the tutorial on the official virtual machine that comes with this data, but you are working with your own database, you first have to import this archive (download it from: https://drive.google.com/open?id=1eqJaJr7q1VsYYvGxqHxmDN7gBMs534rz) using `verdi import`\n", - "\n", - "These calculations are spin-polarized (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. The idea of this part of the tutorial is to “screen” for magnetic and metallic perovskites in a “high-throughput” way. As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. Once more check the list of groups in your database by typing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!verdi group list -A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The calculations needed for this task were put in three different groups whose names start with \"tutorial\" (one for each pseudopotential family). The main task is to make a plot showing, for all perovskites and for each pseudopotential family, the total magnetization and the $-TS$ contribution from the smearing to the total energy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start building the query\n", - "So we first of all need to instantiate a QueryBuilder instance. We `append` the groups of interest, which means that we select only groups that start with the string `tutorial_`. We can execute the query after this append (this will not affect the final results) and check whether we have retrieved 3 groups." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiating QB:\n", - "qb = QueryBuilder()\n", - "# Appending the groups I care about:\n", - "qb.append(Group, filters={'label':{'like':'tutorial_%'}}, project='label', tag='group')\n", - "# Visualize:\n", - "print(\"Groups:\", ', '.join([g for g, in qb.all()]))\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query3.png')\n", - "Image(filename='query3.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the calculations that are members of each group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# I want every PwCalculation that is a member of the specified groups:\n", - "#TUT_USER_START\n", - "qb.append(PwCalculation, tag='calculation', with_group=) # Complete the function call with the correct relationship-tag!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(PwCalculation, tag='calculation', with_group='group')\n", - "#TUT_SOLUTION_END\n", - "#Visualize\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query4.png') \n", - "Image(filename='query4.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the structures that are input of the calculation. \n", - "We extend the query to include the structures that are input of the calculations that match the query so far.\n", - "This means that we `append` StructureData, and defining the relationship with the calculation with corresponding keyword.\n", - "\n", - "For simplicity the formulas have been added in the extras of each structure node under the key `formula`. \n", - "(The function that does this is called `store_formula_in_extra` and can be found in the top cell of this notebook. It also uses the QueryBuilder!)\n", - "\n", - "Project the formula, stored in the extras under the key `formula`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(StructureData, project=, tag='structure', with_outgoing=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(StructureData, project=['extras.formula'], tag='structure', with_outgoing='calculation')\n", - "#TUT_SOLUTION_END\n", - "# Visualize:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query5.png')\n", - "Image(filename='query5.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the output of the calculation\n", - "Every successful PwCalculation outputs a `Dict` node that stores the parsed results as key-value pairs. You can find these pairs among the attributes of the `Dict` node. To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. For convenience, the units are also added as key/value pairs (with the same key name, but with `_units` appended). \n", - "Extend the query so that also the output `Dict` of each calculation is returned. Project only\n", - "the attributes relevant to your analysis.\n", - "\n", - "In particular, project (in this order):\n", - "\n", - "* The smearing contribution \n", - "* The units of the smearing contribution\n", - "* The magnetization \n", - "* The units of the magnetization\n", - "\n", - "(to know the projection keys, you can try to load one `CalcJobNode` from one of the groups, get its output `Dict` and inspect its `attributes` as discussed before, to see the key-value pairs that have been parsed)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(Dict, tag='results', project=['attributes.energy_smearing', ...], with_incoming=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Dict, tag='results',\n", - " project=['attributes.energy_smearing', 'attributes.energy_smearing_units',\n", - " 'attributes.total_magnetization', 'attributes.total_magnetization_units',\n", - " ], with_incoming='calculation'\n", - " )\n", - "#TUT_SOLUTION_END\n", - "\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query6.png') \n", - "Image(filename='query6.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print the query results\n", - "We can print the results to see if everything worked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = qb.all()\n", - "for item in results:\n", - " print(', '.join(map(str, item)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results\n", - "Getting a long list is not always helpful, and a graph can be much more clear and useful. To help you, we have already prepared a function that visualizes the results of the query. Run the following cell and, if you did everything correctly, you should get a graph with the results of your queries!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_results(results)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/__init__.py b/docs/pages/2019_IIT_Mandi_India/scripts/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/common_wf.py b/docs/pages/2019_IIT_Mandi_India/scripts/common_wf.py deleted file mode 100644 index 4a75d0ee..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/common_wf.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper functions.""" -from __future__ import absolute_import -import numpy as np -from aiida.plugins import CalculationFactory, DataFactory -from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def generate_scf_input_params(structure, code, pseudo_family): - """Construct a builder for the `PwCalculation` class and populate its inputs. - - :return: `ProcessBuilder` instance for `PwCalculation` with preset inputs - """ - parameters = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, # Important that this stays to get stress - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - - kpoints = KpointsData() - kpoints.set_kpoints_mesh([2, 2, 2]) - - builder = PwCalculation.get_builder() - builder.code = code - builder.structure = structure - builder.kpoints = kpoints - builder.parameters = Dict(dict=parameters) - builder.pseudos = validate_and_prepare_pseudos_inputs( - structure, pseudo_family=pseudo_family) - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calculation of Silicon with Quantum ESPRESSO" - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - - return builder - - -def birch_murnaghan(V, E0, V0, B0, B01): - """Compute energy by Birch Murnaghan formula.""" - r = (V0 / V)**(2. / 3.) - return E0 + 9. / 16. * B0 * V0 * (r - 1.)**2 * (2. + (B01 - 4.) * (r - 1.)) - - -def fit_birch_murnaghan_params(volumes_, energies_): - """Fit Birch Murnaghan parameters.""" - from scipy.optimize import curve_fit - - volumes = np.array(volumes_) - energies = np.array(energies_) - params, covariance = curve_fit( - birch_murnaghan, - xdata=volumes, - ydata=energies, - p0=( - energies.min(), # E0 - volumes.mean(), # V0 - 0.1, # B0 - 3., # B01 - ), - sigma=None) - return params, covariance - - -def plot_eos(eos_pk): - """ - Plots equation of state taking as input the pk of the ProcessCalculation - printed at the beginning of the execution of run_eos_wf - """ - import pylab as pl - from aiida.orm import load_node - eos_calc = load_node(eos_pk) - - data = [] - for V, E, units in eos_calc.outputs.eos.dict.eos: - data.append((V, E)) - - data = np.array(data) - params, _covariance = fit_birch_murnaghan_params(data[:, 0], data[:, 1]) - - vmin = data[:, 0].min() - vmax = data[:, 0].max() - vrange = np.linspace(vmin, vmax, 300) - - pl.plot(data[:, 0], data[:, 1], 'o') - pl.plot(vrange, birch_murnaghan(vrange, *params)) - - pl.xlabel("Volume (ang^3)") - # I take the last value in the list of units assuming units do not change - pl.ylabel("Energy ({})".format(units)) # pylint: disable=undefined-loop-variable - pl.show() diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/create_rescale.py b/docs/pages/2019_IIT_Mandi_India/scripts/create_rescale.py deleted file mode 100644 index 508df377..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/create_rescale.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper CalcFunctions for equation of state.""" -from __future__ import absolute_import -from aiida.engine import calcfunction - - -@calcfunction -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - symbol = element.value - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure - - -@calcfunction -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/equation_of_state.py b/docs/pages/2019_IIT_Mandi_India/scripts/equation_of_state.py deleted file mode 100644 index 703dc7e2..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/equation_of_state.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -"""Equation of State WorkChain.""" -from __future__ import absolute_import -from six.moves import zip - -from aiida.engine import WorkChain, ToContext, calcfunction -from aiida.orm import Code, Dict, Float, Str, StructureData -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -PwCalculation = CalculationFactory('quantumespresso.pw') -scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) -labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - -@calcfunction -def get_eos_data(**kwargs): - """Store EOS data in Dict node.""" - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -class EquationOfState(WorkChain): - """WorkChain to compute Equation of State using Quantum Espresso.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super(EquationOfState, cls).define(spec) - spec.input('code', valid_type=Code) - spec.input('element', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.output('initial_structure', valid_type=StructureData) - spec.output('eos', valid_type=Dict) - spec.outline( - cls.run_eos, - cls.results, - ) - - def run_eos(self): - """Run calculations for equation of state.""" - # Create basic structure and attach it as an output - initial_structure = create_diamond_fcc(self.inputs.element) - self.out('initial_structure', initial_structure) - - calculations = {} - - for label, factor in zip(labels, scale_facs): - - structure = rescale(initial_structure, Float(factor)) - inputs = generate_scf_input_params(structure, self.inputs.code, - self.inputs.pseudo_family) - - self.report( - 'Running an SCF calculation for {} with scale factor {}'. - format(self.inputs.element, factor)) - future = self.submit(PwCalculation, **inputs) - calculations[label] = future - - # Ask the workflow to continue when the results are ready and store them in the context - return ToContext(**calculations) - - def results(self): - """Process results.""" - inputs = { - label: self.ctx[label].get_outgoing().get_node_by_label( - 'output_parameters') - for label in labels - } - eos = get_eos_data(**inputs) - - # Attach Equation of State results as output node to be able to plot the EOS later - self.out('eos', eos) diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/plot_calculation_results.py b/docs/pages/2019_IIT_Mandi_India/scripts/plot_calculation_results.py deleted file mode 100644 index 628f8a7f..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/plot_calculation_results.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot results of your calculations.""" -from __future__ import absolute_import -from __future__ import print_function -import sys -from argparse import ArgumentParser -from six.moves import map, range, zip - -import numpy as np -from matplotlib import gridspec, pyplot as plt - - -def plot_results(inputfname, outputfname=None, plotformat=None): # pylint: disable=too-many-locals,too-many-statements,too-many-branches - """ - :param inputfile: the filename of the inputfile - :param (opt) outputfile: - The (optional) name for the outputfile. - :param (opt) format: - The (optional) file format, if not clear from the input file name - - Reads the input file provided by the user. - The format should be comma separated values: - formula, pseudo_family, smearing, smearing_units, magnetization, magnetization_units - - - This order has to be respected! - - If an outputfile is provided, use matplotlib.pyplot.savefig to save the figure - Else, calls matplotlib.pyplot.show to show in screen - """ - - # Reading the input file given to me: - with open(inputfname) as f: - lines = f.readlines() - - # Definining the variables I need to read the units and pseudo families - smearing_unit_set = set() - magnetization_unit_set = set() - pseudo_family_set = set() - - # Storing results: - results_dict = {} - - # Looping through results: - for line in lines: - try: - (formula, pseudo_family, smearing, smearing_units, magnetization, - magnetization_units) = [i.strip() for i in line.split(',')] - smearing, magnetization = list( - map(float, (smearing, magnetization))) - except Exception as e: # pylint: disable=broad-except - print("There was an {} reading line:\n" - "{}" - "Exception: {}\n" - "Skipping this line\n".format(type(e), line, e)) - continue - - # If this is a new formula, not present in results, - # Creating the key-value pair: - if formula not in results_dict: - results_dict[formula] = {} - - # Storing the results: - results_dict[formula][pseudo_family] = (smearing, magnetization) - - # Adding to the unit set: - smearing_unit_set.add(smearing_units) - magnetization_unit_set.add(magnetization_units) - pseudo_family_set.add(pseudo_family) - - # Sorting by formula: - sorted_results = sorted(results_dict.items()) - formula_list = list(zip(*sorted_results))[0] - nr_of_results = len(formula_list) - - # Checks that I have not more than 3 pseudo families. - # If more are needed, define more colors - - if len(pseudo_family_set) > 3: - raise Exception('I was expecting 3 or less pseudo families') - - colors = ['b', 'r', 'g'] - - # Plotting: - gs = gridspec.GridSpec(2, 1, hspace=0.01, left=0.1, right=0.94) - fig = plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None) - - # Defining barwidth - barwidth = 1. / (len(pseudo_family_set) + 1) - offset = [ - -0.5 + (0.5 + n) * barwidth for n in range(len(pseudo_family_set)) - ] - - # Axing labels with units: - yaxis = ("Smearing energy [{}]".format(smearing_unit_set.pop()), - "Total magnetization [{}]".format(magnetization_unit_set.pop())) - # If more than one unit was specified, I will exit: - if smearing_unit_set: - raise Exception('Found different units for smearing') - if magnetization_unit_set: - raise Exception('Found different units for magnetization') - - # Making two plots, one for the smearing, the lower for magnetization - for index in range(2): - ax = fig.add_subplot(gs[index]) - for i, pseudo_family in enumerate(pseudo_family_set): - X = np.arange(nr_of_results) + offset[i] - Y = np.array([ - thisres[1][pseudo_family][index] for thisres in sorted_results - ]) - - ax.bar(X, - Y, - width=0.2, - facecolor=colors[i], - edgecolor=colors[i], - label=pseudo_family) - ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15 * index + 5) - ax.set_xlim(-0.5, nr_of_results - 0.5) - ax.set_xticks(np.arange(nr_of_results)) - - if index: - plt.setp(ax.get_yticklabels()[-1], visible=False) - else: - plt.setp(ax.get_yticklabels()[0], visible=False) - ax.xaxis.tick_top() - ax.legend(loc=3, prop={'size': 18}) - - for i in range(0, nr_of_results, 2): - ax.axvspan(i - 0.5, i + 0.5, facecolor='y', alpha=0.2) - ax.set_xticklabels(list(formula_list), - rotation=90, - size=14, - ha='center') - - if outputfname is not None: - plt.savefig(outputfname, format=plotformat) - else: - plt.show() - - -if __name__ == '__main__': - parser = ArgumentParser() - parser.add_argument('inputfile', - help='The input file with the data to plot') - parser.add_argument('-o', - '--outputfile', - help='The outputfile to plot results to', - default=None) - parser.add_argument( - '-f', - '--format', - default=None, - help= - 'The format to plot, if outputfile is specified and no valid extension is provided' - ) - parsed_args = parser.parse_args(sys.argv[1:]) - plot_results(inputfname=parsed_args.inputfile, - outputfname=parsed_args.outputfile, - plotformat=parsed_args.format) diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/plot_convergence_pressure.py b/docs/pages/2019_IIT_Mandi_India/scripts/plot_convergence_pressure.py deleted file mode 100644 index 725133d3..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/plot_convergence_pressure.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot parabola using matplotlib.""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import range -from aiida.orm import load_node - - -def parabola(x, a, b, c): - """Parabola.""" - return a * x**2 + b * x + c - - -def show_parabola_results(pk): # pylint: disable=too-many-locals - """Plot (and show) parabola using matplotlib.""" - out_p = load_node(pk).outputs.steps.get_dict() - - step0 = out_p['step0'] - steps = out_p['steps'] - - import numpy as np - import pylab as pl - - data = [] - data.append((step0['V'], step0['E'])) - for step in steps: - data.append((step['V'], step['E'])) - - data = np.array(data) - min_V = data[:, 0].min() - max_V = data[:, 0].max() - - min_E = data[:, 1].min() - max_E = data[:, 1].max() - - min_V -= 10. - max_V += 10. - min_E -= 0.1 - max_E += 0.5 - - V_range = np.linspace(min_V, max_V, 500) - - for subplot in range(2): - pl.subplot(1, 2, 1 + subplot) - - pl.plot(data[:1, 0], data[:1, 1], 'o', color='red') - pl.annotate('0', xy=(data[0, 0], data[0, 1])) - - pl.plot(data[1:, 0], data[1:, 1], 'o', color='blue') - - color = 0.8 - for idx, step in enumerate(steps, start=1): - pl.plot(V_range, - parabola(V_range, step['a'], step['b'], step['c']), - color=(color, color, color)) - parabola_Vmin = -step['b'] / 2. / step['a'] - parabola_Emin = parabola(parabola_Vmin, step['a'], step['b'], - step['c']) - pl.plot([parabola_Vmin], [parabola_Emin], - 'x', - color=(color, color, color)) - pl.annotate(str(idx), xy=(step['V'], step['E'])) - pl.axvline(step['V'], color=(1., 0.7, 1.), linestyle='--') - color -= 0.25 - if color <= 0.: - color = 0. - - pl.axis([min_V, max_V, min_E, max_E]) - if subplot == 1: - # some zoom - pl.xlim((41.5, 44.)) - pl.ylim((-259.47, -259.45)) - pl.xlabel('Volume (ang^3)') - pl.ylabel('Energy (eV)') - - pl.show() - - -if __name__ == '__main__': - import sys - try: - pk_value = int(sys.argv[1]) - except (IndexError, ValueError): - print( - 'Pass as parameter the PK of the WorkChain calculating the pressure', - file=sys.stderr) - print('convergence to generate the plot.', file=sys.stderr) - sys.exit(1) - show_parabola_results(pk_value) diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/pressure_convergence.py b/docs/pages/2019_IIT_Mandi_India/scripts/pressure_convergence.py deleted file mode 100644 index 89f42195..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/pressure_convergence.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding: utf-8 -*- -"""Pressure convergence WorkChain""" -from __future__ import absolute_import -from __future__ import print_function -from aiida.engine import WorkChain, ToContext, while_, calcfunction, workfunction -from aiida.orm import Code, Float, Str, StructureData -from aiida.plugins import CalculationFactory, DataFactory - -from common_wf import generate_scf_input_params -from create_rescale import rescale - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def get_energy_first_derivative(stress): - """Return the energy first derivative from the stress. - - :param stress: the stress tensor in GPa - :return: first derivative of the energy in eV/Å^3 - """ - from numpy import trace - - GPa_to_eV_over_ang3 = 1. / 160.21766208 - - # Get the pressure (GPa) - pressure = trace(stress) / 3. - - # Pressure is -dE/dV; moreover p in kbar, we need to convert it to eV/Å^3 to be consistent - dE = -pressure * GPa_to_eV_over_ang3 - - return dE - - -def get_volume_energy_and_derivative(output_parameters): - """Return the volume, energy and energy derivative for given `PwCalculation` output parameters. - - Volume is in Å^3, energy in eV and energy derivative eV/Å^3 - - :param output_parameters: the `output_parameters` `Dict` result node of a `PwCalculation` - :return: tuple with volume, energy and energy derivative - """ - V = output_parameters.dict.volume - E = output_parameters.dict.energy - dE = get_energy_first_derivative(output_parameters.dict.stress) - - return V, E, dE - - -def get_energy_second_derivative(output_parameters_one, output_parameters_two): - """Return second derivative of the energy with respect to the volume given the results of two `PwCalculations`. - - The derivate is computed using the finite differences method. - - :param output_parameters_one: the `output_parameters` `Dict` result node of the first `PwCalculation` - :param output_parameters_two: the `output_parameters` `Dict` result node of the second `PwCalculation` - :return: the second derivative of the energy with respect to the volume - """ - dE1 = get_energy_first_derivative(output_parameters_one.dict.stress) - dE2 = get_energy_first_derivative(output_parameters_two.dict.stress) - V1 = output_parameters_one.dict.volume - V2 = output_parameters_two.dict.volume - - return (dE2 - dE1) / (V2 - V1) - - -def get_parabola_coefficients(V, E, dE, ddE): - """Return coefficients of a parabola fit to E = a*V^2 + b*V + c for the given volume and energy. - - :param V: volume in Å^3 - :param E: energy in eV - :param dE: the first derivative of the energy with respect to the volume - :param ddE: the second derivative of the energy with respect to the volume - :return: coefficients a, b and c - """ - a = ddE / 2. - b = dE - ddE * V - c = E - V * dE + V**2 * ddE / 2. - - return a, b, c - - -@workfunction -def get_structure(structure, step_data=None): - """Return a scaled version of the given structure, where the new volume is determined by the given step data.""" - initial_volume = structure.get_cell_volume() - - if step_data is None: - new_volume = initial_volume + 4. # In Å^3 - else: - # Minimum of a parabola - new_volume = -step_data.dict.b / 2. / step_data.dict.a - - scale_factor = (new_volume / initial_volume)**(1. / 3.) - scaled_structure = rescale(structure, Float(scale_factor)) - return scaled_structure - - -@calcfunction -def get_step_data(parameters_first, parameters_second=None): - """Generate a dictionary with the step parameters from the output parameters of a completed `PwCalculation`.""" - - # If the parameters of the second calculation are not passed, this is for the first step and we only return - # a dictionary with the volume, energy and derivative - if parameters_second is None: - V, E, dE = get_volume_energy_and_derivative(parameters_first) - return Dict(dict={'V': V, 'E': E, 'dE': dE}) - - # Otherwise, this is an iteration step and we return the difference between the steps - V, E, dE = get_volume_energy_and_derivative(parameters_first) - ddE = get_energy_second_derivative(parameters_first, parameters_second) - a, b, c = get_parabola_coefficients(V, E, dE, ddE) - - return Dict(dict={ - 'V': V, - 'E': E, - 'dE': dE, - 'ddE': ddE, - 'a': a, - 'b': b, - 'c': c - }) - - -@calcfunction -def bundle_step_data(step0, **kwargs): - """Bundle step data into Dict.""" - steps = [step.get_dict() for step in kwargs.values()] - print((step0, type(step0))) - print((steps, type(steps))) - return Dict(dict={'step0': step0.get_dict(), 'steps': steps}) - - -class PressureConvergence(WorkChain): - """Relax a structure using Newton's algorithm on the first derivative of the energy (minus the pressure).""" - - @classmethod - def define(cls, spec): - """Define spec of WorkChain.""" - # yapf: disable - # pylint: disable=bad-continuation - super(PressureConvergence, cls).define(spec) - spec.input('code', valid_type=Code, - help='Code setup to run `pw.x` to use for the calculations.') - spec.input('structure', valid_type=StructureData, - help='The structure to minimize.') - spec.input('pseudo_family', valid_type=Str, - help='Family of pseudopotentials to use for the calculations.') - spec.input('volume_tolerance', valid_type=Float, - help='Stop if the volume difference of two consecutive calculations is less than this threshold.') - spec.output('steps', valid_type=Dict, - help='The data of all the steps in the minimization process containing info about energy and volume') - spec.output('structure', valid_type=StructureData, - help='Final relaxed structure.') - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - - def setup(self): - """Launch the first calculation for the input structure, and a second calculation for a shifted volume.""" - scaled_structure = get_structure(self.inputs.structure) - self.ctx.last_structure = scaled_structure - - inputs0 = generate_scf_input_params(self.inputs.structure, - self.inputs.code, - self.inputs.pseudo_family) - inputs1 = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - - # Run two `PwCalculations` - future0 = self.submit(PwCalculation, **inputs0) - future1 = self.submit(PwCalculation, **inputs1) - - # Wait for them to complete before going to the next step - return ToContext(r0=future0, r1=future1) - - def put_step0_in_ctx(self): - """Store the outputs of the very first step in a specific dictionary.""" - self.ctx.step0 = get_step_data(self.ctx.r0.outputs.output_parameters) - - # Prepare the list containing the steps: step 1 will be stored here by move_next_step - self.ctx.steps = [] - - def move_next_step(self): - """Main part of the algorithm. - - Compare the results of two consecutive calculations and use Newton's algorithm on the pressure by fitting the - results with a parabola and setting the next volume to calculate to the parabola minimum. - - The oldest calculation gets replaced by the most recent and a new calculation is launched that will replace - the most recent. - """ - # Computer the new Volume using Newton's algorithm and create the new corresponding structure by scaling it - new_step_data = get_step_data(self.ctx.r0.outputs.output_parameters, - self.ctx.r1.outputs.output_parameters) - scaled_structure = get_structure(self.inputs.structure, new_step_data) - self.ctx.steps.append(new_step_data) - - # Replace the older step with the latest and set the current structure - self.ctx.r0 = self.ctx.r1 - self.ctx.last_structure = scaled_structure - - inputs = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - future = self.submit(PwCalculation, **inputs) - - return ToContext(r1=future) - - def not_converged(self): - """Return True if the worflow is not converged yet (i.e. the volume changed significantly).""" - r0_out = self.ctx.r0.outputs.output_parameters - r1_out = self.ctx.r1.outputs.output_parameters - - return abs(r1_out.dict.volume - - r0_out.dict.volume) > self.inputs.volume_tolerance - - def finish(self): - """Attach the result nodes as outputs.""" - steps = { - 'step{}'.format(index + 1): step - for index, step in enumerate(self.ctx.steps) - } - bundled_steps = bundle_step_data(step0=self.ctx.step0, **steps) - - self.out('steps', bundled_steps) - self.out('structure', self.ctx.last_structure) diff --git a/docs/pages/2019_IIT_Mandi_India/scripts/simple_sync_workflow.py b/docs/pages/2019_IIT_Mandi_India/scripts/simple_sync_workflow.py deleted file mode 100644 index 1a89206e..00000000 --- a/docs/pages/2019_IIT_Mandi_India/scripts/simple_sync_workflow.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -"""Simple workflow example""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import zip - -from aiida.engine import run, Process, calcfunction, workfunction -from aiida.orm import load_code, Dict, Float, Str -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -# Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' -PwCalculation = CalculationFactory('quantumespresso.pw') - - -@calcfunction -def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -@workfunction -def run_eos_wf(code, pseudo_family, element): - """Run an equation of state of a bulk crystal structure for the given element.""" - - # This will print the pk of the work function - print('Running run_eos_wf<{}>'.format(Process.current().pid)) - - scale_factors = (0.96, 0.98, 1.0, 1.02, 1.04) - labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - calculations = {} - - # Create an initial bulk crystal structure for the given element, using the calculation function defined earlier - initial_structure = create_diamond_fcc(element) - - # Loop over the label and scale_factor pairs - for label, factor in list(zip(labels, scale_factors)): - - # Generated the scaled structure from the initial structure - structure = rescale(initial_structure, Float(factor)) - - # Generate the inputs for the `PwCalculation` - inputs = generate_scf_input_params(structure, code, pseudo_family) - - # Launch a `PwCalculation` for each scaled structure - print('Running a scf for {} with scale factor {}'.format( - element, factor)) - calculations[label] = run(PwCalculation, **inputs) - - # Bundle the individual results from each `PwCalculation` in a single dictionary node. - # Note: since we are 'creating' new data from existing data, we *have* to go through a `calcfunction`, otherwise - # the provenance would be lost! - inputs = { - label: result['output_parameters'] - for label, result in calculations.items() - } - eos = create_eos_dictionary(**inputs) - - # Finally, return the results of this work function - result = {'initial_structure': initial_structure, 'eos': eos} - - return result - - -def run_eos(code=load_code('qe-6.4.1-pw@localhost'), - pseudo_family='SSSP', - element='Si'): - """Helper function to run EOS WorkChain.""" - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - -if __name__ == '__main__': - run_eos() diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_computer_code_setup.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_computer_code_setup.rst deleted file mode 100644 index 2a14a59d..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_computer_code_setup.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _2019_mandi_appendix_computer_code_setup: - -Setting up computers and codes -============================== - -To setup a computer, please refer to the `online documentation `_. -Instructions to setup a code for a computer can be `found here `_. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_input_validation.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_input_validation.rst deleted file mode 100644 index f41e6e0c..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_input_validation.rst +++ /dev/null @@ -1,94 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Calculation input validation -============================ - -This appendix shows additional ways to debug errors with Quantum ESPRESSO, namely -how to take advantage of a powerful tool to validate the input to Quantum ESPRESSO -and possibly suggest the correct name to misspelled keywords. - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. -Let's check for example this input dictionary, where we inserted two mistakes: - -.. code:: python - - parameters_dict = { - 'CTRL': { - 'calculation': 'scf', - 'restart_mode': 'from_scratch', - }, - 'SYSTEM': { - 'nat': 2, - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - -The two mistakes in the dictionary are the following: - -- We wrote a wrong namelist name (``CTRL`` instead of ``CONTROL``). -- We inserted the number of atoms explicitly: while that is indeed how the - number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system, - since this information is already contained within the StructureData. - -After we correct the dictionary ``parameters_dict``, we could run the calculation directly. -Alternatively, we can use a tool of the the `aiida-quantumespresso` plugin, -the 'input helper', which checks the inputs before submitting the calculation. - -In your script, after you define ``parameters_dict``, -you can validate it with the following command (note that you need to pass also an input crystal structure, -``structure``, to allow the validator to perform all the needed checks): - -.. code:: python - - PwCalculation = CalculationFactory('quantumespresso.pw') - validated_dict = PwCalculation.input_helper(parameters_dict, structure=structure) - parameters = Dict(dict=validated_dict) - - -The ``input_helper`` method will check for the correctness of the input parameters. -If misspelling or incorrect keys are detected, the method raises an exception, -which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, -without providing the namelists (useful if you don't remember where to put some variables in the input file). -Hence, you can provide to ``input_helper`` a dictionary like this one: - -.. code:: python - - parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, - } - - validated_dict = PwCalculation.input_helper( - parameters_dict, - structure=structure, - flat_mode=True - ) - -If you print ``validated_dict``, it will look like the following: - -.. code:: python - - { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_queries.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_queries.rst deleted file mode 100644 index 90adaa64..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_queries.rst +++ /dev/null @@ -1,89 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Queries in AiiDA - Examples and Exercises -========================================= - -Optional exercises on relationships (Task 3) --------------------------------------------- - -**Hint for the exercises:** - -- You can have projections on properties of more than one entity in your query. - You just have to add the ``project`` key (specifying the list of properties that you want to project) along with the corresponding entity when you append it. - -**Exercises:**   Try to write the following queries: - -- Find all descendants of a ``StructureData`` with a certain ``uuid`` (descendants indicate all nodes that can be reached by following outgoing links starting from a given node, with any number of hops). - For every match, print both the ``StructureData`` node and the descendant. - -- Find all the ``FolderData`` nodes created by a specific user. - -Optional exercises on attributes and extras (Task 4) ----------------------------------------------------- - -**Hint for the exercises:** - -- You can easily order or limit the number of the results by using the ``order_by()`` and ``limit()`` methods of the QueryBuilder. - For example, we can order all our ``CalcJobNode``s by their ``id`` and limit the result to the first 10 as follows: - - .. code:: python - - qb = QueryBuilder() - qb.append(CalcJobNode, tag='calc') - qb.order_by({'calc': 'id'}) - qb.limit(10) - qb.all() - -**Exercises:** - -- Write a code snippet to check how many pseudopotentials you have for each element. - -- Smearing contribution to the total energy for calculations: - - 1. Write a query that returns the smearing contribution to the energy stored in some instances of ``Dict`` output by a ``CalcJobNode`` generated by running a ``PwCalculation`` process. - 2. Extend the previous query to also get the input structures of the ``CalcJobNode``. - 3. Which structures have a smearing contribution to the energy smaller or equal to ``-0.02``? - -Summarizing what we learned by now - An example ------------------------------------------------ - -At this point you should be able to do queries with projections, joins and filters. -Moreover, you saw how to apply filters and projections even on attributes and extras. -Let's discover the full power of the ``QueryBuilder`` with a complex graph query that allows you to project various properties from different nodes and apply different filters and joins. - -Imagine that you would like to get the smearing energy for all the calculations that have a smearing energy below a given value and have a Sn\ :sub:`2`\ O\ :sub:`3` as input. -Moreover, besides the smearing energy, you would like to print the units of this energy and the formula of the structure that was given to the calculation. -The graphical representation of this query can be seen in :numref:`fig_qb_example_2_2019` and the actual query follows: - -.. _2019_mandi_fig_qb_example_2_2019: -.. figure:: include/images/qb_example_2.png - :width: 50% - :align: center - - Complex graph query. - -.. code:: python - - qb = QueryBuilder() - qb.append( - StructureData, - project=['extras.formula'], - filters={'extras.formula': 'BaO3Ti'}, - tag='structure' - ) - qb.append( - CalcJobNode, - tag='calculation', - with_incoming='structure' - ) - qb.append( - Dict, - tag='results', - filters={'attributes.energy_smearing':{'<=':-1e-8}}, - project=[ - 'attributes.energy_smearing', - 'attributes.energy_smearing_units', - ], - with_incoming='calculation' - ) - qb.all() diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_restarting_calculations.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_restarting_calculations.rst deleted file mode 100644 index bb06ea24..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_restarting_calculations.rst +++ /dev/null @@ -1,90 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -Restarting calculations -======================= - -Up till now, we have presented several cases where, for example, the wrong input parameters were passed to a calculation, causing it to fail. -Often we had to retype a lot of code, to setup a new calculation with the correct inputs. -In addition, there are many real-life scenarios where one would need to restart calculations from completed calculations with largely the same inputs. -For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to relaunch a calculation from one that has already completed, while having the chance to add or change inputs before launching it. -As an example, let us first submit a total energy calculation using a parameters dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, - } - -In this case, we set a very low number of self consistent iterations (3), which is too small to be able to reach the desired accuracy of 10\ :sup:`-14`. -This means the calculation will not converge and will not be successful, despite there not being any actual mistake in the parameters dictionary. - -To easily restart from the previous calculation, instead of retyping all the inputs, you can simply use the ``get_builder_restart`` method of the ``CalcJobNode``. -Just like the ``get_builder`` method of the ``Process`` class, this will create an instance of the ``ProcessBuilder``, except this one will have all the inputs pre-populated based on the node. -To do so, simply load the node of a terminated calculation job in a ``verdi shell``, that you want to restart from, e.g.: - -.. code:: python - - failed_calculation = load_node('') - restart_builder = failed_calculation.get_builder_restart() - -If you simply type ``restart_builder``, you can see that all the inputs have already been set to those that were used for the original calculation. -The only thing that now remains to be done, is to replace those inputs that caused the failure in the first place. - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['ELECTRONS']['electron_maxstep'] = 80 - restart_builder.parameters = Dict(dict=parameters) - -Simply giving the calculation some more steps in the convergence cycle will probably already fix the problem. - -.. note:: - - We have to create a new ``Dict`` node as we are changing its contents, and the original input is immutable as it would break the provenance. - -However, in this Quantum ESPRESSO example even more can be done, by using the results of the previous calculation to speed up the restart. -To do so, we simply have to set the input ``parent_folder`` to the remote working directory of the old calculation, i.e.: - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['CONTROL']['restart_mode'] = 'restart' - restart_builder.parameters = Dict(dict=parameters) - restart_builder.parent_folder = failed_calculation.outputs.remote_folder - -Note that this particular step is specific to a ``PwCalculation``, but the restart builder concept works for any calculation class. -Any of the inputs can be changed or set. -For example, you may also want to record that this calculation is a restart, in addition to the provenance graph, by setting the label or description: - -.. code:: python - - restart_builder.metadata.label = 'Restart from PwCalculation<{}>'.format(failed_calculation.pk) - -Ultimately whatever needs to be changed for the restart is up to you, but the restart builder makes it a lot easier. -Finally, to submit the restart, since it is a process builder, it works exactly as any other builder: - -.. code:: python - - from aiida.engine import launch - results, node = launch.run.get_node(restart_builder) - -You can now inspect the restarted calculation to verify that this time it actually completed successfully. -Using the restart builder, the required code to setup a calculation is much shorter than the one needed to launch a new one from scratch. -There is no need to load or create many of the inputs such as the pseudopotentials, structures and k-points, -because they were reused from the first calculation. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_structure_data.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_structure_data.rst deleted file mode 100644 index 309cddda..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_structure_data.rst +++ /dev/null @@ -1,280 +0,0 @@ -.. _2019_mandi_appendix_structure_data: - -General comments ----------------- - -This section contains an example of how you can use the -:class:`~aiida:aiida.orm.nodes.data.structure.StructureData` object -to create complex crystals. - -With the :class:`~aiida:aiida.orm.nodes.data.structure.StructureData` class we did not -try to have a full set of features to manipulate crystal structures. -Indeed, other libraries such as `ASE `_ exist, -and we simply provide easy -ways to convert between the ASE and the AiiDA formats. On the other hand, -we tried to define a "standard" format for structures in AiiDA, that can be -used across different codes. - - -How to use ``StructureData`` -------------------------------- - -Take a look at the following example:: - - alat = 4. # angstrom - cell = [[alat, 0., 0.,], - [0., alat, 0.,], - [0., 0., alat,], - ] - s = StructureData(cell=cell) - s.append_atom(position=(0.,0.,0.), symbols='Fe') - s.append_atom(position=(alat/2.,alat/2.,alat/2.), symbols='O') - -With the commands above, we have created a crystal structure ``s`` with -a cubic unit cell and lattice parameter of 4 angstrom, and two atoms in the -cell: one iron (Fe) atom in the origin, and one oxygen (O) at the center of -the cube (this cell has been just chosen as an example and most probably does -not exist). - -.. note:: As you can see in the example above, both the cell coordinates and - the atom coordinates are expressed in angstrom, and the position of - the atoms are given in a global absolute reference frame. - -In this way, any periodic structure can be defined. If you want to import -from ASE in order to specify the coordinates, e.g., in terms of the crystal -lattice vectors, see the guide on the conversion to/from ASE below. - -When using the :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` -method, further parameters can be passed. In particular, one can specify -the mass of the atom, particularly important if you want e.g. to run a -phonon calculation. If no mass is specified, the mass provided by -`NIST `_ (retrieved in October 2014) -is going to be used. The list of -masses is stored in the module :py:mod:`aiida.common.constants`, in the -``elements`` dictionary. - -Moreover, in the :class:`~aiida:aiida.orm.nodes.data.structure.StructureData` class -of AiiDA we also support the storage of crystal structures with alloys, -vacancies or partial occupancies. -In this case, the argument of the parameter ``symbols`` -should be a list of symbols, if you want to consider an alloy; -moreover, you must pass a ``weights`` list, with the same length as ``symbols``, -and with values between 0. (no occupancy) and 1. (full occupancy), to specify -the fractional occupancy of that site for each of the symbols specified -in the ``symbols`` list. The sum of -all occupancies must be lower or equal to one; if the sum is lower than one, -it means that there is a given probability of having a vacancy at that -specific site position. - -As an example, you could use:: - - s.append_atom(position=(0.,0.,0.),symbols=['Ba','Ca'],weights=[0.9,0.1]) - -to add a site at the origin of a structure ``s`` consisting of an alloy of -90% of Barium and 10% of Calcium (again, just an example). - -The following line instead:: - - s.append_atom(position=(0.,0.,0.),symbols='Ca',weights=0.9) - -would create a site with 90% probability of being occupied by Calcium, and -10% of being a vacancy. - -Utility properties ``s.is_alloy`` and ``s.has_vacancies`` can be used to -verify, respectively, if more than one element if given in the symbols list, -and if the sum of all weights is smaller than one. - -.. note:: if you pass more than one symbol, the property ``s.is_alloy`` will - always be ``True``, even if only one symbol has occupancy 1 and - all others have occupancy zero:: - - >>> s = StructureData(cell=[[4,0,0],[0,4,0],[0,0,4]]) - >>> s.append_atom(position=(0.,0.,0.), symbols=['Fe', 'O'], weights=[1.,0.]) - >>> s.is_alloy - True - - -Internals: Kinds and Sites --------------------------- -Internally, the :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` -method works by manipulating the kinds and sites of the current structure. -Kinds are instances of the :class:`~aiida:aiida.orm.nodes.data.structure.Kind` class and -represent a chemical species, with given properties (composing element or -elements, occupancies, mass, ...) and identified -by a label (normally, simply the element chemical symbol). - -Sites are instances of the :class:`~aiida:aiida.orm.nodes.data.structure.Site` class -and represent instead each single site. Each site refers -to a :class:`~aiida:aiida.orm.nodes.data.structure.Kind` to -identify its properties (which element it is, the mass, ...) and to its three -spatial coordinates. - -The :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` works in -the following way: - -* It creates a new :class:`~aiida:aiida.orm.nodes.data.structure.Kind` - class with the properties passed as parameters - (i.e., all parameters except ``position``). - -* It tries to identify if an identical Kind already exists in the list - of kinds of the structure (e.g., in the same atom with the same mass was - already previously added). Comparison of kinds is performed using - :py:meth:`aiida.orm.nodes.data.structure.Kind.compare_with`, and in particular - it returns ``True`` if the mass and the list of symbols and of weights are - identical (within a threshold). If an identical kind ``k`` is found, - it simply adds a new site referencing to kind ``k`` and with the provided - ``position``. Otherwise, it appends ``k`` to the list of kinds of the current - structure and then creates the site referencing to ``k``. The name of the - kind is chosen, by default, equal to the name of the chemical symbol (e.g., - "Fe" for iron). - -* If you pass more than one species for the same chemical symbol, but e.g. with - different masses, a new kind is created and the name is obtained postponing - an integer to the chemical symbol name. For instance, the following lines:: - - s.append_atom(position = [0,0,0], symbols='Fe', mass = 55.8) - s.append_atom(position = [1,1,1], symbols='Fe', mass = 57) - s.append_atom(position = [1,1,1], symbols='Fe', mass = 59) - - will automatically create three kinds, all for iron, with names ``Fe``, - ``Fe1`` and ``Fe2``, and masses 55.8, 57. and 59. respecively. - -* In case of alloys, the kind name is obtained concatenating all chemical - symbols names (and a X is the sum of weights is less than one). The same - rules as above are used to append a digit to the kind name, if needed. - -* Finally, you can simply specify the kind_name to automatically generate a - new kind with a specific name. This is the case if you want a name different - from the automatically generated one, or for instance if you want to create - two different species with the same properties (same mass, symbols, ...). - This is for instance the case in Quantum ESPRESSO in order to describe an - antiferromagnetic cyrstal, with different magnetizations on the different - atoms in the unit cell. - - In this case, you can for instance use:: - - s.append_atom(position = [0,0,0], symbols='Fe', mass = 55.845, name='Fe1') - s.append_atom(position = [2,2,2], symbols='Fe', mass = 55.845, name='Fe2') - - To create two species ``Fe1`` and ``Fe2`` for iron, with the same mass. - - .. note:: You do not need to specify explicitly the mass if the default one - is ok for you. However, when you pass explicitly a name and it coincides - with the name of an existing species, all properties that you - specify must be identical to the ones of the existing species, or the - method will raise an exception. - - .. note:: If you prefer to work with the - internal :class:`~aiida:aiida.orm.nodes.data.structure.Kind` - and :class:`~aiida:aiida.orm.nodes.data.structure.Site` classes, - you can obtain the same - result of the two lines above with:: - - from aiida.orm.nodes.data.structure import Kind, Site - s.append_kind(Kind(symbols='Fe', mass=55.845, name='Fe1')) - s.append_kind(Kind(symbols='Fe', mass=55.845, name='Fe1')) - s.append_site(Site(kind_name='Fe1', position=[0.,0.,0.])) - s.append_site(Site(kind_name='Fe2', position=[2.,2.,2.])) - - -Conversion to/from ASE ----------------------- - -If you have an AiiDA structure ``s``, you can get an ``ase.Atom`` object by -just calling the :meth:`aiida:aiida.orm.nodes.data.structure.StructureData.get_ase` -method:: - - ase_atoms = s.get_ase() - -.. note:: As we support alloys and vacancies in AiiDA, while ``ase.Atom`` does not, - it is not possible to export to ASE a structure with vacancies or alloys. - -If instead you have as ASE Atoms object and you want to load the structure -from it, just pass it when initializing the class:: - - StructureData = DataFactory('structure') - # or: - # from aiida.orm import StructureData - aiida_structure = StructureData(ase = ase_atoms) - -Creating multiple species -+++++++++++++++++++++++++ - -We implemented the possibility of specifying different Kinds (species) in the -``ase.atoms`` and then importing them. - -In particular, if you specify atoms with different mass in ASE, during the -import phase different kinds will be created:: - - >>> import ase - >>> StructureData = DataFactory("structure") - >>> ase_structure = ase.Atoms('Fe2') - >>> ase_structure[0].mass = 55. - >>> ase_structure[1].mass = 56. - >>> ase_structure.cell = cell # defines a periodic cell - >>> s = StructureData(ase=ase_structure) - >>> for kind in s.kinds: - >>> print(kind.name, kind.mass) - Fe 55.0 - Fe1 56.0 - -Moreover, even if the mass is the same, but you want to get different species, -you can use the ASE ``tags`` to specify the number to append to the element -symbol in order to get the species name:: - - >>> import ase - >>> StructureData = DataFactory("structure") - >>> ase_structure = ase.Atoms('Fe2') - >>> ase_structure[0].tag = 1 - >>> ase_structure[1].tag = 2 - >>> ase_structure.cell = cell - >>> s = StructureData(ase=ase_structure) - >>> for kind in s.kinds: - >>> print(kind.name) - Fe1 - Fe2 - -.. note:: in complicated cases (multiple tags, masses, ...), - it is possible that exporting a AiiDA structure - to ASE and then importing it again will not perfectly preserve the kinds and - kind names. - -Conversion to/from pymatgen ---------------------------- - -AiiDA structure can be converted to pymatgen's `Molecule`_ and -`Structure`_ objects by using, accordingly, -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen_molecule` -and -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen_structure` -methods:: - - pymatgen_molecule = aiida_structure.get_pymatgen_molecule() - pymatgen_structure = aiida_structure.get_pymatgen_structure() - -A single method -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen` can be -used for both tasks: converting periodic structures (periodic boundary -conditions are met in all three directions) to pymatgen's Structure and -other structures to pymatgen's Molecule:: - - pymatgen_object = aiida_structure.get_pymatgen() - -It is also possible to convert pymatgen's Molecule and Structure -objects to AiiDA structures:: - - StructureData = DataFactory("structure") - from_mol = StructureData(pymatgen_molecule=mol) - from_struct = StructureData(pymatgen_structure=struct) - -Also in this case, a generic converter is provided:: - - StructureData = DataFactory("structure") - from_mol = StructureData(pymatgen=mol) - from_struct = StructureData(pymatgen=struct) - -.. note:: Converters work with version 3.0.13 or later of - pymatgen. Earlier versions may cause errors. - -.. _Molecule: http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Molecule -.. _Structure: http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Structure diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_upf_data.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_upf_data.rst deleted file mode 100644 index 99be879b..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_upf_data.rst +++ /dev/null @@ -1,124 +0,0 @@ -.. _2019_mandi_appendix_upf_data: - -Introduction: Pseudopotential families -++++++++++++++++++++++++++++++++++++++ - -The procedure of attaching a pseudopotential file to each atomic species -can easily become tedious. In many situations, you will not produce a different -pseudopotential file for every calculation you perform; most likely, when you start a project -you will stick to a pseudopotential file that is adequate for what you need. -Furthermore, in a high-throughput calculation, you will like to do calculations -over several elements while keeping the same functional. -That is also part of the reason why there are several projects -(like the `PSLibrary `_ -or `GBRV `_ -to name a few), that intend to develop a set of pseudopotentials that covers most -of the periodic table for different functionals. - -For this reason we introduced the concept of *pseudopotential families*. -Each family is a set of pseudopotentials that are grouped together in a special type of -`AiiDA Group of nodes`. Within each family, at most one pseudopotential -can be present for a given chemical element. - -A pseudopotential family does not necessarily have to cover the whole periodic table. -This means that you can create a pseudopotential family containing only the -pseudopotentials for a few elements that you are interested in. - -.. note:: - In principle, you can group different kinds of pseudopotentials into the same family. - It is your responsibility to group only those with the same type, - or obtained using the same functionals, approximations and / or levels of theory. - -Creating a pseudopotential family -+++++++++++++++++++++++++++++++++ - -.. note:: - The following commands are specific to the `Quantum ESPRESSO - interface `_. - For interfaces to other codes, please refer to the respective plugin documentation. - -In the following, we will create a pseudopotential family. -First, you need to collect the pseudopotential files which should go into the family in a -single folder -- we'll call it ``path/to/folder``. You can then add the family to -the AiiDA database with ``verdi``:: - - verdi data upf uploadfamily path/to/folder name_of_the_family "some description for your convenience" - -where ``name_of_the_family`` should be a unique name for the family, -and the final parameter is a string that is set in the ``description`` field of the group. - -If the a pseudopotential family with the same ``name_of_the_family`` exists already, -the pseudopotentials in the folder will be added to the existing group. -The code will raise an error if you try to add two (different) pseudopotentials for the same element. - -After the upload (which may take some seconds, so please be patient) -the pseudopotential family will be ready for use. - -.. hint:: - If you upload pseudopotentials which are already present in your database, - AiiDA will use the existing ``UPFData`` node instead of creating a duplicate one. - You can use the optional flag ``--stop-if-existing`` to instead abort - (without changing anything in the database) if an existing pseudopotential is found. - - -Getting the list of existing families -+++++++++++++++++++++++++++++++++++++ - -To see which pseudopotential families already exist in the database, type - -.. code-block:: console - - $ verdi data upf listfamilies - -Add a ``-d`` (or ``--with-description``) flag if you also want to read the description of each family. - -You can also filter the groups to get only a list of those containing a given set of elements -using the ``-e`` option. For instance, if you want to get only the families containing the -elements ``Ba``, ``Ti`` and ``O``, use - -.. code-block:: console - - $ verdi data upf listfamilies -e Ba Ti O - - -For more information on the command line options, type - -.. code-block:: console - - $ verdi data upf listfamilies -h - - -Manually adding pseudopotentials -++++++++++++++++++++++++++++++++ - -If you do not want to use pseudopotentials from a family, it is also possible to manually -add them to the database (even though we discourage this in general). - -A possible way of doing it is the following: we start by creating a list of -pseudopotential filenames that we need to use:: - - raw_pseudos = [ - "Ba.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF", - "Ti.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF", - "O.pbesol-n-rrkjus_psl.0.1-tested-pslib030.UPF"] - -In this simple example, we expect the pseudopotentials to be in the same folder -of the script. Then, we loop over the filenames and add them to the AiiDA database. -The ``get_or_create`` method checks if the pseudopotential is already in the database -and either stores it, or just returns the node already present in the database. -The second value returned is a boolean and tells us if the pseudopotential was -already present or not. - -:: - - UpfData = DataFactory('upf') - pseudos_to_use = [] - - for filename in raw_pseudos: - absname = os.path.abspath(filename) - pseudo, created = UpfData.get_or_create(absname, use_first=True) - pseudos_to_use.append(pseudo) - -.. note:: - When the pseudopotential is created, it is parsed and the elements to which it refers is stored - in the database and can be accessed using the ``pseudo.element`` property, as shown above. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/appendix_workflow_logic.rst b/docs/pages/2019_IIT_Mandi_India/sections/appendix_workflow_logic.rst deleted file mode 100644 index 94eef675..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/appendix_workflow_logic.rst +++ /dev/null @@ -1,118 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -.. _2019_mandi_workflow_logic: - -More workflow logic: while loops and conditional statements -=========================================================== - -In the previous sections, you have been introduced to ``WorkChains``, and the reason for using them over 'standard' work functions. -However, in the :ref:`original example<2019_mandi_workchainsimple>`, the ``spec.outline`` was quite simple, with a 'static' sequence of two steps. -Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). -To support this scenario, the ``spec.outline`` can support logic: `while` loops and `if/elif/else` blocks. -The simplest way to explain it is to show an example: - -.. code:: python - - from aiida.engine import if_, while_ - - spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), - ) - -that would *roughly* correspond, in a python syntax, to: - -.. code:: python - - s1() - if isA(): - s2() - elif isB(): - s3() - else: - s4() - s5() - while condition(): - s6() - -The only constraint is that condition functions (in the example above ``isA``, ``isB`` and ``condition``) must be class methods that returns ``True`` or ``False`` depending on whether the condition is met or not. - -A suggestion on how to write new workchains: use the outline to help you in designing the logic. -First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. -Then, define one by one the methods. - -.. _2019_mandi_convpressure: - -Pressure convergence --------------------- - -For example, we have prepared a simple workflow (using work chains, work functions and calculation functions) to optimize the lattice parameter of silicon efficiently using Newton's algorithm on the energy derivative, i.e. the pressure :math:`p=-dE/dV`. -You can download this code :download:`here <../scripts/pressure_convergence.py>` -The outline looks like this: - -.. code:: python - - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - -This outline already roughly explains the algorithm: after an initialization (``setup``) and putting the first step (number zero) in the ctx (``put_step0_in_ctx``), a function to move to the next step is called (``move_next_step``). -This is iterated while a given convergence criterion is not met (``not_converged``). -Finally, some reporting is done, including returning some output nodes (``finish``). - -If you are interested in the details of the algorithm, you can inspect the file. -The main ideas are described here: - -``setup`` -Generate a ``pw.x`` calculation for the input structure (with volume -(V)), and one for a structure where the volume is :math:`V+4 \mbox{\normalfont\AA}^3` (just to get a closeby volume). -Store the results in the context as ``r0`` and ``r1`` - -``put_step0_in_ctx`` -Store in the context :math:`V`, :math:`E(V)` and :math:`dE/dV` for the first calculation ``r0`` - -``move_next_step`` -This is the most important function. Calculate :math:`V`, :math:`E(V)` and :math:`dE/dV` for ``r1``. -Also, estimate :math:`d^2E/dV^2` from the finite difference of the first derivative of ``r0`` and ``r1`` (helper functions to achieve this are provided). -Get the :math:`a`, :math:`b` and :math:`c` coefficients of a parabolic fit :math:`E = aV^2 + bV + c` and estimate the expected minimum of the EOS function as the minimum of the fit :math:`V_0 = -b / 2a`. -Finally, replace ``r0`` with ``r1`` in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume :math:`V_0`, that will be stored in the context replacing ``r1``. -In this way, at the next iteration ``r0`` and ``r1`` will contain the latest two simulations. -Finally, at each step some relevant information (coefficients :math:`a`, :math:`b` and :math:`c`, volumes, energies, energy derivatives, ...) are stored in a list called ``steps``. -This whole list is stored in the context because it provides quantities to be preserved between different work chain steps. - -``not_converged`` -Return ``True`` if convergence has not been achieved yet. -Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold ``volume_tolerance``. - -``finish`` -This is the final step. -Mainly, we return the output nodes: ``steps`` with the list of results at each step, and ``structure`` with the final converged structure. - -The results returned in ``steps`` can be used to represent the evolution of the minimisation algorithm. -A possible way to visualize it is presented in :numref:`fig_convpressure` obtained with an initial lattice constant of `alat = 5.2`. - -.. _2019_mandi_fig_convpressure: -.. figure:: include/images/convergence_pressure.png - - Example of results of the convergence algorithm presented in this section. - The bottom plot is a zoom near the minimum. - The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. - The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/calculations.rst b/docs/pages/2019_IIT_Mandi_India/sections/calculations.rst deleted file mode 100644 index c2dcecc3..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/calculations.rst +++ /dev/null @@ -1,625 +0,0 @@ -.. _2019_mandi_calculations: - -Submit, monitor and debug calculations -====================================== - -In this section we'll be learning how to create new data in AiiDA. - -We will use the `Quantum Espresso `_ package to launch a simple `density functional theory `_ calculation of a silicon crystal using the :doi:`PBE exchange-correlation functional <10.1103/PhysRevB.54.16533>` and check its results. -While we're going to debug these issues 'manually' here, workflows (which you'll encounter later in this tutorial) can help you avoid these issues systematically. - -Note that besides the ``aiida-quantumespresso`` plugin, AiiDA comes with plugins for a range of other codes, all of which are listed in the `AiiDA plugin registry `_. - -Computer setup --------------- - -In a production environment, AiiDA would typically be running on your work station or laptop, while launching calculations -on remote high-performance compute resources that you have SSH access to. -For this reason AiiDA has the concept of a ``Computer`` to run calculations on. - -To keep things simple, Quantum ESPRESSO (together with several other *ab initio* codes) has been installed directly in the -``codes`` subfolder of your virtual machine, -and you are going to launch your first calculations on the same computer where AiiDA is installed. -Nevertheless, we're now going to set up this computer for launching calculations: - -.. code:: bash - - verdi computer setup --config computer.yml - -where ``computer.yml`` is a configuration file in the `YAML format `_) that you can :download:`download here `. This is its content: - -.. literalinclude:: include/configuration/computer.yml - -.. note:: - When used without the ``--config`` option, ``verdi computer setup`` will prompt you for the required information, just like you have seen when :ref:`setting up a profile<2019_mandi_setup_verdi_quicksetup>`. - The configuration file should work for the virtual machine that comes with this tutorial - but may need to be adapted when you are running AiiDA in a different environment, - as explained in :ref:`this appendix<2019_mandi_appendix_computer_code_setup>`. - -Finally, you need to provide AiiDA with information on how to access the ``Computer``. -For remote computers with ``ssh`` transport, this would involve e.g. an SSH key. -For ``local`` computers, this is just a "formality" (press enter to confirm the default cooldown time): - -.. code:: bash - - verdi computer configure local localhost - -.. note:: - - For remote computers with ``ssh`` transport, use ``verdi computer configure ssh`` instead of ``verdi computer configure local``. - -Your ``localhost`` computer should now show up in - -.. code:: bash - - verdi computer list - -Before proceeding, test that it works: - -.. code:: bash - - verdi computer test localhost - - -Code setup ----------- - -Now that we have our localhost set up, let's configure the ``Code``, namely the ``pw.x`` executable. -As with the computer, we have prepared a configuration file for you to :download:`download `. -This is its content: - -.. literalinclude:: include/configuration/code.yml - :language: yaml - -Once you have the configuration file in your local working environment, set up the code: - -.. code:: bash - - verdi code setup --config code.yml - -.. warning:: - - The configuration should work for the virtual machine that comes with this tutorial. - If you are following this tutorial in a different environment, you will need to install Quantum ESPRESSO and adapt the configuration to your needs, - as explained in :ref:`this appendix<2019_mandi_appendix_computer_code_setup>`. - -Similar to the computers, you can list all the configured codes with: - -.. code:: bash - - verdi code list - -Verify that it now contains a code named ``qe-6.4.1-pw`` that we just configured. -You can always check the configuration details of an existing code using: - -.. code:: bash - - verdi code show qe-6.4.1-pw - -.. note:: - - The ``generic`` profile has already a number of other codes configured. - See ``verdi -p generic code list``. - - -The AiiDA daemon ----------------- - -First of all, check that the AiiDA daemon is actually running. -The AiiDA daemon is a program that - - * runs continuously in the background - * waits for new calculations to be submitted - * transfers the inputs of new calculations to your compute resource - * checks the status of your calculation at the compute resource, and - * retrieves the results from the compute resource - -Check the status of the daemon process by typing in the terminal: - -.. code:: bash - - verdi daemon status - -If the daemon is running, the output should look like - -.. code:: bash - - Profile: default - Daemon is running as PID 2050 since 2019-04-30 12:37:12 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 2055 2.147 0 2019-04-30 12:37:12 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers - -If this is not the case, type in the terminal - -.. code:: bash - - verdi daemon start - -to start the daemon. - -Creating a new calculation --------------------------- - -In the following, we'll be working in the ``verdi shell``. -As you go along, feel free to keep track of your commands by -copying them into a python script ``test_pw.py``. - -.. note:: - - The ``verdi shell`` imports a number of AiiDA internals so that you as the user don't have to. - You can also make those available to a python script, by running it using - - .. code:: bash - - verdi run - - -Every calculation sent to a cluster is linked to a *code*, which describes -the executable file to be used as well as some metadata. -Let's have a look at the codes already installed on your machine: - -.. code:: bash - - verdi code list - -There should be a number of them. Here, we're interested in the "PWscf" executable of Quantum Espresso, i.e. in codes for the ``quantumespresso.pw`` plugin: - -.. code:: bash - - verdi code list -P quantumespresso.pw - -Pick the correct codename, that might look like, e.g. -``qe-6.4.1-pw@localhost`` and load it in the verdi shell. - -.. code:: python - - code = load_code("") - -.. note:: - - ``load_code`` returns an object of type ``Code``, which is the general AiiDA class for describing simulation codes. - -Let's build the inputs for a new ``PwCalculation`` (defined by the ``quantumespresso.pw`` plugin, the default plugin for the code you chose before) - -.. code:: python - - builder = code.get_builder() - -As the first step, assign a (short) label or a (long) description to your -calculation, that you might find convenient in the future. - -.. code:: python - - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calc with Quantum ESPRESSO on Si" - -This information will be saved in the database for later queries or -inspection. Note that you can press TAB after writing ``builder.`` to -see all inputs available for this calculation. -In order to figure out which data type is expected for a particular input, such as ``builder.structure``, -and whether the input is optional or required, use ``builder.structure??``. - -Now, specify the number of machines (a.k.a. cluster nodes) -you are going to run on and the maximum time allowed for the -calculation. -The general options grouped under ``builder.metadata.options`` are independent of -the code or plugin, and will be passed to the scheduler that handles the -queue on your compute resource . - -.. code:: python - - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - -Again, to see the list of available options, type -``builder.metadata.options.`` and hit the TAB button. - -Preparation of inputs -~~~~~~~~~~~~~~~~~~~~~ - -A Quantum Espresso calculation needs a number of inputs: - -1. `Pseudopotentials `_ -2. a structure -3. a mesh in reciprocal space (k-points) -4. a number of input parameters - -These are mirrored in the inputs of the ``aiida-quantumespresso`` plugin -(see `documentation `_). -We'll start with the structure, k-points, and pseudopotentials -and leave the input parameters as the last thing to setup. - -.. admonition:: Exercise - - Use what you learned in the previous section to load the ``structure`` and ``kpoints`` inputs for your calculation: - - * Use a silicon crystal structure - * Define a ``2x2x2`` mesh of k-points. - - Note: If you just copy and paste code that you executed previously, - this may result in duplication of information on your database. - In fact, you can re-use an existing structure stored in your database [#f1]_. Use a combination of the bash command - ``verdi data structure list`` and of the shell command ``load_node()`` - to get an object representing the structure created earlier. - -Attaching the input information to the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once you've created a ``structure`` node and a ``kpoints`` node, -attach it to the calculation: - -.. code:: python - - builder.structure = structure - builder.kpoints = kpoints - -.. note:: - - The builder accepts both *stored* and *unstored* data nodes. - AiiDA will take care of storing the unstored nodes upon submission. - If you decide not to submit, nothing will be stored in the database. - -PWscf also needs information on the pseudopotentials, -in the form of a dictionary, where keys are the names of the elements and the values are the corresponding ``UpfData`` objects containing the information on the pseudopotential. -However, instead of creating the dictionary by hand, we can use a helper function that picks the right pseudopotentials for our structure from a pseudopotential *family*. -You can list the preconfigured families from the command line: - -.. code:: bash - - verdi data upf listfamilies - -Pick the one you configured earlier (or one of the ``SSSP`` families that -we provide) and link it to the calculation using the command: - -.. code:: python - - from aiida.orm.nodes.data.upf import get_pseudos_from_structure - builder.pseudos = get_pseudos_from_structure(structure, '') - -Print the content of the `pseudos` namespace, e.g. ``print(builder.pseudos)`` to see what the helper function created. - -Preparing and debugging input parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Finally, we need to specify a number of input parameters (i.e. plane wave cutoffs, convergence thresholds, etc.). -to launch the Quantum ESPRESSO calculation. -The structure of the parameter dictionary closely follows the structure of the `PWscf input file `_. - -Since these are often the parameters to tune in a calculation, -let's **introduce a few mistakes intentionally** -and use this part of the tutorial to learn how to debug problems. - -Define a set of input parameters for Quantum ESPRESSO, preparing a -dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, - } - -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, -except for an invalid key ``mickeymouse``. When Quantum ESPRESSO -receives an unrecognized key, it will stop. -By default, the AiiDA plugin will *not* validate your input and simply pass -it on to the code. - -Let's wrap the ``parameters_dict`` python dictionary in an AiiDA ``Dict`` node and see what happens. - -.. code:: python - - builder.parameters = Dict(dict=parameters_dict) - -Simulate submission -~~~~~~~~~~~~~~~~~~~ - -At this stage, you have created in memory (it's not yet stored in the -database) the input of the graph shown below. The outputs will -be created by the daemon later on. - -.. figure:: include/images/verdi_graph/si/graph-full.png - :alt: - -In order to check which input files AiiDA creates, -we can perform a *dry run* of the submission process. -Let's tell the builder that we want a dry run and that -we don't want to store the provenance of the dry run: - -.. code:: python - - builder.metadata.dry_run = True - builder.metadata.store_provenance = False - -It's time to run: - -.. code:: python - - from aiida.engine import run - run(builder) - -.. note:: - - Instead of using the builder, you can also simply pass the calculation class - as the first argument, followed by the inputs as keyword arguments, e.g.: - - .. code:: python - - run(PwCalculation, structure=structure, pseudos={'Si': pseudo_node}, ....) - - The builder is simply a convenience wrapper providing tab-completion in the shell and automatic help strings. - -This creates a folder of the form ``submit_test/[date]-0000[x]`` in the -current directory. In your second terminal: - - * open the input file ``aiida.in`` within this folder - * compare it to input data nodes you created earlier - * verify that the `pseudo` folder contains the needed pseudopotentials - * have a look at the submission script ``_aiidasubmit.sh`` - -.. note:: - - The files created by a dry run are only intended for inspection - and cannot be used to correct the inputs of your calculation. - -Submitting the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Up to now we've just been playing around and our calculation has been kept -in memory and not in the database. -Now that we have inspected the input files and convinced ourselves that -Quantum ESPRESSO will have all the information it needs to perform the -calculation, we will submit the calculation properly. -Doing so will make sure that all inputs are stored in the database, -will run and store the calculation and link the outputs to it. - -Let's revert the following values in our builder to their defaults: - -.. code:: python - - builder.metadata.dry_run = False - builder.metadata.store_provenance = True - -And then rely on the submit machinery of AiiDA, - -.. code:: python - - from aiida.engine import submit - calculation = submit(builder) - -As soon as you have executed these lines, the ``calculation`` variable contains a ``PwCalculation`` instance, already submitted to the daemon. - -.. note:: - - You may have noticed that we used ``submit`` here instead of ``run``. - The difference is that ``submit`` will hands over the calculation to the daemon running in the background, - while ``run`` will execute all tasks in the current shell. - - All processes in AiiDA (you will soon get to know more) can be "launched" using one of available functions: - - * run - * run_get_node - * run_get_pk - * submit - - which are explained in more detail in the `online documentation `_. - - -The calculation is now stored in the database and was assigned a "database primary key" or ``pk`` (``calculation.pk``) as well as a UUID (``calculation.uuid``). -See the :ref:`previous section <2019-aiida-identifiers>` for more details on these identifiers. - -Note that while AiiDA will prevent you from changing the content of stored nodes, -the concept of "extras" allows you to set extra attributes, e.g. as a way of -labelling nodes and providing information for querying. - -For example, let's add an extra attribute called ``element``, with value ``Si``: - -.. code:: python - - calculation.set_extra("element", "Si") - -In the mean time, after you submitted your calculation, the daemon -picked it up and started to: generate the input files, submit the calculation -to the queue, wait for it to run and finish, retrieve the output files, -parse them, store them in the database and set the state of the -calculation to ``Finished``. - -.. note:: - - If the daemon is not running, the calculation will remain in the - ``NEW`` state until you start the daemon. - -Checking the status of the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can check the calculation status from the command line: - -.. code:: bash - - verdi process list - -.. note:: - - Since you are running your DFT calculation directly on the VM, - ``verdi`` commands can be a bit slow until the calculation finishes. - -If you don't see any calculation in the -output, the calculation you submitted has already finished. - -By default, the command only prints calculations that are still active [#f2]_. -Let's also list your finished calculations (and limit those only -to the one created in the past day): - -.. code:: bash - - verdi process list -a -p1 - -as explained in the first section. - -Similar to the dry run, we can also inspect the input files of the *actual* -calculation: - -.. code:: bash - - verdi calcjob inputls -c - -for the ``pk_number`` of your calculation. This will show the -contents of the input directory (``-c`` prints directories in color). -Check the content of input files with - -.. code:: bash - - verdi calcjob inputcat | less - -Troubleshooting ---------------- - -Your calculation should end up in a FAILED state -(last column of ``verdi process list -a -p1``), and correspondingly the -error code near the "Finished" status of the State should be non-zero, - -.. code:: bash - - $ verdi process list -a -p1 - PK Created State Process label Process status - ---- --------- ---------------- --------------- ---------------- - 98 16h ago Finished [115] PwCalculation - ... - $ # Anything but [0] after the Finished state signals a failure - -This was expected, since we used an invalid key in the input parameters. -Situations like this happen in real life, so AiiDA provides -tools to trace back to the source of the problem and correct it. - -A first way to proceed is to inspect the output file of -PWscf. - -.. code:: bash - - verdi calcjob outputcat | less - -This might be enough to understand the reason why the calculation -failed. - -AiiDA provides further tools for troubleshooting in a more compact way. -For any calculation, both successful and failed, you can get a summary by: - -.. code:: bash - - $ verdi process show - Property Value - ------------- --------------------------------------------------- - type CalcJobNode - pk 98 - uuid 4c444afd-f6e2-4896-b9ae-8cb8a5ec75c5 - label PW test - description My first AiiDA calc with Quantum ESPRESSO on Si - ctime 2019-05-01 15:59:39.180018+00:00 - mtime 2019-05-01 16:01:44.870902+00:00 - process state Finished - exit status 115 - computer [1] localhost - - Inputs PK Type - ---------- ---- ------------- - pseudos - Si 50 UpfData - code 2 Code - kpoints 10 KpointsData - parameters 96 Dict - settings 97 Dict - structure 9 StructureData - - Outputs PK Type - ------------- ---- ---------- - remote_folder 99 RemoteData - retrieved 100 FolderData - - Log messages - --------------------------------------------- - There are 2 log messages for this calculation - Run 'verdi process report 98' to see them - -The last part of the output alerts you to the fact that there -are some log messages waiting for you, if you run -``verdi process report ``. - -Let's now correct our input parameters dictionary by leaving out the invalid -key and see if our calculation succeeds: - -.. code:: python - - parameters_dict = { - "CONTROL": { - "calculation": "scf", - }, - "SYSTEM": { - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } - } - builder.parameters = Dict(dict=parameters_dict) - calculation = submit(builder) - -If you have been using the separate script approach, modify -the script to remove the faulty input and run it again with: - -.. code:: bash - - verdi run test_pw.py - -Use ``verdi process list -a -p1`` to verify that -the calculation reaches the finished status, with exit code zero. - -Using the calculation results ------------------------------ - -Now you can access the results as you have seen earlier. For example, -note down the pk of the calculation so that you can load it in the -``verdi shell`` and check the total energy with the commands: - -.. code:: python - - calculation = load_node() - calculation.res.energy - -Besides writing input files, running the software for you, storing the output -files, and connecting it all together in your provenance graph, -many AiiDA plugins will parse the output of your code and make output values -of interest available through an output dictionary node (as depicted in the -graph above). -In the case of the ``aiida-quantumespresso`` plugin this output node -is available at ``calculation.outputs.output_parameters`` and you can access -all the available attributes (not only the energy) using: - -.. code:: python - - calculation.outputs.output_parameters.attributes - -While the name of this output dictionary node can be chosen by the plugin, -AiiDA provides the "results" shortcut ``calculation.res`` -that plugin developers can use to provide what they consider the result of the -calculation. - - -.. rubric:: Footnotes - -.. [#f1] In order to avoid duplication of KpointsData, you would first need to learn how to query the database, therefore we will ignore this issue for now. -.. [#f2] A process is considered active if it is either ``Created``, ``Running`` or ``Waiting``. If a process is no longer active, but terminated, it will have a state ``Finished``, ``Killed`` or ``Excepted``. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/first_taste.rst b/docs/pages/2019_IIT_Mandi_India/sections/first_taste.rst deleted file mode 100644 index 406ea80e..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/first_taste.rst +++ /dev/null @@ -1,331 +0,0 @@ -A first taste -============= - -Let's start with a quick demo of how AiiDA can make your life easier as a computational scientist. - -To get started, type ``workon aiida`` in your terminal to enter the *virtual environment* where AiiDA is installed. -You have entered the the virtual environment when the prompt starts with ``(aiida)``, e.g.:: - - (aiida) username@hostname:~$ - -.. note:: - - You need to retype ``workon aiida`` whenever you open a new terminal. - -We'll be using the ``verdi`` command-line interface, -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - -Here are some first tasks for you: - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on any ``verdi`` command, simply append the ``-h`` or ``--help`` flag: - - .. code:: bash - - verdi -h - -Getting help ------------- - -There are a number of helpful resources available to you for getting more information about AiiDA. -Please consider: - - * consulting the extensive `AiiDA documentation `_ - * asking in the `Slack channel of the tutorial `_ - * asking your neighbor - * asking a tutor - * opening a new issue on the `tutorial issue tracker `_ - -Importing a structure and running a calculation ------------------------------------------------ - -Let's download a structure from the `Crystallography Open Database `_ and import it into AiiDA: - -.. code:: bash - - wget http://crystallography.net/cod/9008565.cif - verdi data structure import ase 9008565.cif - - -Each piece of data in AiiDA gets a PK number (a "primary key") that identifies it in your database. -The PK is printed to screen by the ``verdi data structure import`` command. -**Mark down the PK for your structure and use it to replace the placeholders in what follows.** - -.. tip:: - - You can view the structure either `online `_ - or by executing the ``jmol 9008565.cif`` command on the virtual machine (or preferably on your local machine for better performance in case that ``jmol`` is installed). - -.. Let jason/jianxing test speed of SSH forwarding - potentially mention jupyter - -The following short python script sets up a self-consistent field calculation for the Quantum ESPRESSO code: - -.. literalinclude:: include/snippets/demo_calcjob.py - -Download the :download:`demo_calcjob.py ` script using ``wget`` to your working directory. -It contains a few placeholders for you to fill in: - - #. The VM already has a number of codes preconfigured. Use ``verdi code list`` to find the label for the "PW" code and use it in the script. - #. Replace the PK of the structure with the one you noted down earlier. - #. The VM already a number of pseudopotential families installed. - Replace the PP family name with the one for the "SSSP efficiency" library found via ``verdi data upf listfamilies``. - -Then submit the calculation using: - -.. code:: bash - - verdi run demo_calcjob.py - -From this point onwards, the AiiDA daemon will take care of your calculation: creating the necessary input files, running the calculation, and parsing its results. -It should take less than one minute to complete. - -Analyzing the outputs of a calculation --------------------------------------- - -Let's have a look how your calculation is doing: - -.. code:: bash - - verdi process list # shows only running processes - verdi process list --all # shows all processes - -Again, your calculation will get a PK, which you can use to get more information on it: - -.. code:: bash - - verdi process show - -As you can see, AiiDA has tracked all the inputs provided to the calculation, allowing you (or anyone else) to reproduce it later on. -AiiDA's record of a calculation is best displayed in the form of a provenance graph - -.. figure:: include/images/demo_calc.png - :width: 100% - - Provenance graph for a single Quantum ESPRESSO calculation. - -You can generate such a provenance graph for any calculation or data in AiiDA by running: - -.. code:: bash - - verdi node graph generate - -Try to reproduce the figure using the PK of your calculation. - -.. note:: - - By default, AiiDA uses UUIDs to label nodes in provenance graphs (more about UUIDs vs PKs later). - Try using the ``-h`` option to figure out how to switch to the PK identifier. - - -You might wonder what happened under the hood, e.g. where to find the actual input and output files of the calculation. -You will learn more about this later -- until then, here are a few useful to try: - -.. code:: bash - - verdi calcjob inputcat # shows the input file of the calculation - verdi calcjob outputcat # shows the output file of the calculation - verdi calcjob res # shows the parsed output - -A few questions you could answer using these commands (optional) - * How many atoms did the structure contain? - How many electrons? - * How many k-points were specified? How many k-points were actually computed? Why? - * How many SCF iterations were needed for convergence? - * How long did Quantum ESPRESSO actually run (wall time)? - -.. tip:: - - Use the ``grep`` command to filter the terminal output by keywords, e.g., ``verdi calc job res 175 | grep wall_time``. - -Moving to a different computer ------------------------------- - -The Quantum ESPRESSO calculation we just ran, was directly executed on the virtual machine. -This is fine for tests, but production calculations should typically be run on a remote compute cluster. -With AiiDA, moving a calculation from one computer to another means changing one line of code. - -For the purposes of this tutorial, you'll run on your neighbor's computer. -Ask your neighbor for the IP address of their VM. -Then, download the :download:`neighbor.yml ` setup template, replace the placeholder by the IP address and let AiiDA know about this computer by running: - -.. .. literalinclude:: include/configuration/neighbor.yml - -.. code:: bash - - verdi computer setup --config neighbor.yml - -.. note:: - - If you do not have a partner machine available, for example because you are completing this - tutorial at a later time, simply use "localhost" instead of the IP address. - -AiiDA is now aware of the existence of the computer but you'll still need to let AiiDA -know how to connect to it. -AiiDA does this via `SSH `_ keys. -Your tutorial VM already contains a private SSH key for connecting to the ``compute`` user of your neighbor's machine, -so all that is left is to configure it in AiiDA. - -Download the :download:`neighbor-config.yml ` configuration template and run: - -.. .. literalinclude:: include/configuration/neighbor-config.yml - -.. code:: bash - - verdi computer configure ssh neighbor --config neighbor-config.yml --non-interactive - -.. note:: Both ``verdi computer setup`` and ``verdi computer configure`` can be used interactively without - configuration files, which are provided here just to avoid typing errors. - -AiiDA should now have access to your neighbor's computer. Let's quickly test this: - -.. code:: bash - - verdi computer test neighbor - -Finally, let AiiDA know about the **code** we are going to use. -We've again prepared a template that looks as follows: - -.. literalinclude:: include/configuration/qe.yml - -Download the :download:`qe.yml ` code template and run: - -.. code:: bash - - verdi code setup --config qe.yml - verdi code list # note the label of the new code you just set up! - -Now modify the code label in your ``demo_calcjob.py`` script to the label of your new code and simply run another calculation using ``verdi run demo_calcjob.py``. - -To see what is going on, AiiDA provides a command that lets you jump to the folder of the directory of the calculation on the remote computer: - -.. code:: bash - - verdi process list --all # get PK of new calculation - verdi calcjob gotocomputer - -Have a look around. - * Do you recognize the different files? - * Have a look at the submission script ``_aiidasubmit.sh``. - Compare it to the submission script of your previous calculation. - What are the differences? - -From calculations to workflows ------------------------------- - -AiiDA can help you run individual calculations but it is really designed to help you run workflows that involve several calculations, while automatically keeping track of the provenance for full reproducibility. -As the final step, let's actually run such a workflow. - -Let's have a look at the workflows that are currently installed: - -.. code:: bash - - verdi plugin list aiida.workflows - - -The ``quantumespresso.pw.band_structure`` workflow from the `aiida-quantumespresso `_ plugin computes the electronic band structure for a given atomic structure. -Let AiiDA tell you which inputs it takes and which outputs it produces: - -.. code:: bash - - verdi plugin list aiida.workflows quantumespresso.pw.band_structure - -.. literalinclude:: include/snippets/demo_bands.py - -Download the :download:`demo_bands.py ` snippet, replace the PK, and run it using - -.. code:: bash - - verdi run demo_bands.py - -This workflow will: - - #. Determine the primitive cell of the input structure - #. Run a calculation on the primitive cell to relax both the cell and the atomic positions (``vc-relax``) - #. Refine the symmetry of the relaxed structure, and find a standardised primitive cell using SeeK-path_ - #. Run a self-consistent field calculation on the refined structure - #. Run a band structure calculation at fixed Kohn-Sham potential along a standard path between high-symmetry k-points determined by SeeK-path_ - -The workflow uses the PBE exchange-correlation functional with suitable pseudopotentials and energy cutoffs from the `SSSP library version 1.1 `_. - - -.. _SeeK-path: https://www.materialscloud.org/work/tools/seekpath - -.. K-point mesh is selected to have a minimum k-point density of 0.2 Å-1 - A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations - -The workflow should take ~10 minutes on your virtual machine. -You may notice that ``verdi process list`` now shows more than one entry. -While you wait for the workflow to complete, -let's start exploring its provenance. - -The full provenance graph obtained from ``verdi node graph generate`` will already be rather complex (you can try!), -so let's try browsing the provenance interactively instead. - -Start the AiiDA REST API: - -.. code:: bash - - verdi restapi - -and open the |provenance browser|. - -.. |provenance browser| raw:: html - - Materials Cloud provenance browser - -.. note:: - - In order for the provenance browser to work, you need to configure SSH to tunnel port 5000 from your VM to your local laptop (see here :ref:`2019_mandi_connect`). - - -The provenance browser is a Javascript application that connects to the AiiDA REST API. -Your data never leaves your computer. - -.. some general comment on importance of the graph? -.. a sentence on how to continue from here - -Browse your AiiDA database. - * Start by finding your structure in Data => StructureData - * Use the provenance browser to explore the steps of the ``PwBandStructureWorkChain`` - -.. note:: - - When perfoming calculations for a publication, you can export your provenance graph using ``verdi export create`` and upload it to the `Materials Cloud Archive `_, enabling your peers to explore the provenance of your calculations online. - -Once the workchain is finished, use ``verdi process show `` to inspect the ``PwBandStructureWorkChain`` and find the PK of its ``band_structure`` output. -Use this to produce a PDF of the band structure: - -.. code:: bash - - verdi data bands export --format mpl_pdf --output band_structure.pdf - - -.. figure:: include/images/si_bands.png - :width: 80% - - Band structure computed by the `PwBandStructure` workchain. - -.. note:: - The ``BandsData`` node does contain information about the Fermi energy, so the energy zero in your plot will be arbitrary. - You can produce a plot with the Fermi energy set to zero (as above) using the following code in a jupyter notebook: - - .. code:: ipython - - %aiida - %matplotlib inline - - scf_params = load_node() # REPLACE with PK of "scf_parameters" output - fermi_energy = scf_params.dict.fermi_energy - - bands = load_node() # REPLACE with PK of "band_structure" output - bands.show_mpl(y_origin=fermi_energy, plot_zero_axis=True) - - - -What next? ----------- - -You now have a first taste of the type of problems AiiDA tries to solve. -Now let's continue with the in-depth tutorial and learn more about the ``verdi``, ``verdi shell`` and ``python`` interfaces to AiiDA. -Please follow the link to the :ref:`2019_mandi_verdi_cli` section. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/code.yml b/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/code.yml deleted file mode 100644 index e9bdaad2..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/code.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" -on_computer: true -computer: "localhost" -remote_abs_path: "/usr/local/bin/pw.x" -prepend_text: "ulimit -s unlimited" -append_text: " " diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/computer.yml b/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/computer.yml deleted file mode 100644 index 01938930..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/computer.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -description: "localhost" -label: "localhost" -hostname: "localhost" -transport: local -scheduler: "direct" -work_dir: "/home/max/.aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/neighbor-config.yml b/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/neighbor-config.yml deleted file mode 100644 index ae7a0c26..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/neighbor-config.yml +++ /dev/null @@ -1,6 +0,0 @@ ---- -username: compute -key_filename: /home/max/.ssh/aiida-tutorial-compute.pem -timeout: 30 # timout for SSH before giving up -key_policy: AutoAddPolicy # add to known hosts automatically -safe_interval: 5 # Minimum time (s) between consecutive connections diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/neighbor.yml b/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/neighbor.yml deleted file mode 100644 index 9c311820..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/neighbor.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -label: neighbor -description: Your neighbor's VM. -hostname: IP_ADDRESS # << REPLACE -transport: ssh # connects via SSH -scheduler: slurm # use SLURM scheduler -work_dir: "/tmp/aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/qe.yml b/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/qe.yml deleted file mode 100644 index 4f9fc76d..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/configuration/qe.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" # input plugin for pw.x code -computer: "neighbor" -on_computer: true -remote_abs_path: "/usr/local/bin/pw.x" # path to executable -prepend_text: "ulimit -s unlimited" # set stacksize to unlimited -append_text: " " diff --git 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a/docs/pages/2019_IIT_Mandi_India/sections/include/images/process_functions.svg +++ /dev/null @@ -1,1996 +0,0 @@ - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - - - Float(0.98) - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - - CALL CALL - - - - RETURN - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - Float(0.98) - - - - - - - - - - - W1 - - - - - - CREATE RESCALED - - - - a) b) diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/images/qb_example_2.png b/docs/pages/2019_IIT_Mandi_India/sections/include/images/qb_example_2.png deleted file mode 100644 index 6a0c9117..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sections/include/images/qb_example_2.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/images/si_bands.png b/docs/pages/2019_IIT_Mandi_India/sections/include/images/si_bands.png deleted file mode 100644 index 80401fa9..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sections/include/images/si_bands.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/images/verdi_graph/batio3/graph-input.png 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/images/verdi_graph/si/graph-input.png b/docs/pages/2019_IIT_Mandi_India/sections/include/images/verdi_graph/si/graph-input.png deleted file mode 100644 index 306d45d9..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sections/include/images/verdi_graph/si/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/__init__.py b/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/demo_bands.py b/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/demo_bands.py deleted file mode 100644 index 57eaa03a..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/demo_bands.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Compute a band structure with Quantum ESPRESSO - -Uses the PwBandStructureWorkChain provided by aiida-quantumespresso. -""" -from aiida.engine import submit -PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure') - -results = submit( - PwBandStructureWorkChain, - code=Code.get_from_string("qe-6.4.1-pw@localhost"), - structure=load_node(), # REPLACE -) diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/demo_calcjob.py b/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/demo_calcjob.py deleted file mode 100644 index 42bd55ac..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/demo_calcjob.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Run SCF calculation with Quantum ESPRESSO""" -from aiida.orm.nodes.data.upf import get_pseudos_from_structure -from aiida.engine import submit - -code = Code.get_from_string('') # REPLACE -builder = code.get_builder() - -# Select structure -structure = load_node() # REPLACE -builder.structure = structure - -# Define calculation -parameters = { - 'CONTROL': { - 'calculation': 'scf', # self-consistent field - }, - 'SYSTEM': { - 'ecutwfc': 30., # wave function cutoff in Ry - 'ecutrho': 240., # density cutoff in Ry - }, -} -builder.parameters = Dict(dict=parameters) - -# Select pseudopotentials -builder.pseudos = get_pseudos_from_structure(structure, '') # REPLACE - -# Define K-point mesh in reciprocal space -KpointsData = DataFactory('array.kpoints') -kpoints = KpointsData() -kpoints.set_kpoints_mesh([4,4,4]) -builder.kpoints = kpoints - -# Set resources -builder.metadata.options.resources = {'num_machines': 1} - -# Submit the job -calcjob = submit(builder) -print('Submitted CalcJob with PK=' + str(calcjob.pk)) diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_create_diamond_fcc.py b/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_create_diamond_fcc.py deleted file mode 100644 index 0ad3d483..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_create_diamond_fcc.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import absolute_import -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - try: - symbol = element.value - except AttributeError: - symbol = str(element) - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_create_rescaled.py b/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_create_rescaled.py deleted file mode 100644 index c7d0a617..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_create_rescaled.py +++ /dev/null @@ -1,11 +0,0 @@ -from __future__ import absolute_import -from aiida.engine import workfunction - - -@workfunction -def create_rescaled(element, scale): - """Workfunction to create and immediately rescale a crystal structure of a given element.""" - structure = create_diamond_fcc(element) - rescaled = rescale(structure, scale) - - return rescaled diff --git a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_rescale.py b/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_rescale.py deleted file mode 100644 index 99761a77..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/include/snippets/workflows_rescale.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import absolute_import -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_IIT_Mandi_India/sections/querybuilder.rst b/docs/pages/2019_IIT_Mandi_India/sections/querybuilder.rst deleted file mode 100644 index bf50bdb3..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/querybuilder.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _2019_mandi_querybuilder: - -Queries in AiiDA: The Querybuilder -================================== - -This part of the tutorial is provided only in interactive mode through a -Jupyter notebook, which you will be able to run in your browser. -For instructions on starting the Jupyter server, please refer to the -:ref:`setup section`. Once the server is running, -:download:`download this tutorial notebook <../notebooks/querybuilder-tutorial.ipynb>` -and open it in Jupyter. For instructions on downloading these files on -a machine through which you are connected through SSH, refer to -:ref:`this section`. - -The notebook will show you how the ``QueryBuilder`` can be used to query your -database for specific data. It will demonstrate certain concepts and then ask -you to use those to perform certain queries on your own database. Some of these -question cells will have partial solutions that you will have to complete. - -Once you have finished the notebook, you can download a -:download:`notebook with the solutions <../notebooks/querybuilder-solutions.ipynb>` -but try not to use them at first! - -See below for a rendered version of the notebook: - -.. toctree:: - - QueryBuilder Notebook <../notebooks/querybuilder-tutorial.ipynb> diff --git a/docs/pages/2019_IIT_Mandi_India/sections/setup.rst b/docs/pages/2019_IIT_Mandi_India/sections/setup.rst deleted file mode 100644 index dd198a79..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/setup.rst +++ /dev/null @@ -1,175 +0,0 @@ -Getting set up -============== - -.. note:: If you are using the Quantum Mobile virtual machine locally inside VirtualBox, you can skip this section. - -.. _2019_mandi_connect: - -Connect to your virtual machine -------------------------------- - -You should each have received from the instructors: - -- an IP address -- a private SSH key ``aiida_tutorial_NUM`` -- a public SSH key ``aiida_tutorial_NUM.pub`` - -The steps below explain how to use these in order to connect to your personal virtual machine (VM) on Amazon Elastic Cloud 2 using the `Secure Shell `_ protocol. -The software on this VM is based on `Quantum Mobile `_ and already includes a pre-configured AiiDA installation as well as some test data for the tutorial. - -.. note:: - - If you decide to work in pairs, one of you should forward their credentials to the other, and you should both use the same IP address and ssh key. - Since you will be sharing the VM and user account, be careful not to delete the work of your colleague. - -Linux and MacOS -~~~~~~~~~~~~~~~ - -It's recommended for you to place the ssh key you received in a folder dedicated to your ssh configuration, to do so: - -- If not already present, create a ``.ssh`` directory in your home (``mkdir ~/.ssh``), and set its permissions: ``chmod 700 ~/.ssh`` -- Copy the two keys ``aiida_tutorial_NUM`` and ``aiida_tutorial_NUM.pub`` - in the ``~/.ssh`` directory -- Set the correct permissions on the private key: - ``chmod 600 ~/.ssh/aiida_tutorial_NUM``. You can check check with ``ls -l`` - that the permissions of this file are now ``-rw-------``. - -After the ssh key files are in place, add the following block to your ``~/.ssh/config`` file: - -.. code:: bash - - Host aiidatutorial - Hostname IP_ADDRESS - User max - IdentityFile ~/.ssh/aiida_tutorial_NUM - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - ServerAliveInterval 120 - -replacing the IP address field (``IP_ADDRESS``) and the ``NUM`` with what you received earlier. - -Afterwards you can connect to the server using this simple command: - -.. code:: console - - ssh aiidatutorial - -.. note:: - - Here's a copy-paste ready command for you to use directly with zero configuration: - - .. code:: console - - ssh \ - -i ~/.ssh/aiida-tutorial-max.pem \ - -L 8888:localhost:8888 \ - -L 5000:localhost:5000 \ - -o ServerAliveInterval=120 \ - -X -C \ - max@IP_ADDRESS - -.. note:: - - On MacOS you need to install `XQuartz `_ in order to use X-forwarding. - -Windows -~~~~~~~ - -If you're running Windows 10, you may want to consider `installing the Windows Subsystem for Linux `_ (and then follow the instructions above). -Alternatively: - -- Install the `PuTTY SSH client `_. - -- Run PuTTYGen - - - Load the ``aiida_tutorial_NN`` private key (button "Load"). - You may need to choose to show "All files (*.*)", and select the file without any extension (Type: File). - - In the same window, click on "Save private Key", and save the key with the name ``aiida_tutorial_NN.ppk`` (don't specify a password). - -- Run Pageant - - - It will add a new icon near the clock, in the bottom right of your screen. - - Right click on this Pageant icon, and click on “View Keys”. - - Click on "Add key" and select the ``aiida_tutorial_NN.ppk`` you saved a few steps above. - -- Run PuTTY - - - Put the given IP address as hostname, type ``aiidatutorial`` in "Saved Sessions" and click "Save". - - Go to Connection > Data and put ``max`` as autologin username. - - Go to Connection > SSH > Tunnels, type ``8888`` in the "Source Port" box, type ``localhost:8888`` in "Destination" and click "Add". - - Repeat the previous step for port ``5000`` instead of ``8888``. - - Go back to the "Session" screen, select "aiidatutorial" and click "Save". - - Finally, click "Open" (and click "Yes" on the putty security alert to add the VM to your known hosts). - You should be redirected to a bash terminal on the virtual machine. - -.. note:: Next time you open PuTTY, select ``aiidatutorial`` and click "Load" before clicking "Open". - -In order to enable X-forwarding: - -- Install the `Xming X Server for Windows `_. - -- Configure PuTTy as described in the `Xming wiki `_. - -.. _2019_mandi_setup_jupyter: - -Start jupyter -------------- - -Once connected to your virtual machine, type in the remote terminal - -.. code:: bash - - workon aiida - -This will enable the virtual environment in which AiiDA is installed, allowing you to use AiiDA. -Now type in the same shell: - -.. code:: bash - - jupyter notebook --no-browser - -This will run a server with a web application called ``jupyter``, which is used to create interactive python notebooks. -In order to connect to the jupyter notebook server: - - - Copy the URL that has been printed to the terminal (similar to ``http://localhost:8888/?token=2a3ba37cd1...``). - - Open a web browser **on your laptop** and paste the URL. - - You will see a list of folders on your personal VM. - -While keeping the first ``ssh`` connection running, open a second ``ssh`` connection in a separate terminal and execute ``workon aiida`` there as well. -We will use the second terminal to directly interact with the virtual machine on the command line, while we use the first one to only serve the jupyter notebook. - -.. note:: - - You can safely ignore all warnings related to port forwarding when opening a second ssh connection. - Those are caused by the fact that the ports are now already in use which in this context is perfectly fine. - - -.. _2019_mandi_setup_downloading_files: - -Downloading files ------------------ - -Throughout this tutorial, you will encounter links to download python scripts, jupyter notebooks and more. -These files should be downloaded to the environment/working directory you use to run the tutorial. -In particular, when running the tutorial on a Linux VM, copy the link address and download the files to the machine using ``wget`` in the terminal: - -.. code:: bash - - wget - -where you replace ```` with the actual HTTPS URL copied from the tutorial text in your browser. -This will download the file to the current directory. - - -Troubleshooting ---------------- - -- If you encounter errors such as ``ImportError: No module named aiida`` or ``No command ’verdi’ found``, double check that you have loaded the virtual environment with ``workon aiida`` before launching ``python``, ``ipython`` or the ``jupyter`` notebook server. - Your command line prompt should start with ``(aiida)``, e.g., ``(aiida) max@workhorse:~$``. - -- If your browser cannot connect to the jupyter notebook server, check that you have correctly configured SSH tunneling/forwarding as described above. - Keep in mind that you need to start the jupyter server from the terminal connected to the VM, while the web browser should be opened locally on your laptop. - -- See the `jupyter notebook documentation `_ for compatibility of jupyter with various web browsers. diff --git a/docs/pages/2019_IIT_Mandi_India/sections/verdi_cmdline.rst b/docs/pages/2019_IIT_Mandi_India/sections/verdi_cmdline.rst deleted file mode 100644 index 77c5c574..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/verdi_cmdline.rst +++ /dev/null @@ -1,533 +0,0 @@ -.. _2019_mandi_verdi_cli: - -Verdi command line -================== - -This part of the tutorial will familiarize you with the ``verdi`` command-line interface (CLI), -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``-h/--help`` flag, e.g.: - - .. code:: bash - - verdi quicksetup -h - -More details on ``verdi`` can be found in the `online documentation `_. - - -.. _2019_mandi_setup_verdi_quicksetup: - -Setting up a profile --------------------- - -After installing AiiDA, the first step is to create a "profile". -Typically, you would be using one profile per independent research project. - -The easiest way of setting up a new profile is through ``verdi quicksetup``. -Let's set up a new profile that we will use throughout this tutorial: - -.. code:: bash - - verdi quicksetup - -This will prompt you for some information, such as the name of the profile, your name, etc. -The information about you as a user will be associated with all the data that you create in AiiDA -and is important for attribution, when you start sharing your data with others. -After you have answered all the questions, a new profile will be created, along -with the required database and repository. - -.. note:: - - ``verdi quicksetup`` is a user-friendly wrapper around the ``verdi setup`` command that provides more control over the profile setup. - As explained in `the documentation `_, ``verdi setup`` expects certain external resources, such as the database to already have been pre-configured. - ``verdi quicksetup`` will try to do this for you, but may not be successful in certain environments. - -To see this profile, and any others that may have been configured, run: - -.. code:: bash - - verdi profile list - - Info: configuration folder: /home/max/.aiida - * generic - quicksetup - -Each line, ``generic`` and ``quicksetup`` in this example, corresponds to a profile. -The one marked with an asterisk is the "default" profile, meaning that any ``verdi`` command that you execute will be applied to that profile. - -.. note:: - - The output you get may differ. - The ``generic`` profile is pre-configured on the virtual machine and built only for the first part of the tutorial. - -Let's change the default profile to the newly created ``quicksetup`` for the rest of the tutorial: - -.. code:: bash - - verdi profile setdefault quicksetup - -From now on, all ``verdi`` commands will apply to the ``quicksetup`` profile. - -.. note:: - - To quickly perform a single command on a profile that is not the default, use the ``-p/--profile`` option: - For example, ``verdi -p generic code list`` will display the codes for the ``generic`` profile, despite it not being the current default profile. - - -Importing data --------------- - -Before we start running calculations ourselves, we are going to look at an AiiDA database already created by someone else. -Let's import one from the web: - -.. code:: bash - - verdi import https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2019_05_perovskites_v0.3.aiida - -Contrary to most databases, AiiDA databases contain not only *results* of calculations but also their inputs and information on how a particular result was obtained. -This information, the *data provenance*, is stored in the form of a *directed acyclic graph* (DAG). -In the following, we are going to introduce you to different ways of browsing this graph and will ask you to find out some information regarding the database you just imported. - -.. _2019_mandi_aiidagraph: - -Your first AiiDA graph ----------------------- - -:numref:`2019_mandi_fig_graph_input_only` shows a typcial example of a calculation represented in an AiiDA graph. -Have a look to the figure and its caption before moving on. - -.. _2019_mandi_fig_graph_input_only: -.. figure:: include/images/verdi_graph/batio3/graph-input.png - :width: 100% - - Graph with all inputs (data, circles; and code, diamond) to the Quantum ESPRESSO calculation (square) that you will create in the :ref:`calculations` section of this tutorial. - -.. _2019_mandi_fig_graph: -.. figure:: include/images/verdi_graph/batio3/graph-full.png - :width: 100% - - Same as :numref:`2019_mandi_fig_graph_input_only`, but also with the outputs that the engine will create and connect automatically. - The ``RemoteData`` node is created during submission and can be thought as a symbolic link to the remote folder in which the calculation runs on the cluster. - The other nodes are created when the calculation has finished, after retrieval and parsing. - The node with linkname 'retrieved' contains the raw output files stored in the AiiDA repository; all other nodes are added by the parser. - Additional nodes (symbolized in gray) can be added by the parser (e.g. an output ``StructureData`` if you performed a relaxation calculation, a ``TrajectoryData`` for molecular dynamics etc.). - -:numref:`2019_mandi_fig_graph_input_only` was created manually but you can generate a similar graph automatically by passing the **identifier** of a calculation node to ``verdi node graph generate ``. -Identifiers in AiiDA come in three forms: - - * "Primary Key" (PK): An integer, e.g. ``723``, that identifies your entity within your database (automatically assigned) - * `Universally Unique Identifier `_ (UUID): A string, e.g. ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` that identifies your entity globally (automatically assigned) - * Label: A human-readable string, e.g. ``test_qe_calculation`` (manually assigned) - -Any ``verdi`` command that expects an identifier will accept a PK, a UUID or a label (although not all entities have a label by default). -While PKs are often shorter than UUIDs and can be easier to remember, they are only unique within your database. -**Whenever you intend to share your data with others, use UUIDs to refer to nodes.** - -.. note:: - For UUIDs, it is sufficient to specify a subset (starting at the beginning) as long as it can already be uniquely resolved. - For more information on identifiers in ``verdi`` and AiiDA in general, see the `documentation `_. - -For the remainder of this section, fields enclosed in angular brackets, such as ````, are placeholders that you should replace before executing the command. -With that in mind, let's generate a graph for the calculation node with UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``: - -.. code:: bash - - verdi node graph generate - -This command will create a file with the naming scheme ``.dot.pdf`` that can be viewed with any PDF document viewer. -You can open this file on the Amazon machine by using ``evince`` or, if the ssh connection is too slow, copy it via ``scp`` to your local machine. -To do so, if you are using Linux/Mac OS X, you can type in your *local* machine: - -.. code:: bash - - scp aiidatutorial: - -and then open the file. -Alternatively, you can use graphical software to achieve the same, for instance: WinSCP on Windows, Cyberduck on the Mac, or the 'Connect to server' option in the main menu after clicking on the desktop for Ubuntu. - - -The provenance browser ----------------------- - -While the ``verdi`` CLI provides full access to the data underlying the provenance graph, a more intuitive tool for browsing AiiDA graphs is the interactive provenance browser available on `Materials Cloud `__. - -In order to use it, we first need to start the `AiiDA REST API `_: - -.. code:: bash - - verdi restapi - * Serving Flask app "aiida.restapi.run_api" (lazy loading) - * Environment: production - WARNING: Do not use the development server in a production environment. - Use a production WSGI server instead. - * Debug mode: off - * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) - -Now you can connect the provenance browser to your local REST API: - -- Open the |provenance_browser| on your laptop -- In the form, paste the (local) URL ``http://127.0.0.1:5000/api/v3`` - of our REST API -- Click "GO!" - -.. |provenance_browser| raw:: html - - provenance explorer - -Once the provenance browser javascript application has been loaded by your browser, it is communicating directly with the REST API and your data never leaves your computer. - -.. note:: - In order for this to work on your laptop, while the REST API is running on the virtual machine, we've enabled SSH tunneling for port ``5000`` in :ref:`2019_mandi_connect`. - -Start by clicking on the Details of a ``CalcJobNode`` and use the graph explorer to complete the exercise below. -If you ever get lost, just go to the "Details" tab, enter ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` and click on the "GO" button. - -.. admonition:: Exercise - - Use the provenance browser in order to figure out: - - - When was the calculation run and who run it? - - Was it a serial or a parallel calculation? How many MPI processes were used? - - What inputs did the calculation take? - - What code was used and what was the name of the executable? - - How many calculations were performed using this code? - - -Processes ---------- - -Anything that 'runs' in AiiDA, be it calculations or workflows, is considered a ``Process``. -To get a list of currently running processes, use: - -.. code:: bash - - verdi process list - -.. note:: - - The first time you run this command, it might take a few seconds. - Subsequent calls will be faster. - -which should be empty: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - - Total results: 0 - - Info: last time an entry changed state: never - -Let's see whether there are any *finished* processes in the database by passing the ``-S/--process-state`` flag: - -.. code:: bash - - verdi process list -S finished - -This command will list all the processes that have a process state ``Finished`` and should look something like: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - 1178 1653D ago PwCalculaton ⏹ Finished [0] - 1953 1653D ago PwCalculaton ⏹ Finished [0] - 1734 1653D ago PwCalculaton ⏹ Finished [0] - 336 1653D ago PwCalculaton ⏹ Finished [0] - 1056 1653D ago PwCalculaton ⏹ Finished [0] - 1369 1653D ago PwCalculaton ⏹ Finished [0] - - Total results: 6 - - Info: last time an entry changed state: never - -Processes can be in any of the following states: - - * ``Created`` - * ``Waiting`` - * ``Running`` - * ``Finished`` - * ``Excepted`` - * ``Killed`` - -The first three states are 'active' states, meaning the process is not done yet, and the last three are 'terminal' states. -Once a process is in a terminal state, it will never become active again. -The `official documentation `_ contains more details on process states. - -In order to list processes of *all* states, use the ``-a/--all`` flag: - -.. code:: bash - - verdi process list -a - -This command will list all the processes that have *ever* been launched. -As your database will grow, so will the output of this command. -To limit the number of results, you can use the ``-p/--past-days `` option, that will only show processes that were created ``NUM`` days ago. -For example, this lists all processes launched since yesterday: - -.. code:: bash - - verdi process list -a -p1 - -.. _2019-aiida-identifiers: - -Each row of the output identifies a process with some basic information about its status. -For a more detailed list of properties, you can use ``verdi process show``, but to address any specific process, you need an identifier for it. - -Let's revisit the process with the UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``, this time using the CLI: - -.. code:: bash - - verdi process show ce81c420-7751-48f6-af8e-eb7c6a30cec - -Producing the output: - -.. code:: bash - - Property Value - ------------- ------------------------------------ - type CalcJobNode - pk 828 - uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 - label - description - ctime 2014-10-27 17:51:21.781045+00:00 - mtime 2019-05-09 14:10:09.307986+00:00 - process state Finished - exit status 0 - computer [1] daint - - Inputs PK Type - ---------- ---- ------------- - pseudos - Ba 611 UpfData - O 661 UpfData - Ti 989 UpfData - code 825 Code - kpoints 811 KpointsData - parameters 829 Dict - settings 813 Dict - structure 27 StructureData - - Outputs PK Type - ----------------------- ---- ------------- - output_kpoints 1894 KpointsData - output_parameters 62 Dict - output_structure 61 StructureData - output_trajectory_array 63 ArrayData - remote_folder 357 RemoteData - retrieved 60 FolderData - -You can use the PKs shown for the inputs and outputs to get more information about those nodes. - -.. warning:: - - Since the inputs and outputs are ``Data`` nodes, not ``Process`` nodes, use ``verdi node show`` instead. - - -Dict and CalcJobNode -~~~~~~~~~~~~~~~~~~~~ - -Let's investigate some of the nodes appearing in the graph. -From the inputs of the process, let's choose the node of type ``Dict`` with input link name ``parameters`` and type in the terminal: - -.. code:: bash - - verdi data dict show - -where ```` is the PK of the node. - -A ``Dict`` contains a dictionary (i.e. key–value pairs), stored in the database in a format ready to be queried. -We will learn how to run queries later on in this tutorial. -The command above will print the content dictionary, containing the parameters used to define the input file for the calculation. -You can compare the dictionary with the content of the raw input file to Quantum ESPRESSO (that was generated by AiiDA) via the command: - -.. code:: bash - - verdi calcjob inputcat - -where you provide the identifier of the calculation node. -Check the consistency of the parameters written in the input file and those stored in the ``Dict`` node. -Even if you don't know the meaning of the input flags of a Quantum ESPRESSO calculation, you should be able to see how the input dictionary has been converted to Fortran namelists. - -The previous command just printed the content of the 'default' input file ``aiida.in``. -To see a list of all the files used to run a calculation (input file, submission script, etc.) instead type: - -.. code:: bash - - verdi calcjob inputls - -Adding a ``--color`` flag allows you to easily distinguish files from folders by a different coloring. -Once you know the name of the file you want to visualize, you can call the ``verdi calcjob inputcat [PATH]`` command specifying the path. -For instance, to see the submission script, you can do: - -.. code:: bash - - verdi calcjob inputcat _aiidasubmit.sh - -StructureData -~~~~~~~~~~~~~ - -Now let us focus on ``StructureData`` objects, which represent a crystal structure. -We can consider for instance the input structure to the calculation we were considering before (it should have the UUID ``3a4b1270``). -Such objects can be inspected interactively by means of an atomic viewer such as the one provided by ``ase``. -AiiDA however supports several other viewers such as ``xcrysden``, ``jmol``, and ``vmd``. -Type in the terminal: - -.. code:: bash - - verdi data structure show --format ase - -to show the selected structure, although it will take a few seconds to appear. -You should be able to rotate the view with the right mouse button. - -.. note:: - - If you receive some errors, make sure you started your SSH connection with the ``-X`` or ``-Y`` flag. - -Alternatively -- especially if viewing the structure over SSH is too slow -- you can export the content of a structure node in various formats, such as ``xyz`` or ``xsf``, and then view them on your local machine. -For example, to view the file with the ``xcrysden`` application, first export the structure node in the ``xsf`` format: - -.. code:: bash - - # verdi data structure export --format xsf > .xsf - verdi data structure export --format xsf 254e5a86 > 254e5a86.xsf - -Then **copy** the ``xsf``-file to your local machine and open it with the ``xcrysden`` application: - -.. code:: bash - - xcrysden --xsf 254e5a86.xsf - -.. note:: - - You will need to have the ``xcrysden`` application installed on your local machine for this to work. - -Codes and computers -~~~~~~~~~~~~~~~~~~~ - -Let us focus now on the nodes of type ``Code``. -A code represents (in the database) the actual executable used to run the calculation. -Find the identifier of such a node in the graph and type: - -.. code:: bash - - verdi code show - -The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc. -To show a list of all available codes type: - -.. code:: bash - - verdi code list - -If you want to show all codes, including hidden ones and those created by other users, use ``verdi code list -a -A``. -Now, among the entries of the output you should also find the code just shown. - -Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command: - -.. code:: bash - - verdi computer list -a - -The ``-a`` flag shows all computers, also the one imported in your database but that you did not configure, i.e. to which you don't have access. -Details about each computer can be obtained by the command: - -.. code:: bash - - verdi computer show - -Now you have the tools to answer the question: what is the scheduler installed on the computer where the calculations of the graph have run? - -Calculation results -~~~~~~~~~~~~~~~~~~~ - -The results of a calculation can be accessed directly from the calculation node. -Type in the terminal: - -.. code:: bash - - verdi calcjob res - -which will print the output dictionary of the 'scalar' results parsed by AiiDA at the end of the calculation. -Note that this is actually a shortcut for: - -.. code:: bash - - verdi data dict show - -where ``IDENTIFIER`` refers to the ``Dict`` node attached as an output of the calculation node, with link name ``output_parameters``. -By looking at the output of the command, what is the Fermi energy of the calculation with UUID ``ce81c420``? - -Similarly to what you did for the calculation inputs, you can access the output files via the commands: - -.. code:: bash - - verdi calcjob outputls - -and - -.. code:: bash - - verdi calcjob outputcat - -Use the latter to verify that the Fermi energy that you have found in the last step has been extracted correctly from the output file - -.. hint:: - - Filter the lines containing the string 'Fermi', e.g. using ``grep``, to isolate the relevant lines. - -The results of calculations are stored in two ways: ``Dict`` objects are stored in the database, which makes querying them very convenient, whereas ``ArrayData`` objects are stored on the disk. -Once more, use the command ``verdi data array show `` to determine the Fermi energy obtained from calculation with the UUID ``ce81c420``. -This time you will need to use the identifier of the output ``ArrayData`` of the calculation, with link name ``output_trajectory_array``. -As you might have realized the difference now is that the whole series of values of the Fermi energy calculated after each relax/vc-relax step are stored. -The choice of what to store in ``Dict`` and ``ArrayData`` nodes is made by the parser of ``pw.x`` implemented in the ``aiida-quantumespresso`` plugin. - -*(Optional section)* Comments -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -AiiDA offers the possibility to attach comments to a any node, in order to be able to remember more easily its details. -Node with UUID prefix ``ce81c420`` should have no comments, but you can add a very instructive one by typing in the terminal: - -.. code:: bash - - verdi node comment add "vc-relax of a BaTiO3 done with QE pw.x" -N - -Now, if you ask for a list of all comments associated to that calculation by typing: - -.. code:: bash - - verdi node comment show - -the comment that you just added will appear together with some useful information such as its creator and creation date. -We let you play with the other options of ``verdi comment`` command to learn how to update or remove comments. - -AiiDA groups of calculations ----------------------------- - -In AiiDA, calculations (and more generally nodes) can be organized in groups, which are particularly useful to assign a set of calculations or data to a common project. -This allows you to have quick access to a whole set of calculations with no need for tedious browsing of the database or writing complex scripts for retrieving the desired nodes. -Type in the terminal: - -.. code:: bash - - verdi group list -a -A - -to show a list of **all** groups that exist in the database. -Choose the PK of the group named ``tutorial_pbesol`` and look at the calculations that it contains by typing: - -.. code:: bash - - verdi group show - -In this case, we have used the name of the group to organize calculations according to the pseudopotential that has been used to perform them. -Among the rows printed by the last command you will be able to find the calculation we have been inspecting until now. - -If instead you wanted to know all the groups to which a specific node belongs, use the ``-N/--node`` option: - -.. code:: bash - - verdi group list -N diff --git a/docs/pages/2019_IIT_Mandi_India/sections/verdi_shell.rst b/docs/pages/2019_IIT_Mandi_India/sections/verdi_shell.rst deleted file mode 100644 index c2d74d6f..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/verdi_shell.rst +++ /dev/null @@ -1,393 +0,0 @@ -Verdi shell and AiiDA objects -============================= - -In this section we will use an interactive IPython environment with all the basic AiiDA classes already loaded. -We propose two realizations of such a tool. -The first consists of a special IPython shell where all the AiiDA classes, methods and functions are accessible. -Type in the terminal - -.. code:: bash - - verdi shell - -For all the everyday AiiDA-based operations, i.e. creating, querying, and using AiiDA objects, the ``verdi shell`` is probably the best tool. -In this case, we suggest that you use two terminals, one for the ``verdi shell`` and one to execute bash commands. - - -The second option is based on Jupyter notebooks and is probably most suitable to the purposes of our tutorial. -Go to the browser where you have opened ``jupyter`` and click ``New`` → ``Python 3`` (top right corner). -This will open an IPython-based Jupyter notebook, made of cells in which you can type portions of python code. -The code will not be executed until you press ``Shift+Enter`` from within a cell. -Type in the first cell - -.. code:: ipython - - %aiida - -and execute it. -This will set exactly the same environment as the ``verdi shell``. -The notebook will be automatically saved upon any modification and when you think you are done, you can export your notebook in many formats by going to ``File`` → ``Download as``. -We suggest you to have a look at the drop-down menus ``Insert`` and ``Cell`` where you will find the main commands to manage the cells of your notebook. - -.. note:: - - The ``verdi shell`` and Jupyter notebook are completely equivalent. - Use either according to your personal preference. - -You will still sometimes need to type command-line instructions in ``bash`` in the first terminal you opened. -To differentiate these from the commands to be typed in the ``verdi shell``, the latter will be marked in this document by a green background, like: - -.. code:: python - - some verdi shell command - -while command-line instructions in ``bash`` to be typed into a terminal will be written with a blue background: - -.. code:: bash - - some bash command - -Alternatively, to avoid changing terminal, you can execute ``bash`` commands within the ``verdi shell`` or the notebook by adding an exclamation mark before the command itself: - -.. code:: ipython - - !some bash command - -.. note:: - - The background colors of the code sections may be displayed differently by different browsers. - For the color scheme mentioned here, we recommend using Google Chrome. - -.. _2019_mandi_loadnode: - -Loading a node --------------- - -Most AiiDA objects are represented by nodes, identified in the database by its ``PK`` (an integer). -You can access a node using the following command in the shell: - -.. code:: python - - node = load_node(PK) - -Load a node using one of the calculation ``PK`` s visible in the graph you displayed in the previous section of the tutorial. -Then get the energy of the calculation with the command - -.. code:: python - - node.res.energy - -You can also type - -.. code:: python - - node.res. - -and then press ``TAB`` to see all the available output results of the calculation. - -Loading specific kinds of nodes -------------------------------- - -Pseudopotentials -~~~~~~~~~~~~~~~~ - -From the graph you generated in section :ref:`2019_mandi_aiidagraph`, find the ``UUID`` of the pseudopotential files (``UpfData``). -Load one of them and show what elements it corresponds to by typing: - -.. code:: python - - upf = load_node("") - upf.element - -All methods of ``UpfData`` are accessible by typing ``upf.`` and then pressing ``TAB``. - -k-points -~~~~~~~~ - -A set of k-points in the Brillouin zone is represented by an instance of the ``KpointsData`` class. -Choose one from the graph of produced in section :ref:`2019_mandi_aiidagraph`, load it as ``kpoints`` and inspect its content: - -.. code:: python - - kpoints.get_kpoints_mesh() - -Then get the full (explicit) list of k-points belonging to this mesh using - -.. code:: python - - kpoints.get_kpoints_mesh(print_list=True) - -If this throws an ``AttributeError``, it means that the kpoints instance does not represent a regular mesh but rather a list of k-points defined by their crystal coordinates (typically used when plotting a band structure). -In this case, get the list of k-points coordinates using - -.. code:: python - - kpoints.get_kpoints() - -Conversely, if the `KpointsData` node `does` actually represent a mesh, this method is the one, that when called, will throw an ``AttributeError``. - -If you prefer Cartesian (rather than crystal) coordinates, type - -.. code:: python - - kpoints.get_kpoints(cartesian=True) - -For later use in this tutorial, let us try now to create a kpoints instance, to describe a regular (2 x 2 x 2) mesh of k-points, centered at the Gamma point (i.e. without offset). -This can be done with the following commands: - -.. code:: python - - KpointsData = DataFactory('array.kpoints') - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh] * 3) - kpoints.store() - -This function loads the appropriate class defined in a string (here ``array.kpoints``). -Therefore, ``KpointsData`` is not a class instance, but the kpoints class itself! - -While it is also possible to import ``KpointsData`` directly, it is recommended to use the ``DataFactory`` function instead, as this is more future-proof: -even if the import path of the class changes in the future, its entry point string (``array.kpoints``) will remain stable. - - -Parameters -~~~~~~~~~~ - -Dictionaries with various parameters are represented in AiiDA by ``Dict`` nodes. -Get the PK and load the input parameters of a calculation in the graph produced in section :ref:`2019_mandi_aiidagraph`. -Then display its content by typing - -.. code:: python - - params = load_node('') - YOUR_DICT = params.get_dict() - YOUR_DICT - -Modify the python dictionary ``YOUR_DICT`` so that the wave-function cutoff is now set to 20 Ry. -Note that you cannot modify an object already stored in the database. -To write the modified dictionary to the database, create a new object of class ``Dict``: - -.. code:: python - - Dict = DataFactory('dict') - new_params = Dict(dict=YOUR_DICT) - -where ``YOUR_DICT`` is the modified python dictionary. -Note that ``new_params`` is not yet stored in the database. -In fact, typing ``new_params`` in the verdi shell will print a string notifying you of its 'unstored' status. -Let's finish by storing the ``new_params`` dictionary node in the datbase: - -.. code:: python - - new_params.store() - -Structures -~~~~~~~~~~ - -Find a structure in the graph you generated in section :ref:`2019_mandi_aiidagraph` and load it. -Display its chemical formula, atomic positions and species using - -.. code:: python - - structure.get_formula() - structure.sites - -where ``structure`` is the structure you loaded. -If you are familiar with ASE and PYMATGEN, you can convert this structure to those formats by typing - -.. code:: python - - structure.get_ase() - structure.get_pymatgen() - -Let’s try now to define a new structure to study, specifically a silicon crystal. -In the ``verdi shell``, define a cubic unit cell as a 3 x 3 matrix, with lattice parameter `a`\ :sub:`lat`\ `= 5.4` Å: - -.. code:: python - - alat = 5.4 - the_cell = [[alat/2, alat/2, 0.], [alat/2, 0., alat/2], [0., alat/2, alat/2]] - -.. note:: - - Default units for crystal structure cell and coordinates in AiiDA are Å (Ångström). - -Structures in AiiDA are instances of the class ``StructureData``: load it in the verdi shell - -.. code:: python - - StructureData = DataFactory('structure') - -Now, initialize the class instance (i.e. the actual structure we want to study) by the command - -.. code:: python - - structure = StructureData(cell=the_cell) - -which sets the cubic cell defined before. -From now on, you can access the cell with the command - -.. code:: python - - structure.cell - -Finally, append each of the 2 atoms of the cell command. -You can do it using commands like - -.. code:: python - - structure.append_atom(position=(alat/4., alat/4., alat/4.), symbols="Si") - -for the first ‘Si’ atom. -Repeat it for the other atomic site (0, 0, 0). -You can access and inspect the structure sites with the command - -.. code:: python - - structure.sites - -If you make a mistake, start over from -``structure = StructureData(cell=the_cell)``, or equivalently use -``structure.clear_kinds()`` to remove all kinds (atomic species) and sites. -Alternatively, AiiDA structures can also be converted directly from ASE structures [#f1]_ using - -.. code:: python - - from ase.spacegroup import crystal - ase_structure = crystal('Si', [(0, 0, 0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90], primitive_cell=True) - structure = StructureData(ase=ase_structure) - -Now you can store the new structure object in the database with the command: - -.. code:: python - - structure.store() - -Finally, we can also import the silicon structure from an external (online) repository such as the Crystallography Open Database (COD): - -.. code:: python - - from aiida.tools.dbimporters.plugins.cod import CodDbImporter - importer = CodDbImporter() - for entry in importer.query(formula='Si', spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print("Formula", structure.get_formula()) - print("Unit cell volume: ", structure.get_cell_volume()) - -In that case two duplicate structures are found for 'Si'. -A more in-depth tutorial can be found in :ref:`this appendix<2019_mandi_appendix_structure_data>`. - -Accessing inputs and outputs ----------------------------- - -Load again the calculation node used in Section :ref:`2019_mandi_loadnode`: - -.. code:: python - - calc = load_node(PK) - -Then type - -.. code:: python - - calc.inputs. - -and press ``TAB``: you will see all the link names between the calculation and its input nodes. -You can use a specific linkname to access the corresponding input node, e.g.: - -.. code:: python - - calc.inputs.structure - -Similarly, if you type: - -.. code:: python - - calc.outputs. - -and then ``TAB``, you will list all output link names of the calculation. -One of them leads to the structure that was the input of ``calc`` we loaded previously: - -.. code:: python - - calc.outputs.output_structure - -Note that links have a single name, that was assigned by the calculation that used the corresponding input or produced the corresponding output, as illustrated in section :ref:`2019_mandi_aiidagraph`. - -For a more programmatic approach, you can get a represenation of the inputs and outputs of a node, say ``calc``, through the following methods: - -.. code:: python - - calc_incoming = calc.get_incoming() - calc_outgoing = calc.get_outgoing() - -These methods will return an instance of the ``LinkManager`` class. -You can iterate over the neighboring nodes by calling the ``.all()`` method: - -.. code:: python - - for entry in calc.get_outgoing(): - print(entry.link_label, entry.link_type, entry.node) - -each entry returned by ``.all()`` is a ``LinkTriple``, a named tuple, from which you can get the link label and type and the neighboring node itself. -If you print one, you will see something like: - -.. code:: python - - LinkTriple(node=, link_type=, link_label=u'output_parameters') - -There are many other convenience methods on the ``LinkManager``. -For example if you are only interested in the link labels you can use: - -.. code:: python - - calc.get_outgoing().all_link_labels() - -which will return a list of all the labels of the outgoing links. -Likewise, ``.all_nodes()`` will give you a list of all the nodes to which links are going out from the ``calc`` node. -If you are looking for the node with a specific label, you can use: - -.. code:: python - - calc.get_outgoing().get_node_by_label('output_parameters') - -The ``get_outgoing`` and ``get_incoming`` methods also support filtering on various properties, such as the link label. -For example, if you only want to get the outgoing links whose label starts with ``output``, you can do the following: - -.. code:: python - - calc.get_outgoing(link_label_filter='output%').all_nodes() - - -Pseudopotential families ------------------------- - -Pseudopotentials in AiiDA are grouped in 'families' that contain one single pseudo per element. -We will see how to work with UPF pseudopotentials (the format used by Quantum ESPRESSO and some other codes). -Download and untar the SSSP pseudopotentials via the commands: - -.. code:: bash - - wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz - -Then you can upload the whole set of pseudopotentials to AiiDA by using the following ``verdi`` command: - -.. code:: bash - - verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' - -In the command above, ``SSSP_efficiency_pseudos`` is the folder containing the pseudopotentials, ``'SSSP'`` is the name given to the family, and the last argument is its description. -Finally, you can list all the pseudo families present in the database with - -.. code:: bash - - verdi data upf listfamilies - -A more in-depth tutorial about working with ``UpfData`` nodes and pseudopotential families can be found in :ref:`this appendix<2019_mandi_appendix_upf_data>`. - - -.. rubric:: Footnotes - -.. [#f1] We purposefully do not provide advanced commands for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks already exist (such as ASE or pymatgen). diff --git a/docs/pages/2019_IIT_Mandi_India/sections/workflows.rst b/docs/pages/2019_IIT_Mandi_India/sections/workflows.rst deleted file mode 100644 index 55b37578..00000000 --- a/docs/pages/2019_IIT_Mandi_India/sections/workflows.rst +++ /dev/null @@ -1,512 +0,0 @@ -Workflows -========= - -The aim of the last part of this tutorial is to introduce the concept of workflows in AiiDA. - -In this section, we will ask you to: - -1. Understand how to keep the provenance when running small python scripts to convert one data object into another (postprocessing, preparation of inputs, etc.) -2. Understand how to represent simple python functions in the AiiDA database -3. Learn how to write a simple workflow in AiiDA (without and with remote calculation submission) -4. Learn how to write a workflow with checkpoints: this means that, even if your workflow requires external calculations to start, they and their dependencies are managed through the daemon. - While you are waiting for the calculations to complete, you can stop and even shutdown the computer in which AiiDA is running. - When you restart, the workflow will continue from where it was. -5. (optional) Go a bit deeper in the syntax of workflows with checkpoints (``WorkChain``), e.g. implementing a convergence workflow using ``while`` loops. - -.. note:: - - This is probably the most 'complex' part of the tutorial. - We suggest that you try to understand the underlying logic behind the scripts, without focusing too much on the details of the workflows implementation or the syntax. - If you want, you can then focus more on the technicalities in a second reading. - -Introduction ------------- - -The ultimate aim of this section is to create a workflow to calculate the equation of state of silicon. -This is a very common task for an *ab initio* researcher. -An equation of state consists in calculating the total energy (E) as a function of the unit cell volume (V). -The minimal energy is reached at the equilibrium volume. -Equivalently, the equilibrium is defined by a vanishing pressure (p=-dE/dV). -In the vicinity of the minimum, the functional form of the equation of state can be approximated by a parabola. -Such an approximation greatly simplifies the calculation of the bulk modulus, that is proportional to the second derivative of the energy (a more advanced treatment requires fitting the curve with, e.g., the Birch–Murnaghan expression). - -The process of calculating an equation of state puts together several operations. -First, we need to define and store in the AiiDA database the basic structure of, e.g., bulk Si. -Next, one has to define several structures with different lattice parameters. -Those structures must be connected between them in the database, in order to ensure that their provenance is recorded. -In other words, we want to be sure that in the future we will know that if we find a bunch of rescaled structures in the database, they all descend from the same one. -How to link two nodes in the database in an easy way is the subject of :ref:`2019_mandi_provenancewf`. - -In the following sections, the newly created structures will then serve as an input for total energy calculations performed, in this tutorial, with Quantum ESPRESSO. -This task is very similar to what you have done in the previous part of the tutorial. -Finally, you will fit the resulting energies as a function of volume to get the bulk modulus. -As the EOS task is very common, we will show how to automate its computation with workflows, and how to deal with both serial and parallel (i.e., independent) execution of multiple tasks. -Finally, we will show how to introduce more complex logic in your workflows such as loops and conditional statements (:ref:`see this section<2019_mandi_convpressure>`), with an example on a convergence loop to find iteratively the minimum of an EOS. - -.. _2019_mandi_provenancewf: - -Process functions: a way to generalize provenance in AiiDA ----------------------------------------------------------- - -Imagine having a function that takes as input a string of the name of a chemical element and generates the corresponding bulk structure as a ``StructureData`` object. -The function might look like the following snippet: - -.. literalinclude:: include/snippets/workflows_create_diamond_fcc.py - :language: python - -For the equation of state you need another function that takes as input a ``StructureData`` object and a rescaling factor, and returns a ``StructureData`` object with the rescaled lattice parameter: - -.. literalinclude:: include/snippets/workflows_rescale.py - :language: python - -In order to generate the rescaled starting structures, say for five different lattice parameters you would combine the two functions. -Open a ``verdi shell``, define the two functions from the previous snippets and enter the following commands: - -.. code:: python - - initial_structure = create_diamond_fcc('Si') - rescaled_structures = [rescale(initial_structure, factor) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and store them in the database: - -.. code:: python - - initial_structure.store() - for structure in rescaled_structures: - structure.store() - -As expected, all the structures that you have created are not linked in any manner as you can verify via the ``get_incoming()``/``get_outgoing()`` methods of the ``StuctureData`` class. -Instead, you would like these objects to be connected as sketched in :numref:`2019_mandi_fig_provenance_process_functions`. - -.. _2019_mandi_fig_provenance_process_functions: -.. figure:: include/images/process_functions.png - - Typical graphs created by using calculation and work functions. - (a) The calculation function ``create_structure`` takes a ``Str`` object as input and returns a single ``StructureData`` object which is used as input for the calculation function ``rescale`` together with a ``Float`` object. - This latter calculation function returns another ``StructureData`` object, defining a crystal with a rescaled lattice constant. - (b) Graph generated by a work function that calls two calculation functions. - A wrapper work function ``create_rescaled`` calls serially the calculation functions ``create_structure`` and ``rescale``. - This relationship is stored via ``CALL`` links. - - -Now that you are familiar with AiiDA, you know that the way to connect two data nodes is through a calculation. -In order to 'wrap' python functions and automate the generation of the needed links, in AiiDA we provide you with what we call 'process functions'. -There are two variants of process functions: - - * calculation functions - * work functions - -These operate mostly the same, but they should be used for different purposes, which will become clear later. -A normal function can be converted to a calculation function by using the ``@calcfunction`` decorator [#f1]_ that takes care of storing the execution as a calculation and adding the links between the input and output data nodes. - -To turn the original functions ``create_diamond_fcc`` and ``rescale`` into calculation functions, simply change the definition as follows: - -.. code:: python - - # Add this import - from aiida.engine import calcfunction - - # Add decorators - @calcfunction - def create_diamond_fcc(element): - ... (same as above) - - @calcfunction - def rescale(structure, scale): - ... (same as above) - -Note that the only change is that the function definitions were 'decorated' with the ``@calcfunction`` line. -This is the only thing that is necessary to transform the plain python functions magically into fully-fledged AiiDA process functions. - -.. note:: - - The only additional change necessary, is that process function input and outputs need to be ``Data`` nodes, so that they can be stored in the database. - AiiDA objects such as ``StructureData``, ``Dict``, etc. carry around information about their provenance as stored in the database. - This is why we must use the special database-storable types ``Float``, ``Str``, etc. as shown in the snippet below. - -Try now to run the following script: - -.. code:: python - - from aiida.orm import Float, Str - initial_structure = create_diamond_fcc(Str('Si')) - rescaled_structures = [rescale(initial_structure, Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and check now that the output of ``initial_structure`` as well as the input of the rescaled structures point to an intermediate node, representing the execution of the calculation function, see :numref:`2019_mandi_fig_provenance_process_functions`. -For instance, you can check that the output links of ``initial_structure`` are the five ``rescale`` calculations: - -.. code:: python - - initial_structure.get_outgoing().all_nodes() - -which outputs - -.. code:: bash - - [, - , - , - , - ] - -and the inputs of each ``CalcFunctionNode`` 'rescale' are obtained with: - -.. code:: python - - for structure in initial_structure.get_outgoing().all_nodes(): - print(structure.get_incoming().all_nodes()) - -that will return - -.. code:: bash - - [, ] - [, ] - [, ] - [, ] - [, ] - -Function nesting -~~~~~~~~~~~~~~~~ -Calculation functions can be 'chained' together by wrapping them together in a work function. -The work function works almost exactly the same as a calculation function, except that it cannot 'create' data, but rather is used to 'call' calculation functions that do the calculations for it. -The calculation functions that it calls will be automatically linked in the provenance graph through 'call' links. -As an example, let us combine the two previously defined calculation functions by means of a wrapper work function called 'create_rescaled' that takes as input the element and the rescale factor. - -Type in your shell (or modify the functions defined in ``create_rescale.py`` and then run): - -.. include:: include/snippets/workflows_create_rescaled.py - :code: python - -and create an already rescaled structure by typing - -.. code:: python - - rescaled = create_rescaled(element=Str('Si'), scale=Float(0.98)) - -Now inspect the input links of ``rescaled``: - -.. code:: ipython - - In [6]: rescaled.get_incoming().all_nodes() - Out[6]: - [, - ] - -The object ``rescaled`` has two incoming links, corresponding to *two* different calculations as input. -These correspond to the calculations 'create_rescaled' and 'rescale' as shown in :numref:`2019_mandi_fig_provenance_process_functions`. -To see the 'call' link, inspect now the outputs of the ``WorkFunctionNode`` which corresponds to the ``create_rescaled`` work function. -Write down its ```` (in general, it will be different from 441), then in the shell load the corresponding node and inspect the outputs: - -.. code:: ipython - - In [12]: node = load_node() - In [13]: node.get_outgoing().all() - -You should be able to identify the two children calculations as well as the final structure (you will see the process nodes linked via CALL links: these are process-to-process links representing the fact that ``create_rescaled`` called two calculation functions). -The graphical representation of what you have in the database should match :numref:`2019_mandi_fig_provenance_process_functions`. - -.. _2019_mandi_sync: - -Run a simple workflow ---------------------- -Let us now use the work and calculation functions that we have just created to build a simple workflow to calculate the equation of state of silicon. -We will consider five different values of the lattice parameter obtained rescaling the experimental minimum, ``a=5.431``, by a factor in ``[0.96, 0.98, 1.0, 1.02, 1.04]``. -We will write a simple script that runs a series of five calculations and at the end returns the volume and the total energy corresponding to each value of the lattice parameter. -For your convenience, besides the functions that you have written so far in the file ``create_rescale.py``, we provide you with some other utilities to get the correct pseudopotential and to generate a pw input file, in :download:`common_wf.py <../scripts/common_wf.py>`. - -We have already created the following script, which you can :download:`download <../scripts/simple_sync_workflow.py>`, but please go through the lines carefully and make sure you understand them. -We suggest that you first have a careful look at it before running it: - -.. include:: ../scripts/simple_sync_workflow.py - :code: python - -If you look into the previous piece of code, you will notice that the way we submit a QE calculation is slightly different from what you have seen in the first part of the tutorial. -The following: - -.. code:: python - - calculations[label] = run(PwCalculation, **inputs) - -runs in the current python session (without the daemon), waits for its completion and returns the output in the user-defined variable ``result``. -The latter is a dictionary whose values are the output nodes generated by the work function, with the link labels as keys. -For example once the function is finished, in order to access the total energy, we need to access the ``Dict`` node which is linked via the 'output_parameters' link (see again Fig. 1 of Day 1 Tutorial, to see inputs and outputs of a Quantum ESPRESSO calculation). -Once the right node is retrieved as ``result['output_parameters']``, we need to get the ``energy`` attribute. -The global operation is achieved by the command - -.. code:: python - - result['output_parameters'].dict.energy - -To collect these results from the various calculations into a single data node, we need one final calculation function to do so. -Since the ``run_eos_wf`` is a 'workflow'-like processes, which cannot create data, this operation **cannot** simply be done in the work function body itself. -To keep the provenance **we have to** do this through a calculation function. -This is done by the ``create_eos_dictionary`` calculation function, that receives as inputs, the output parameters nodes from the completed calculations: - -.. code:: python - - @calcfunction - def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - -It constructs a new ``Dict`` node that contains a single value ``eos`` which is a list of tuples with the relevant data for each calculation. -If you were to inspect the returned data node, with for example ``verdi data dict show`` you would see something like: - -.. code:: bash - - { - "eos": [ - [ - 40.04786949775, - -308.193208771881, - "eV" - ], - [ - 37.6927343883263, - -307.990673492459, - "eV" - ], - [ - 35.4317918679613, - -307.666113525946, - "eV" - ], - [ - 45.048406674717, - -308.308206255253, - "eV" - ], - [ - 42.4991194939683, - -308.293522312459, - "eV" - ] - ] - } - -As you see, the function ``run_eos_wf`` has been decorated as a work function to keep track of the provenance. -To run the workflow it suffices to call the function ``run_eos_wf`` in a python script providing the required input parameters. -For simplicity, we have included few lines at the end of the script that invoke the function with a static choice of parameters: - -.. code:: python - - def run_eos(code=load_code('qe-6.4.1-pw@localhost'), pseudo_family='SSSP', element='Si'): - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - if __name__ == '__main__': - run_eos() - -To get a reference to the node that represents the function execution, we can ask the ``run`` function to return the node, in addition to the results. -Instead of calling the work function to run it, we can use the method ``run_get_node``: - -.. code:: python - - results, node = run_eos_wf.run_get_node(code, Str(pseudo_family), Str(element)) - print('run_eos_wf<{}> completed'.format(node.pk)) - -Run the workflow: - -.. code:: bash - - verdi run simple_sync_workflow.py - -The command above locks the shell until the full workflow has completed (we will see in a moment how to avoid this). -While the function is running, you can use (in a different shell) the command ``verdi process list`` to show ongoing and finished processes. -You can 'grep' for the ```` you are interested in. -Additionally, you can use the command ``verdi process status `` to show the tree of the calculatino functions called by the root work function with a given ````. - -Wait for the work function to finish, then call the function ``plot_eos()`` that we provided in the file ``common_wf.py`` to plot the equation of state and fit it with a Birch–Murnaghan equation. - -.. _2019_mandi_wf_multiple_calcs: - -Run multiple calculations -------------------------- - -You should have noticed that the calculations for different lattice parameters are executed serially, although they might perfectly be executed in parallel because their inputs and outputs are not connected in any way. -In the language of workflows, these calculations are executed in a synchronous (or blocking) way, whereas we would like to have them running *asynchronously* (i.e., in a non-blocking way, to run them in parallel). -One way to achieve this, is to submit the calculation to the daemon using the ``submit`` function. -Make a copy of the script ``simple_sync_workflow.py`` that we worked on in the previous section and name it ``simple_submit_workflow.py``. -To make the new script work asynchronously, simply change the following subset of lines: - -.. code:: python - - from aiida.engine import run - - [...] - - for label, factor in zip(labels, scale_factors): - - [...] - - calculations[label] = run(PwCalculation, **inputs) - - [...] - - inputs = {label: result['output_parameters'] for label, result in calculations.items()} - eos = create_eos_dictionary(**inputs) - - [...] - -replacing them with - -.. code:: python - - from aiida.engine import submit - from time import sleep - - [...] - - for label, factor in zip(labels, scale_factors): - [...] - - # Replace `run` with `submit` - calculations[label] = submit(PwCalculation, **inputs) - - [...] - - # Wait for the calculations to finish - for calculation in calculations.values(): - while not calculation.is_finished: - sleep(1) - - inputs = {label: node.get_outgoing().get_node_by_label('output_parameters') for label, node in calculations.items()} - eos = create_eos_dictionary(**inputs) - -The main differences are: - -- ``run`` is replaced by ``submit`` -- The return value of ``submit`` is not a dictionary describing the outputs of the calculation, but it is the node that represents the execution of that calculation. -- Each calculation starts in the background and calculation nodes are added to the ``calculations`` dictionary. -- At the end of the loop, when all calculations have been launched with ``submit``, another loop is used to wait for all calculations to finish before gathering the results as the final step. - -In the next section we will show you another way to achieve this, which has the added bonus that it introduces checkpoints in the work function, from which the process can be resumed should it be interrupted. -After applying the modifications, run the script. -You will see that all calculations start at the same time, without waiting for the previous ones to finish. - -If in the meantime you run ``verdi process status ``, all five calculations are already shown as output. -Also, if you run ``verdi process list``, you will see how the calculations are submitted to the scheduler. - -.. _2019_mandi_workchainsimple: - -Workchains, or how not to get lost if your computer shuts down or crashes -------------------------------------------------------------------------- - -The simple workflows that we have used so far have been launched by a python script that needs to be running for the whole time of the execution, namely the time in which the calculations are submitted, and the actual time needed by Quantum ESPRESSO to perform the calculation and the time taken to retrieve the results. -If you had killed the main python process during this time, the workflow would not have terminated correctly. -Perhaps you killed the calculation and you experienced the unpleasant consequences: intermediate calculation results are potentially lost and it is extremely difficult to restart a workflow from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way to insert checkpoints, where the main code defining a workflow can be stopped (you can even shut down the machine on which AiiDA is running!). -We call these work functions with checkpoints 'work chains' because, as you will see, they basically amount to splitting a work function in a chain of steps. -Each step is then ran by the daemon, in a way similar to the remote calculations. - -Here below you can find the basic rules that allow you to convert your workfunction-based script to a workchain-based one and a snippet example focusing on the code used to perform the calculation of an equation of state. - -.. include:: ../scripts/equation_of_state.py - :code: python - -.. warning:: - - WorkChains need to be defined in a **separate file** from the script used to run them. - E.g. save your WorkChain in ``workchains.py`` and use ``from workchains import MyWorkChain`` to import it in your script. - - Furthermore, the directory containing the WorkChain definition needs to be in the ``PYTHONPATH`` in order for the AiiDA daemon to find it. - If your ``workchains.py`` sits in ``/home/max/workchains``, add a line ``export PYTHONPATH=$PYTHONPATH:/home/max`` to the ``/home/max/.virtualenvs/aiida/bin/activate`` script, followed by ``verdi daemon restart``. - -- Instead of using decorated functions you need to define a class, inheriting from a prototype class called ``WorkChain`` that is provided by AiiDA in the ``aiida.engine`` module. - -- Within your class you need to implement a ``define`` classmethod that always takes ``cls`` and ``spec`` as inputs. - Here you specify the main information on the workchain, in particular: - - - The *inputs* that the workchain expects. - This is obtained by means of the ``spec.input()`` method, which provides as the key feature the automatic validation of the input types via the ``valid_type`` argument. - The same holds true for outputs, as you can use the ``spec.output()`` method to state what output types are expected to be returned by the workchain. - - - The ``outline`` consisting in a list of 'steps' that you want to run, put in the right sequence. - This is obtained by means of the method ``spec.outline()`` which takes as input the steps. - *Note*: in this example we just split the main execution in two sequential steps, that is, first ``run_eos`` then ``results``. - However, more complex logic is allowed, as will be explained in :ref:`another section<2019_mandi_convpressure>`. - -- You need to split your main code into methods, with the names you specified before into the outline (``run_eos`` and ``results`` in this example). - Where exactly should you split the code? - Well, the splitting points should be put where you would normally block the execution of the script for collecting results in a standard work function, namely whenever you call the method ``.result()``. - Each method should accept only one parameter, ``self``, e.g. ``def step_name(self)``. - -- You will notice that the methods reference the attribute ``ctx`` through ``self.ctx``, which is called the *context* and is inherited from the base class ``WorkChain``. - A python function or process function normally just stores variables in the local scope of the function. - For instance, in the example of :ref:`this subsection<2019_mandi_sync>`, you stored the completed calculations in the ``calculations`` dictionary, that was a local variable. - - In work chains, instead, to preserve variables between different steps, you need to store them in a special dictionary called *context*. - As explained above, the context variable ``ctx`` is inherited from the base class ``WorkChain``, and at each step method you just need to update its content. - AiiDA will take care of saving the context somewhere between workflow steps (on disk, in the database, depending on how AiiDA was configured). - For your convenience, you can also access the value of a context variable as ``self.ctx.varname`` instead of ``self.ctx['varname']``. - -- Any submission within the workflow should not call the normal ``run`` or ``submit`` functions, but ``self.submit`` to which you have to pass the process class, and a dictionary of inputs. - -- The submission in ``run_eos`` returns a future and not the actual calculation, because at that point in time we have only just launched the calculation to the daemon and it is not yet completed. - Therefore it literally is a 'future' result. - Yet we still need to add these futures to the context, so that in the next step of the workchain, when the calculations are in fact completed, we can access them and continue the work. - To do this, we can use the ``ToContext`` class. - This class takes a dictionary, where the values are the futures and the keys will be the names under which the corresponding calculations will be made available in the context when they are done. - See how the ``ToContext`` object is created and returned in ``run_eos``. - By doing this, the workchain will implicitly wait for the results of all the futures you have specified, and then call the next step *only when all futures have completed*. - -- *Return values*: While in normal process functions you attach output nodes to the node by invoking the *return* statement, in a workchain you need to call ``self.out(link_name, node)`` for each node you want to return. - Of course, if you have already prepared a dictionary of outputs, you can just use the following syntax: - - .. code:: python - - self.out_many(retdict) # Keys are link names, value the nodes - - The advantage of this different syntax is that you can start emitting output nodes already in the middle of the execution, and not necessarily at the very end as it happens for normal functions (*return* is always the last instruction executed in a function or method). - Also, note that once you have called ``self.out(link_name, node)`` on a given ``link_name``, you can no longer call ``self.out()`` on the same ``link_name``: this will raise an exception. - -Finally, the workflow has to be run. -For this you have to use the function ``run`` passing as arguments the ``EquationOfState`` class and the inputs as key-value arguments. -For example, you can execute: - -.. code:: python - - from aiida.engine import run - run(EquationOfState, element=Str('Si'), code=load_code('qe-6.4.1-pw@localhost'), pseudo_family=Str('SSSP')) - -While the workflow is running, you can check (in a different terminal) what is happening to the calculations using ``verdi process list``. -You will see that after a few seconds the calculations are all submitted to the scheduler and can potentially run at the same time. - -.. warning:: - - The calculations launched by the work chain will now be submitted to the daemon, instead of run in the same interpreter - However, by using ``run`` on the work chain itself, it will still not be able to restart. - To make the work chain save its checkpoints, you should use the ``submit`` launcher instead. - -As an additional exercise (optional), instead of running the main workflow ``EquationOfState``, try to submit it. -Note that the file where the work chains is defined will need to be globally importable (so the daemon knows how to load it) and you need to launch it (with ``submit``) from a different python file. -The easiest way to achieve this is typically to embed the workflow inside a python package. - -.. note:: - - As good practice, you should try to keep the steps as short as possible in term of execution time. - The reason is that the daemon can be stopped and restarted only between execution steps and not if a step is in the middle of a long execution. - -Finally, as an optional exercise if you have time, you can jump to :ref:`this appendix<2019_mandi_convpressure>`, which shows how to introduce more complex logic into your work chains (if conditionals, while loops etc.). -The exercise will show how to realize a convergence loop to obtain the minimum-volume structure in a EOS using the Newton's algorithm. - -.. note:: - - You might see warnings that say ``Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint``. - These are generated by the ``PwCalculation`` which is unable to save a checkpoint because it is not in a so called 'importable path'. - Simply put, this means that if AiiDA were to try and reload the class it wouldn't know which file to find it in. - To get around this you could simply put the workchain in a different file that is in the 'PYTHONPATH' and then launch it by importing it in your launch file. - In this way AiiDA knows where to find it next time it loads the checkpoint. - - -.. rubric:: Footnotes - -.. [#f1] In simple words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form ``@decorating_function_name`` on the line just before the ``def`` line of the decorated function. If you want to know more, there are many online resources explaining python decorators. diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/epfl.png b/docs/pages/2019_IIT_Mandi_India/sponsors/epfl.png deleted file mode 100644 index f888916d..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/epfl.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/iit_mandi.jpg b/docs/pages/2019_IIT_Mandi_India/sponsors/iit_mandi.jpg deleted file mode 100644 index 5efc98aa..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/iit_mandi.jpg and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/intersect.png b/docs/pages/2019_IIT_Mandi_India/sponsors/intersect.png deleted file mode 100644 index 17074094..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/intersect.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/marketplace.png b/docs/pages/2019_IIT_Mandi_India/sponsors/marketplace.png deleted file mode 100644 index 450146b5..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/marketplace.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/marvel.png b/docs/pages/2019_IIT_Mandi_India/sponsors/marvel.png deleted file mode 100644 index 92686e61..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/marvel.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/max.png b/docs/pages/2019_IIT_Mandi_India/sponsors/max.png deleted file mode 100644 index 2ebda134..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/max.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/psi-k-crystal_low.png b/docs/pages/2019_IIT_Mandi_India/sponsors/psi-k-crystal_low.png deleted file mode 100644 index d2a3d637..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/psi-k-crystal_low.png and /dev/null differ diff --git a/docs/pages/2019_IIT_Mandi_India/sponsors/swissuniversities.png b/docs/pages/2019_IIT_Mandi_India/sponsors/swissuniversities.png deleted file mode 100644 index dfea0319..00000000 Binary files a/docs/pages/2019_IIT_Mandi_India/sponsors/swissuniversities.png and /dev/null differ diff --git a/docs/pages/2019_ISSP_Chiba_Japan/index.rst b/docs/pages/2019_ISSP_Chiba_Japan/index.rst deleted file mode 100644 index a2b3c260..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/index.rst +++ /dev/null @@ -1,90 +0,0 @@ -.. _Chiba 2019 Homepage: - -2019, ISSP University of Tokyo, Chiba, Japan -============================================= - -+-----------------+--------------------------------------------------------------------------------+ -| Related resources | -+=================+================================================================================+ -| Virtual Machine | `Quantum Mobile (ISSP special)`_ | -+-----------------+--------------------------------------------------------------------------------+ -| python packages | `aiida-core 1.0.1`_, `aiida-quantumespresso 3.0.0a5`_, `aiidalab 19.11.0a2`_ | -+-----------------+--------------------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.4.1`_ | -+-----------------+--------------------------------------------------------------------------------+ - -.. _Quantum Mobile (ISSP special): http://phonondb.mtl.kyoto-u.ac.jp/aiida_tutorial/Quantum_Mobile_ISSP.ova -.. _aiida-core 1.0.1: https://pypi.org/project/aiida-core/1.0.1 -.. _aiida-quantumespresso 3.0.0a5: https://pypi.org/project/aiida-quantumespresso/3.0.0a5 -.. _aiidalab 19.11.0a2: https://pypi.org/project/aiidalab/19.11.0a2 -.. _Quantum Espresso 6.4.1: https://github.com/QEF/q-e/releases/tag/qe-6.4.1 - -These are the hands-on materials from the 2-day AiiDA tutorial -|tutorial_name| from December 19-20, 2019. - -Participants of the tutorial use the Quantum Mobile VirtualBox virtual -machine (VM) image linked above on their own computer. The image -already contains all the required software. This VM image can be -imported from the GUI interface of VirtualBox by "File => Import -Appliance" or from the Virtualbox window "[Tools] => -[Import]". - -Getting started ---------------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/setup - ./sections/first_taste - -Sections --------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/verdi_cmdline - ./sections/verdi_shell - ./sections/calculations - ./sections/querybuilder - ./sections/workflows - -Appendices ----------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/appendix_computer_code_setup - ./sections/appendix_structure_data - ./sections/appendix_upf_data - ./sections/appendix_input_validation - ./sections/appendix_restarting_calculations - ./sections/appendix_queries - ./sections/appendix_workflow_logic - -Acknowledgements ----------------- - -The |tutorial_name| was made possible by support from ISSP -Univ. Tokyo, MARVEL, and kindly hosted by ISSP Univ. Tokyo. - -.. image:: sponsors/issplogo_en.jpg - :target: http://www.issp.u-tokyo.ac.jp/index_en.html - :width: 120px - -.. image:: sponsors/marvel.png - :target: http://nccr-marvel.ch - :width: 105px - -.. image:: sponsors/epfl.png - :target: https://epfl.ch - :width: 105px - -.. |tutorial_name| raw:: html - - Workshop on Writing reproducible workflows for computational materials science using AiiDA diff --git a/docs/pages/2019_ISSP_Chiba_Japan/notebooks/bandstructure.ipynb b/docs/pages/2019_ISSP_Chiba_Japan/notebooks/bandstructure.ipynb deleted file mode 100644 index c1e2dc00..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/notebooks/bandstructure.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A real world workchain example: electronic band structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%aiida\n", - "\n", - "from datetime import datetime, timedelta\n", - "\n", - "from aiida.tools.dbimporters.plugins.cod import CodDbImporter\n", - "from aiida.engine import launch\n", - "\n", - "PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating the electronic band structure with an AiiDA workchain\n", - "This tutorial will show how useful a workchain can be in performing a well defined task, such as computing and visualizing the electronic band structure for a simple crystal structure. The goal of this tutorial is not to show you the intricacies of the actual workchain itself, but rather to serve as an example that workchains can simplify standard workflows in computational materials science. The workchain that we will use here will employ Quantum Espresso's pw.x code to calculate the charge densities for several crystal structures and compute a band structure from those. Many choices that normally face the researcher before being able to perform this calculation, such as the selection of suitable pseudo potentials, energy cutoff values, k-point grids and k-point paths along high symmetry points, are now performed automatically by the workchain. All that remains for the user to do is to simply define a structure, pass it to the workchain and sit back!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we import the crystal structure of Al as an example, and run the PwBandStructureWorkChain for that structure. The estimated run time is noted in a comment in the calculation cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading the COD importer so we can directly import structure from COD id's\n", - "importer = CodDbImporter()\n", - "# Make sure here to define the correct codename that corresponds to the pw.x code installed on your machine of choice\n", - "codename = 'qe-6.4.1-pw@localhost'\n", - "code = Code.get_from_string(codename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Importing example crystal structures from COD to AiiDA structure objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Al COD ID='9008460'\n", - "structure_Al = importer.query(id='9008460')[0].get_aiida_structure()\n", - "\n", - "structure_Al.get_formula()\n", - "\n", - "# The following structure can be used instead of Al, but will take much longer on the AWS machine.\n", - "# CaF2 COD ID='1000043' -- approximately 1/2 hour to run\n", - "# h-BN COD ID='9008997' -- approximately 45 mins to run\n", - "# GaAs COD ID='9008845' -- approximately 2 hours to run " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now we run the bandstructure workchain for the selected structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bandstructure workchain follows the following protocol:\n", - "* Determine the primitive cell of the input structure\n", - "* Run a vc-relax to relax the structure\n", - "* Refine the symmetry of the relaxed structure to ensure the primitive cell is used and run a self-consistent field calculation on it\n", - "* Run a non self-consistent field band structure calculation along a path of high symmetry k-points determined by [seekpath](http://materialscloud.org/tools/seekpath)\n", - "\n", - "Numerical parameters for the default 'theos-ht-1.0' protocol are determined as follows: \n", - "* Suitable pseudopotentials and energy cutoffs are automatically searched from the [SSSP library](http://materialscloud.org/sssp) installed on your machine (it uses the efficiency version 1.1).\n", - "* K-point mesh is selected to have a minimum k-point density of 0.2 Å-1\n", - "* A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations\n", - "\n", - "In case the pseudopotentials are not installed, they can be downloaded in a terminal as:\n", - "\n", - " wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz\n", - " tar -zxvf SSSP_efficiency_pseudos.tar.gz\n", - " \n", - "The protocol looks for a UPF file with a specific hash code, that is unique for each different file. \n", - "You can check that you have the right\n", - "one by performing a search in the database:\n", - "\n", - " qb=QueryBuilder()\n", - " qb.append(UpfData, filters={'attributes.md5':{'==':'cfc449ca30b5f3223ec38ddd88ac046d'}})\n", - " len(qb.all())\n", - "\n", - "'md5' is a searchable attribute of the pseudopotential data object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fermi_energy = results['scf_parameters'].dict.fermi_energy\n", - "results['band_structure'].show_mpl(y_origin=fermi_energy, plot_zero_axis=True)\n", - "\n", - "print(\"\"\"Final crystal symmetry: {spacegroup_international} (number {spacegroup_number})\n", - "Extended Bravais lattice symbol: {bravais_lattice_extended}\n", - "The system has inversion symmetry: {has_inversion_symmetry}\"\"\".format(\n", - " **results['seekpath_parameters'].get_dict()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to use a different pseudopotential family (or version of the family) (for instance [SSSP v1.0](https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz) instead of the default SSSP v1.1) you can pass an additional parameter when calling the WorkChain, as follows:\n", - " \n", - " protocol = Dict(dict={\n", - " 'name':'theos-ht-1.0', \n", - " 'modifiers': {\n", - " 'pseudo' : 'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - "\n", - "(note that only some values are accepted for pseudo, that you can find [here](https://github.com/aiidateam/aiida-quantumespresso/blob/b02250146576eb573ccb45d05047075f54853f9d/aiida_quantumespresso/utils/protocols/pw.py#L24))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al,\n", - " protocol=Dict(dict={\n", - " 'name':'theos-ht-1.0',\n", - " 'modifiers': {\n", - " 'pseudo':'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/pages/2019_ISSP_Chiba_Japan/notebooks/querybuilder-template.ipynb b/docs/pages/2019_ISSP_Chiba_Japan/notebooks/querybuilder-template.ipynb deleted file mode 100644 index 65dd70ae..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/notebooks/querybuilder-template.ipynb +++ /dev/null @@ -1,973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AiiDA's QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from datetime import datetime, timedelta\n", - "import numpy as np\n", - "from aiida import load_profile\n", - "from matplotlib import gridspec, pyplot as plt\n", - "load_profile()\n", - "from aiida.orm import load_node, Node, Group, Computer, User, CalcJobNode, Code\n", - "from aiida.plugins import CalculationFactory, DataFactory\n", - "\n", - "PwCalculation = CalculationFactory('quantumespresso.pw')\n", - "StructureData = DataFactory('structure')\n", - "KpointsData = DataFactory('array.kpoints')\n", - "Dict = DataFactory('dict')\n", - "UpfData = DataFactory('upf')\n", - "\n", - "def plot_results(query_res):\n", - " \"\"\"\n", - " :param query_res: The result of an instance of the QueryBuilder\n", - " \"\"\"\n", - " smearing_unit_set,magnetization_unit_set,pseudo_family_set = set(), set(), set()\n", - " # Storing results:\n", - " results_dict = {}\n", - " for pseudo_family, formula, smearing, smearing_units, mag, mag_units in query_res:\n", - " if formula not in results_dict:\n", - " results_dict[formula] = {}\n", - " # Storing the results:\n", - " results_dict[formula][pseudo_family] = (smearing, mag)\n", - " # Adding to the unit set:\n", - " smearing_unit_set.add(smearing_units)\n", - " magnetization_unit_set.add(mag_units)\n", - " pseudo_family_set.add(pseudo_family)\n", - "\n", - " # Sorting by formula:\n", - " sorted_results = sorted(results_dict.items())\n", - " formula_list = next(zip(*sorted_results))\n", - " nr_of_results = len(formula_list)\n", - "\n", - " # Checks that I have not more than 3 pseudo families.\n", - " # If more are needed, define more colors\n", - " #pseudo_list = list(pseudo_family_set)\n", - " if len(pseudo_family_set) > 3:\n", - " raise Exception('I was expecting 3 or less pseudo families')\n", - "\n", - " colors = ['b', 'r', 'g']\n", - "\n", - " # Plotting:\n", - " plt.clf()\n", - " fig=plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None)\n", - " gs = gridspec.GridSpec(2,1, hspace=0.01, left=0.1, right=0.94)\n", - "\n", - " # Defining barwidth\n", - " barwidth = 1. / (len(pseudo_family_set)+1)\n", - " offset = [-0.5+(0.5+n)*barwidth for n in range(len(pseudo_family_set))]\n", - " # Axing labels with units:\n", - " yaxis = (\"Smearing energy [{}]\".format(smearing_unit_set.pop()),\n", - " \"Total magnetization [{}]\".format(magnetization_unit_set.pop()))\n", - " # If more than one unit was specified, I will exit:\n", - " if smearing_unit_set:\n", - " raise ValueError('Found different units for smearing')\n", - " if magnetization_unit_set:\n", - " raise ValueError('Found different units for magnetization')\n", - " \n", - " # Making two plots, the top one for the smearing, the bottom one for the magnetization\n", - " for index in range(2):\n", - " ax=fig.add_subplot(gs[index])\n", - " for i,pseudo_family in enumerate(pseudo_family_set):\n", - " X = np.arange(nr_of_results)+offset[i]\n", - " Y = np.array([thisres[1][pseudo_family][index] for thisres in sorted_results])\n", - " ax.bar(X, Y, width=0.2, facecolor=colors[i], edgecolor=colors[i], label=pseudo_family)\n", - " ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15*index+5)\n", - " ax.set_xlim(-0.5, nr_of_results-0.5)\n", - " ax.set_xticks(np.arange(nr_of_results))\n", - " if index == 0:\n", - " plt.setp(ax.get_yticklabels()[0], visible=False)\n", - " ax.xaxis.tick_top()\n", - " ax.legend(loc=3, prop={'size': 18})\n", - " else:\n", - " plt.setp(ax.get_yticklabels()[-1], visible=False)\n", - " for i in range(0, nr_of_results, 2):\n", - " ax.axvspan(i-0.5, i+0.5, facecolor='y', alpha=0.2)\n", - " ax.set_xticklabels(list(formula_list),rotation=90, size=14, ha='center')\n", - " plt.show()\n", - "\n", - "def generate_query_graph(qh, out_file_name):\n", - "\n", - " def draw_vertice_settings(idx, vertice, **kwargs):\n", - " \"\"\"\n", - " Returns a string with all infos needed in a .dot file to define a node of a graph.\n", - " :param node:\n", - " :param kwargs: Additional key-value pairs to be added to the returned string\n", - " :return: a string\n", - " \"\"\"\n", - " if vertice['entity_type'].startswith('process'):\n", - " shape = \"shape=polygon,sides=4\"\n", - " elif vertice['entity_type'].startswith('data.code'):\n", - " shape = \"shape=diamond\"\n", - " else:\n", - " shape = \"shape=ellipse\"\n", - " filters = kwargs.pop('filters', None)\n", - " additional_string = \"\"\n", - " if filters:\n", - " additional_string += '\\nFilters:'\n", - " for k,v in filters.items():\n", - " additional_string += \"\\n {} : {}\".format(k,v)\n", - "\n", - "\n", - " label_string = \" ('{}')\".format(vertice['tag'])\n", - "\n", - " labelstring = 'label=\"{} {}{}\"'.format(\n", - " vertice['entity_type'], #.split('.')[-2] or 'Node',\n", - " label_string,\n", - " additional_string)\n", - " #~ return \"N{} [{},{}{}];\".format(idx, shape, labelstring,\n", - " return \"{} [{},{}];\".format(vertice['tag'], shape, labelstring)\n", - " nodes = {v['tag']:draw_vertice_settings(idx, v, filters=qh['filters'][v['tag']]) for idx, v in enumerate(qh['path'])}\n", - " links = [(v['tag'], v['joining_value'], v['joining_keyword']) for v in qh['path'][1:]]\n", - "\n", - " with open('temp.dot','w') as fout:\n", - " fout.write(\"digraph G {\\n\")\n", - " for l in links:\n", - " fout.write(' {} -> {} [label=\" {}\"];\\n'.format(*l))\n", - " for _, n_values in nodes.items():\n", - " fout.write(\" {}\\n\".format(n_values))\n", - "\n", - " fout.write(\"}\\n\")\n", - " import os\n", - " os.system('dot temp.dot -Tpng -o {}'.format(out_file_name))\n", - "\n", - "def store_formula_in_extra():\n", - " from aiida.orm import QueryBuilder\n", - " qb = QueryBuilder()\n", - " qb.append(StructureData, filters={'extras':{'!has_key':'formula'}})\n", - " for structure, in qb.all():\n", - " structure.set_extra('formula', structure.get_formula(mode='count'))\n", - "\n", - "store_formula_in_extra()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction to the QueryBuilder\n", - "As you will use `AiiDA` to run your calculations, the database that stores all the data and the provenance will quickly grow to be very large. To help you find the needle that you might be looking for in this big haystack, we need an efficient search tool. `AiiDA` provides a tool to do exactly this: the `QueryBuilder`. The `QueryBuilder` acts as the gatekeeper to your database, to whom you can ask questions about the contents of your database (also referred to as queries), by specifying what are looking for. In this part of the tutorial, we will focus on how to use the `QueryBuilder` to make these queries and understand/use the results.\n", - "\n", - "In order to use the `QueryBuilder`, we first need to import it. We can accomplish this by executing the `import` statement in the following cell. Go ahead and select the next cell, and press `Shift+Enter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida.orm import QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we can ask the `QueryBuilder` questions about our database, we first need to create an instance of it (i.e., we create a new query):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an instance of our `QueryBuilder` which we named `qb`, we are ready to start asking about the content of our database. For example, we may want to know exactly how many nodes there are in our database. To tell `qb` that we are interested in all the occurrences of the `Node` class in our database, we `append` it to the list of objects it should find. \n", - "\n", - "*Note*: The method is called `append` because, as we will see later, you can append multiple nodes to a `QueryBuilder` instance consecutively to search in the graph, as if you had a list: what we are doing is querying a graph, and for every vertice of the graph in our subquery, we will use one `append` call. But we'll see this use better in a few steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(Node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now narrowed down the scope of `qb` to just the nodes that are present in our database. To learn how many nodes there are exactly, all we have to do is to ask `qb` to count them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now as you may have learned in previous sections of the tutorial, nodes come in different kinds and flavors. For example, all our crystal structures that we have stored in the database are saved in nodes that are of the type `StructureData`. If instead of all the nodes, we would rather like to count only the crystal structure nodes, we simply tell our `QueryBuilder` to narrow its scope only to objects of type `StructureData`. Since we want to create a new independent query, we must create a new instance of the `QueryBuilder`. In the next cell, we have typed part of the code to count all the structure nodes. See if you can finish the line with the comment, to tell the `QueryBuilder` that you are only interested in `StructureData` nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "qb.append() # How do we finish this line to tell the query builder to count only the structure nodes?\n", - "qb.count()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.count()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of just counting how many structure nodes we have, we may also actually want to see some of them. This is as easy as telling our `QueryBuilder` that we are not interested in the `count` but rather that we want to retrieve `all` the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this command is very literal and does in fact retrieve **all** the structure nodes that are stored in your database, which may be very slow if your database becomes very large. One solution is to simply tell the `QueryBuilder` that we are, for example, only interested in 5 structure nodes. This can be done with the `limit` method as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to simply use the concept of array slicing, native to python, to specify a subset of the total return set to be returned. Notice that this example can be very slow in big databases. When you want performance, use the functionality native to the QueryBuilder like `limit`, that limit the number of results directly at the database level!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(None)\n", - "qb.all()[:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to know a little bit more about the retrieved structure nodes, we can loop through our results. This allows you, for instance, to print the formula of the structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "for structure, in qb.all():\n", - " print(structure.get_formula())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just a simple example how we can employ the `QueryBuilder` to get details about the contents of our database. We have now seen simple queries for the `Node` and `StructureData` classes, but the same rules apply to all the `AiiDA` node classes. For example we may want to count the number of entries for each of the node classes in the following list:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class_list = [Node, StructureData, KpointsData, Dict, UpfData, Code]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the tools we have learned so far, we can build a table of the number of occurrences of each of these node classes that are stored in our database. We simply loop over the `class_list` and create a `QueryBuilder` for each and count the entries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for class_name in class_list:\n", - " qb = QueryBuilder()\n", - " qb.append(class_name)\n", - "#TUT_USER_START\n", - " print() # Finish this line to print the results!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - " print('{:>15} | {:6}'.format(class_name.__name__, qb.count()))\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, you should see something like the following, where of course the numbers may differ for your database\n", - "\n", - "Class name | Entries\n", - "---------------|--------\n", - " Node | 10273 \n", - " StructureData | 271 \n", - " KpointsData | 953 \n", - " Dict | 2922 \n", - " UpfData | 85 \n", - " Code | 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Projection and filters\n", - "Up until now we have always asked the `QueryBuilder` to return the node class. However, we might not necessarily be interested in all the node's properties, but rather just a selected set or even just a single property. We can tell the `QueryBuilder` which properties we would like to be returned, by asking it to **project** those properties in the result. For example, we may only want to get the `uuid`s of a set of nodes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['uuid'])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By using the `project` keyword in the `append` call, we are informing the `QueryBuilder` that we are only interested in the `uuid` property of the `Node` class. Note that the value that we assign to `project` is a list, since we may want to specify more than one property. See if you can get the `QueryBuilder` to return *both* the `id` and the `uuid` of the first 5 `Node`s in the following cell.\n", - "\n", - "**Note**: in the context of the QueryBuilder, the `id` is the `pk` of the node." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_USER_START\n", - "qb.append(Node, project=)#? What should the value be for the project key\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Node, project=['id', 'uuid'])\n", - "#TUT_SOLUTION_END\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To give you an idea of the various properties that you can project for some of the `AiiDA` node classes you can consult the following table.\n", - "Note that this is by no means an exhaustive list:\n", - "\n", - "Class | Properties\n", - "---------|-----------\n", - "Node | `id`, `uuid`, `node_type`, `label`, `description`, `ctime`, `mtime`\n", - "Computer | `id`, `uuid`, `name`, `hostname`, `description`, `transport_type`, `scheduler_type`\n", - "User | `id`, `email`, `first_name`, `last_name`, `institution`\n", - "Group | `id`, `uuid`, `label`, `type_string`, `time`, `description`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same properties can also be used to *filter* for specific nodes in your database. Indeed, up until now, we only asked the `QueryBuilder` to return *all* the instances of a certain type of node, or at best a limited number of those (without specifying which ones). But in general we might be interested in a very specific node. For example, we may have the `id` of a certain node and we would like to know when it was created and last modified. We can tell the `QueryBuilder` to select nodes that only match that criterion, by telling it to **filter** based on that property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['ctime', 'mtime'], filters={'id': {'==': 1}})\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the syntax of the `filters` keyword. The value is a dictionary, where the keys indicate the node property that it is supposed to operate on, in this case the `id` property. The value of that key is again itself a dictionary, where the key indicates the logical operator `==` and the value corresponds to the value of the property.\n", - "\n", - "You may have multiple criteria that you want to filter for, in which case you can use the logical `or` and `and` operators. Let's say, for example, that you want the `QueryBuilder` to retrieve all the `StructureData` nodes that have a certain `label` **and** were created no longer than 12 days ago. You can translate this criterion by making use of the `and` operator which allows you to specify multiple filters that all have to be satisfied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(\n", - " Node, \n", - " filters={\n", - " 'and': [\n", - " {'ctime': {'>': datetime.now() - timedelta(days=12)}},\n", - " {'label': {'==':'graphene'}}\n", - " ]\n", - " }\n", - ")\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will have noticed that the `>` operator, and its related operators, can work with python `datetime` objects. These are just a few of the operators that `QueryBuilder` understands. Below you find a table with some of the logical operators that you can use:\n", - "\n", - "Operator | Data type | Example | Description\n", - "---------------------|-----------------------|------------------------------------|------------------\n", - "`==` | all | `{'==': '12'}` | equality operator\n", - "`in` | all | `{'in':['FINISHED', 'PARSING']}` | member of a set\n", - "`<`, `>`, `<=`, `>=` | float, int, datetime | `{'>': 5.2}` | size comparison operator\n", - "`like` | char, str | `{'like': 'calculation%'}` | string comparison, `%` is wildcard\n", - "`ilike` | char, str | `{'ilike': 'caLCulAtion%'}` | string comparison, capital insensitive\n", - "`or` | | `{'or': [{'<': 5.3}, {'>': 6.3}]}` | logical or operator\n", - "`and` | | `{'and': [{'>=': 2}, {'<=': 6}]}` | logical and operator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, try to write a query below that will retrieve all `Group` nodes whose `label` property starts with the string `tutorial`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your query here\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'tutorial%'}})\n", - "qb.limit(5)\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining relationships between query clauses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how we can use the `QueryBuilder` to search the database for entries of a specific node type, potentially projecting only specific properties and filtering for certain property values. However, our nodes do not live in a vacuum, but they are part of a graph and thus are linked one another. Therefore, we typically want to be able to search for nodes based on a certain relationship that they might have with other nodes. Consider for example that you have a `StructureData` node that was produced by some calculation. How would we be able to retrieve that calculation?\n", - "\n", - "To accomplish this, we need to be able to tell the `QueryBuilder` what the relationship is between the nodes that we are interested in. With the `QueryBuilder`, we can do the following to find all the structure nodes that have been created as an output by a `PwCalculation` process.\n", - "\n", - "**IMPORTANT NOTE**: In the graph, we are not looking for a `PwCalculation` process (since processes do not live in the graph, as you have learnt). We are actually looking for a `CalcJobNode` whose `process_type` column indicates that it was run by a `PwCalculation` process. Since this is a very common pattern, the `QueryBuilder` allows to directly append the `PwCalculation` process class as a shortcut, but it internally unwraps this into a query for a `CalcJobNode` with the appropriate filter on the `process_type`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are looking for pairs of nodes, we need to `append` the second node as well to the `QueryBuilder` instance. In the next line, to specify the relationship between the nodes, we need to be able to reference back to the `CalcJobNode` that is matched by the previous clause: therefore, we give it a `tag` with the `tag` keyword." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(StructureData, with_incoming='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal was to find `StructureData` nodes, so we `append` that to the `qb`. However, we didn't want to find just any `StructureData` nodes; they had to be an output of `PwCalculation`. \n", - "\n", - "Note how we expressed this relation by the `with_incoming` keyword, because we want a `StructureData` node having an *incoming* link from the `CalcJobNode` referenced by the `calculation` tag (i.e., the `StructureData` must be an *output* of the calculation).\n", - "\n", - "Now all we have to do is execute the query to retrieve our structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we did can be visualized schematically, thanks to a little tool we have written for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query1.png')\n", - "Image(filename='query1.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `with_incoming` keyword is only one of many potential relationships that exist between the various `AiiDA` nodes and that are implemented in the `QueryBuilder`. The table below gives an overview of the implemented relationships, which nodes they are defined for and what relation it implicates. The full documentation can be found [on this page of the AiiDA documentation](https://aiida-core.readthedocs.io/en/latest/querying/querybuilder/append.html#joining-entities)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entity from\t| Entity to\t| Relationship | Explanation\n", - "------------|-----------|------------------|------------\n", - "Node | Node | with_outgoing | One node as input of another node\n", - "Node | Node | with_incoming | One node as output of another node\n", - "Node | Node | with_descendants | One node as the ancestor of another node\n", - "Node | Node | with_ancestors | One node as descendant of another node\n", - "Group | Node | with_node | The group of a node\n", - "Node | Group | with_group | The node is a member of a group\n", - "Computer | Node | with_node | The computer of a node\n", - "Node | Computer | with_computer | The node of a computer\n", - "User | Node | with_node | The creator of a node is a user\n", - "Node | User | with_user | The node was created by a user" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, see if you can write a query that will return all the `UpfData` nodes that are a member of a `Group` whose name starts with the string `SSSP`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'SSSP%'}}, tag='group')\n", - "qb.append(UpfData, with_group='group')\n", - "qb.all()\n", - "#TUT_SOLUTION_END\n", - "# Here I also visualize what's going on:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query2.png')\n", - "Image(filename='query2.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Attributes and extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In section 2, we showed you how you can `project` specific properties of a `Node` and gave a list of properties that a `Node` instance possesses. Since then, we have come across a lot of different `AiiDA` data nodes, such as `StructureData` and `UpfData`, that were secretly `Node`'s in disguise. Or to put it correctly, as `AiiDA` employs the object-oriented programming paradigm, both `StructureData` and `UpfData` are examples of subclasses of the `Node` class and therefore inherit its properties. That means that whatever property a `Node` has, both `StructureData` and `UpfData` will have too. However, there is a semantic difference between a `StructureData` node and a `UpfData`, and so we may want to add a property to one that would not make sense for the other. To solve this, `AiiDA` introduces the concept of `attributes`. These are similar to properties, except that they are specific to the `Node` type that they are attached to. This allows you to add an `attribute` to a certain node, without having to change the implementation of all the others.\n", - "\n", - "For example, the `Dict` nodes that are generated as output of `PwCalculation`'s may have an attribute named `wfc_cutoff`. To project for this particular `attribute`, one can use exactly the same syntax as shown in section 2 for the regular `Node` properties, and one has to only prepend `attributes.` to the attribute name. Demonstration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='pw')\n", - "qb.append(Dict, with_incoming='pw', project=[\"attributes.wfc_cutoff\"])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not every `Dict` node has to have this attribute, in which case the `QueryBuilder` will return the python keyword `None`. Similar to the `attributes`, nodes also can have `extras`, which work in the same way, except that `extras` are mutable, which means that their value can be changed even after a node instance has been stored.\n", - "\n", - "If you are not sure which attributes a given node has, you can use the `.attributes` property to simply retrieve them all. It will return a dictionary with all the attributes the node has.\n", - "\n", - "Note that a node also has a number of additional methods. For instance, you can do `list(node.attributes_keys())` to get only the attribute keys, or `node.get_attribute('wfc_cutoff')` to get the value of a single attribute (these two variants are more efficient if the node has a lot of attributes and you don't need all data). Similarly, for extras, you have `node.extras`, `list(node.extras_keys())`, and `node.get_attribute('SOME_EXTRA_KEY')`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation)\n", - "node, = qb.first()\n", - "node.attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chemical-element symbol of a pseudopotential, that is represented by a `UpfData` node, is stored in the `element` attribute. Using the knowledge that filtering on attributes works exactly as for normal node properties, see if you can write a query that will search your database for pseudopotentials for silicon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_SOLUTION_START\n", - "qb.append(UpfData, filters={'attributes.element': {'==': 'Si'}})\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**For more exercises on relationships and attributes/extras, have a look at the appendix section on queries.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 5. A small high-throughput study" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The following section assumes that a specific dataset is present in your current AiiDA profile. If you are not running this script on the Virtual Machine of the AiiDA tutorial, this script will not produce the desired output.\n", - "You can download the Virtual Machine image from [https://aiida-tutorials.readthedocs.io](https://aiida-tutorials.readthedocs.io) along with the tutorial text (choose the correct version of the tutorial, depending on which version of AiiDA you want to try).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part of the tutorial, we will focus on how to systematically retrieve, parse and analyze the results of\n", - "multiple calculations using AiiDA. We know you’re able to do this yourself, but to save time, a set of calculations\n", - "have already been done with AiiDA for you on 57 perovskites, using three different pseudopotential families (LDA,\n", - "PBE and PBESOL, all from GBRV 1.2).\n", - "\n", - "**Note**: if you are not following the tutorial on the official virtual machine that comes with this data, but you are working with your own database, you first have to import this archive (download it from: https://drive.google.com/open?id=1eqJaJr7q1VsYYvGxqHxmDN7gBMs534rz) using `verdi import`\n", - "\n", - "These calculations are spin-polarized (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. The idea of this part of the tutorial is to “screen” for magnetic and metallic perovskites in a “high-throughput” way. As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. Once more check the list of groups in your database by typing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!verdi group list -A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The calculations needed for this task were put in three different groups whose names start with \"tutorial\" (one for each pseudopotential family). The main task is to make a plot showing, for all perovskites and for each pseudopotential family, the total magnetization and the $-TS$ contribution from the smearing to the total energy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start building the query\n", - "So we first of all need to instantiate a QueryBuilder instance. We `append` the groups of interest, which means that we select only groups that start with the string `tutorial_`. We can execute the query after this append (this will not affect the final results) and check whether we have retrieved 3 groups." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiating QB:\n", - "qb = QueryBuilder()\n", - "# Appending the groups I care about:\n", - "qb.append(Group, filters={'label':{'like':'tutorial_%'}}, project='label', tag='group')\n", - "# Visualize:\n", - "print(\"Groups:\", ', '.join([g for g, in qb.all()]))\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query3.png')\n", - "Image(filename='query3.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the calculations that are members of each group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# I want every PwCalculation that is a member of the specified groups:\n", - "#TUT_USER_START\n", - "qb.append(PwCalculation, tag='calculation', with_group=) # Complete the function call with the correct relationship-tag!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(PwCalculation, tag='calculation', with_group='group')\n", - "#TUT_SOLUTION_END\n", - "#Visualize\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query4.png') \n", - "Image(filename='query4.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the structures that are input of the calculation. \n", - "We extend the query to include the structures that are input of the calculations that match the query so far.\n", - "This means that we `append` StructureData, and defining the relationship with the calculation with corresponding keyword.\n", - "\n", - "For simplicity the formulas have been added in the extras of each structure node under the key `formula`. \n", - "(The function that does this is called `store_formula_in_extra` and can be found in the top cell of this notebook. It also uses the QueryBuilder!)\n", - "\n", - "Project the formula, stored in the extras under the key `formula`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(StructureData, project=, tag='structure', with_outgoing=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(StructureData, project=['extras.formula'], tag='structure', with_outgoing='calculation')\n", - "#TUT_SOLUTION_END\n", - "# Visualize:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query5.png')\n", - "Image(filename='query5.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the output of the calculation\n", - "Every successful PwCalculation outputs a `Dict` node that stores the parsed results as key-value pairs. You can find these pairs among the attributes of the `Dict` node. To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. For convenience, the units are also added as key/value pairs (with the same key name, but with `_units` appended). \n", - "Extend the query so that also the output `Dict` of each calculation is returned. Project only\n", - "the attributes relevant to your analysis.\n", - "\n", - "In particular, project (in this order):\n", - "\n", - "* The smearing contribution \n", - "* The units of the smearing contribution\n", - "* The magnetization \n", - "* The units of the magnetization\n", - "\n", - "(to know the projection keys, you can try to load one `CalcJobNode` from one of the groups, get its output `Dict` and inspect its `attributes` as discussed before, to see the key-value pairs that have been parsed)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(Dict, tag='results', project=['attributes.energy_smearing', ...], with_incoming=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Dict, tag='results',\n", - " project=['attributes.energy_smearing', 'attributes.energy_smearing_units',\n", - " 'attributes.total_magnetization', 'attributes.total_magnetization_units',\n", - " ], with_incoming='calculation'\n", - " )\n", - "#TUT_SOLUTION_END\n", - "\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query6.png') \n", - "Image(filename='query6.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print the query results\n", - "We can print the results to see if everything worked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = qb.all()\n", - "for item in results:\n", - " print(', '.join(map(str, item)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results\n", - "Getting a long list is not always helpful, and a graph can be much more clear and useful. To help you, we have already prepared a function that visualizes the results of the query. Run the following cell and, if you did everything correctly, you should get a graph with the results of your queries!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_results(results)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/__init__.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/common_wf.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/common_wf.py deleted file mode 100644 index 4a75d0ee..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/common_wf.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper functions.""" -from __future__ import absolute_import -import numpy as np -from aiida.plugins import CalculationFactory, DataFactory -from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def generate_scf_input_params(structure, code, pseudo_family): - """Construct a builder for the `PwCalculation` class and populate its inputs. - - :return: `ProcessBuilder` instance for `PwCalculation` with preset inputs - """ - parameters = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, # Important that this stays to get stress - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - - kpoints = KpointsData() - kpoints.set_kpoints_mesh([2, 2, 2]) - - builder = PwCalculation.get_builder() - builder.code = code - builder.structure = structure - builder.kpoints = kpoints - builder.parameters = Dict(dict=parameters) - builder.pseudos = validate_and_prepare_pseudos_inputs( - structure, pseudo_family=pseudo_family) - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calculation of Silicon with Quantum ESPRESSO" - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - - return builder - - -def birch_murnaghan(V, E0, V0, B0, B01): - """Compute energy by Birch Murnaghan formula.""" - r = (V0 / V)**(2. / 3.) - return E0 + 9. / 16. * B0 * V0 * (r - 1.)**2 * (2. + (B01 - 4.) * (r - 1.)) - - -def fit_birch_murnaghan_params(volumes_, energies_): - """Fit Birch Murnaghan parameters.""" - from scipy.optimize import curve_fit - - volumes = np.array(volumes_) - energies = np.array(energies_) - params, covariance = curve_fit( - birch_murnaghan, - xdata=volumes, - ydata=energies, - p0=( - energies.min(), # E0 - volumes.mean(), # V0 - 0.1, # B0 - 3., # B01 - ), - sigma=None) - return params, covariance - - -def plot_eos(eos_pk): - """ - Plots equation of state taking as input the pk of the ProcessCalculation - printed at the beginning of the execution of run_eos_wf - """ - import pylab as pl - from aiida.orm import load_node - eos_calc = load_node(eos_pk) - - data = [] - for V, E, units in eos_calc.outputs.eos.dict.eos: - data.append((V, E)) - - data = np.array(data) - params, _covariance = fit_birch_murnaghan_params(data[:, 0], data[:, 1]) - - vmin = data[:, 0].min() - vmax = data[:, 0].max() - vrange = np.linspace(vmin, vmax, 300) - - pl.plot(data[:, 0], data[:, 1], 'o') - pl.plot(vrange, birch_murnaghan(vrange, *params)) - - pl.xlabel("Volume (ang^3)") - # I take the last value in the list of units assuming units do not change - pl.ylabel("Energy ({})".format(units)) # pylint: disable=undefined-loop-variable - pl.show() diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/create_rescale.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/create_rescale.py deleted file mode 100644 index 508df377..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/create_rescale.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper CalcFunctions for equation of state.""" -from __future__ import absolute_import -from aiida.engine import calcfunction - - -@calcfunction -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - symbol = element.value - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure - - -@calcfunction -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/equation_of_state.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/equation_of_state.py deleted file mode 100644 index 703dc7e2..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/equation_of_state.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -"""Equation of State WorkChain.""" -from __future__ import absolute_import -from six.moves import zip - -from aiida.engine import WorkChain, ToContext, calcfunction -from aiida.orm import Code, Dict, Float, Str, StructureData -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -PwCalculation = CalculationFactory('quantumespresso.pw') -scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) -labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - -@calcfunction -def get_eos_data(**kwargs): - """Store EOS data in Dict node.""" - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -class EquationOfState(WorkChain): - """WorkChain to compute Equation of State using Quantum Espresso.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super(EquationOfState, cls).define(spec) - spec.input('code', valid_type=Code) - spec.input('element', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.output('initial_structure', valid_type=StructureData) - spec.output('eos', valid_type=Dict) - spec.outline( - cls.run_eos, - cls.results, - ) - - def run_eos(self): - """Run calculations for equation of state.""" - # Create basic structure and attach it as an output - initial_structure = create_diamond_fcc(self.inputs.element) - self.out('initial_structure', initial_structure) - - calculations = {} - - for label, factor in zip(labels, scale_facs): - - structure = rescale(initial_structure, Float(factor)) - inputs = generate_scf_input_params(structure, self.inputs.code, - self.inputs.pseudo_family) - - self.report( - 'Running an SCF calculation for {} with scale factor {}'. - format(self.inputs.element, factor)) - future = self.submit(PwCalculation, **inputs) - calculations[label] = future - - # Ask the workflow to continue when the results are ready and store them in the context - return ToContext(**calculations) - - def results(self): - """Process results.""" - inputs = { - label: self.ctx[label].get_outgoing().get_node_by_label( - 'output_parameters') - for label in labels - } - eos = get_eos_data(**inputs) - - # Attach Equation of State results as output node to be able to plot the EOS later - self.out('eos', eos) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/plot_calculation_results.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/plot_calculation_results.py deleted file mode 100644 index 628f8a7f..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/plot_calculation_results.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot results of your calculations.""" -from __future__ import absolute_import -from __future__ import print_function -import sys -from argparse import ArgumentParser -from six.moves import map, range, zip - -import numpy as np -from matplotlib import gridspec, pyplot as plt - - -def plot_results(inputfname, outputfname=None, plotformat=None): # pylint: disable=too-many-locals,too-many-statements,too-many-branches - """ - :param inputfile: the filename of the inputfile - :param (opt) outputfile: - The (optional) name for the outputfile. - :param (opt) format: - The (optional) file format, if not clear from the input file name - - Reads the input file provided by the user. - The format should be comma separated values: - formula, pseudo_family, smearing, smearing_units, magnetization, magnetization_units - - - This order has to be respected! - - If an outputfile is provided, use matplotlib.pyplot.savefig to save the figure - Else, calls matplotlib.pyplot.show to show in screen - """ - - # Reading the input file given to me: - with open(inputfname) as f: - lines = f.readlines() - - # Definining the variables I need to read the units and pseudo families - smearing_unit_set = set() - magnetization_unit_set = set() - pseudo_family_set = set() - - # Storing results: - results_dict = {} - - # Looping through results: - for line in lines: - try: - (formula, pseudo_family, smearing, smearing_units, magnetization, - magnetization_units) = [i.strip() for i in line.split(',')] - smearing, magnetization = list( - map(float, (smearing, magnetization))) - except Exception as e: # pylint: disable=broad-except - print("There was an {} reading line:\n" - "{}" - "Exception: {}\n" - "Skipping this line\n".format(type(e), line, e)) - continue - - # If this is a new formula, not present in results, - # Creating the key-value pair: - if formula not in results_dict: - results_dict[formula] = {} - - # Storing the results: - results_dict[formula][pseudo_family] = (smearing, magnetization) - - # Adding to the unit set: - smearing_unit_set.add(smearing_units) - magnetization_unit_set.add(magnetization_units) - pseudo_family_set.add(pseudo_family) - - # Sorting by formula: - sorted_results = sorted(results_dict.items()) - formula_list = list(zip(*sorted_results))[0] - nr_of_results = len(formula_list) - - # Checks that I have not more than 3 pseudo families. - # If more are needed, define more colors - - if len(pseudo_family_set) > 3: - raise Exception('I was expecting 3 or less pseudo families') - - colors = ['b', 'r', 'g'] - - # Plotting: - gs = gridspec.GridSpec(2, 1, hspace=0.01, left=0.1, right=0.94) - fig = plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None) - - # Defining barwidth - barwidth = 1. / (len(pseudo_family_set) + 1) - offset = [ - -0.5 + (0.5 + n) * barwidth for n in range(len(pseudo_family_set)) - ] - - # Axing labels with units: - yaxis = ("Smearing energy [{}]".format(smearing_unit_set.pop()), - "Total magnetization [{}]".format(magnetization_unit_set.pop())) - # If more than one unit was specified, I will exit: - if smearing_unit_set: - raise Exception('Found different units for smearing') - if magnetization_unit_set: - raise Exception('Found different units for magnetization') - - # Making two plots, one for the smearing, the lower for magnetization - for index in range(2): - ax = fig.add_subplot(gs[index]) - for i, pseudo_family in enumerate(pseudo_family_set): - X = np.arange(nr_of_results) + offset[i] - Y = np.array([ - thisres[1][pseudo_family][index] for thisres in sorted_results - ]) - - ax.bar(X, - Y, - width=0.2, - facecolor=colors[i], - edgecolor=colors[i], - label=pseudo_family) - ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15 * index + 5) - ax.set_xlim(-0.5, nr_of_results - 0.5) - ax.set_xticks(np.arange(nr_of_results)) - - if index: - plt.setp(ax.get_yticklabels()[-1], visible=False) - else: - plt.setp(ax.get_yticklabels()[0], visible=False) - ax.xaxis.tick_top() - ax.legend(loc=3, prop={'size': 18}) - - for i in range(0, nr_of_results, 2): - ax.axvspan(i - 0.5, i + 0.5, facecolor='y', alpha=0.2) - ax.set_xticklabels(list(formula_list), - rotation=90, - size=14, - ha='center') - - if outputfname is not None: - plt.savefig(outputfname, format=plotformat) - else: - plt.show() - - -if __name__ == '__main__': - parser = ArgumentParser() - parser.add_argument('inputfile', - help='The input file with the data to plot') - parser.add_argument('-o', - '--outputfile', - help='The outputfile to plot results to', - default=None) - parser.add_argument( - '-f', - '--format', - default=None, - help= - 'The format to plot, if outputfile is specified and no valid extension is provided' - ) - parsed_args = parser.parse_args(sys.argv[1:]) - plot_results(inputfname=parsed_args.inputfile, - outputfname=parsed_args.outputfile, - plotformat=parsed_args.format) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/plot_convergence_pressure.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/plot_convergence_pressure.py deleted file mode 100644 index 725133d3..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/plot_convergence_pressure.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot parabola using matplotlib.""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import range -from aiida.orm import load_node - - -def parabola(x, a, b, c): - """Parabola.""" - return a * x**2 + b * x + c - - -def show_parabola_results(pk): # pylint: disable=too-many-locals - """Plot (and show) parabola using matplotlib.""" - out_p = load_node(pk).outputs.steps.get_dict() - - step0 = out_p['step0'] - steps = out_p['steps'] - - import numpy as np - import pylab as pl - - data = [] - data.append((step0['V'], step0['E'])) - for step in steps: - data.append((step['V'], step['E'])) - - data = np.array(data) - min_V = data[:, 0].min() - max_V = data[:, 0].max() - - min_E = data[:, 1].min() - max_E = data[:, 1].max() - - min_V -= 10. - max_V += 10. - min_E -= 0.1 - max_E += 0.5 - - V_range = np.linspace(min_V, max_V, 500) - - for subplot in range(2): - pl.subplot(1, 2, 1 + subplot) - - pl.plot(data[:1, 0], data[:1, 1], 'o', color='red') - pl.annotate('0', xy=(data[0, 0], data[0, 1])) - - pl.plot(data[1:, 0], data[1:, 1], 'o', color='blue') - - color = 0.8 - for idx, step in enumerate(steps, start=1): - pl.plot(V_range, - parabola(V_range, step['a'], step['b'], step['c']), - color=(color, color, color)) - parabola_Vmin = -step['b'] / 2. / step['a'] - parabola_Emin = parabola(parabola_Vmin, step['a'], step['b'], - step['c']) - pl.plot([parabola_Vmin], [parabola_Emin], - 'x', - color=(color, color, color)) - pl.annotate(str(idx), xy=(step['V'], step['E'])) - pl.axvline(step['V'], color=(1., 0.7, 1.), linestyle='--') - color -= 0.25 - if color <= 0.: - color = 0. - - pl.axis([min_V, max_V, min_E, max_E]) - if subplot == 1: - # some zoom - pl.xlim((41.5, 44.)) - pl.ylim((-259.47, -259.45)) - pl.xlabel('Volume (ang^3)') - pl.ylabel('Energy (eV)') - - pl.show() - - -if __name__ == '__main__': - import sys - try: - pk_value = int(sys.argv[1]) - except (IndexError, ValueError): - print( - 'Pass as parameter the PK of the WorkChain calculating the pressure', - file=sys.stderr) - print('convergence to generate the plot.', file=sys.stderr) - sys.exit(1) - show_parabola_results(pk_value) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/pressure_convergence.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/pressure_convergence.py deleted file mode 100644 index 89f42195..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/pressure_convergence.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding: utf-8 -*- -"""Pressure convergence WorkChain""" -from __future__ import absolute_import -from __future__ import print_function -from aiida.engine import WorkChain, ToContext, while_, calcfunction, workfunction -from aiida.orm import Code, Float, Str, StructureData -from aiida.plugins import CalculationFactory, DataFactory - -from common_wf import generate_scf_input_params -from create_rescale import rescale - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def get_energy_first_derivative(stress): - """Return the energy first derivative from the stress. - - :param stress: the stress tensor in GPa - :return: first derivative of the energy in eV/Å^3 - """ - from numpy import trace - - GPa_to_eV_over_ang3 = 1. / 160.21766208 - - # Get the pressure (GPa) - pressure = trace(stress) / 3. - - # Pressure is -dE/dV; moreover p in kbar, we need to convert it to eV/Å^3 to be consistent - dE = -pressure * GPa_to_eV_over_ang3 - - return dE - - -def get_volume_energy_and_derivative(output_parameters): - """Return the volume, energy and energy derivative for given `PwCalculation` output parameters. - - Volume is in Å^3, energy in eV and energy derivative eV/Å^3 - - :param output_parameters: the `output_parameters` `Dict` result node of a `PwCalculation` - :return: tuple with volume, energy and energy derivative - """ - V = output_parameters.dict.volume - E = output_parameters.dict.energy - dE = get_energy_first_derivative(output_parameters.dict.stress) - - return V, E, dE - - -def get_energy_second_derivative(output_parameters_one, output_parameters_two): - """Return second derivative of the energy with respect to the volume given the results of two `PwCalculations`. - - The derivate is computed using the finite differences method. - - :param output_parameters_one: the `output_parameters` `Dict` result node of the first `PwCalculation` - :param output_parameters_two: the `output_parameters` `Dict` result node of the second `PwCalculation` - :return: the second derivative of the energy with respect to the volume - """ - dE1 = get_energy_first_derivative(output_parameters_one.dict.stress) - dE2 = get_energy_first_derivative(output_parameters_two.dict.stress) - V1 = output_parameters_one.dict.volume - V2 = output_parameters_two.dict.volume - - return (dE2 - dE1) / (V2 - V1) - - -def get_parabola_coefficients(V, E, dE, ddE): - """Return coefficients of a parabola fit to E = a*V^2 + b*V + c for the given volume and energy. - - :param V: volume in Å^3 - :param E: energy in eV - :param dE: the first derivative of the energy with respect to the volume - :param ddE: the second derivative of the energy with respect to the volume - :return: coefficients a, b and c - """ - a = ddE / 2. - b = dE - ddE * V - c = E - V * dE + V**2 * ddE / 2. - - return a, b, c - - -@workfunction -def get_structure(structure, step_data=None): - """Return a scaled version of the given structure, where the new volume is determined by the given step data.""" - initial_volume = structure.get_cell_volume() - - if step_data is None: - new_volume = initial_volume + 4. # In Å^3 - else: - # Minimum of a parabola - new_volume = -step_data.dict.b / 2. / step_data.dict.a - - scale_factor = (new_volume / initial_volume)**(1. / 3.) - scaled_structure = rescale(structure, Float(scale_factor)) - return scaled_structure - - -@calcfunction -def get_step_data(parameters_first, parameters_second=None): - """Generate a dictionary with the step parameters from the output parameters of a completed `PwCalculation`.""" - - # If the parameters of the second calculation are not passed, this is for the first step and we only return - # a dictionary with the volume, energy and derivative - if parameters_second is None: - V, E, dE = get_volume_energy_and_derivative(parameters_first) - return Dict(dict={'V': V, 'E': E, 'dE': dE}) - - # Otherwise, this is an iteration step and we return the difference between the steps - V, E, dE = get_volume_energy_and_derivative(parameters_first) - ddE = get_energy_second_derivative(parameters_first, parameters_second) - a, b, c = get_parabola_coefficients(V, E, dE, ddE) - - return Dict(dict={ - 'V': V, - 'E': E, - 'dE': dE, - 'ddE': ddE, - 'a': a, - 'b': b, - 'c': c - }) - - -@calcfunction -def bundle_step_data(step0, **kwargs): - """Bundle step data into Dict.""" - steps = [step.get_dict() for step in kwargs.values()] - print((step0, type(step0))) - print((steps, type(steps))) - return Dict(dict={'step0': step0.get_dict(), 'steps': steps}) - - -class PressureConvergence(WorkChain): - """Relax a structure using Newton's algorithm on the first derivative of the energy (minus the pressure).""" - - @classmethod - def define(cls, spec): - """Define spec of WorkChain.""" - # yapf: disable - # pylint: disable=bad-continuation - super(PressureConvergence, cls).define(spec) - spec.input('code', valid_type=Code, - help='Code setup to run `pw.x` to use for the calculations.') - spec.input('structure', valid_type=StructureData, - help='The structure to minimize.') - spec.input('pseudo_family', valid_type=Str, - help='Family of pseudopotentials to use for the calculations.') - spec.input('volume_tolerance', valid_type=Float, - help='Stop if the volume difference of two consecutive calculations is less than this threshold.') - spec.output('steps', valid_type=Dict, - help='The data of all the steps in the minimization process containing info about energy and volume') - spec.output('structure', valid_type=StructureData, - help='Final relaxed structure.') - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - - def setup(self): - """Launch the first calculation for the input structure, and a second calculation for a shifted volume.""" - scaled_structure = get_structure(self.inputs.structure) - self.ctx.last_structure = scaled_structure - - inputs0 = generate_scf_input_params(self.inputs.structure, - self.inputs.code, - self.inputs.pseudo_family) - inputs1 = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - - # Run two `PwCalculations` - future0 = self.submit(PwCalculation, **inputs0) - future1 = self.submit(PwCalculation, **inputs1) - - # Wait for them to complete before going to the next step - return ToContext(r0=future0, r1=future1) - - def put_step0_in_ctx(self): - """Store the outputs of the very first step in a specific dictionary.""" - self.ctx.step0 = get_step_data(self.ctx.r0.outputs.output_parameters) - - # Prepare the list containing the steps: step 1 will be stored here by move_next_step - self.ctx.steps = [] - - def move_next_step(self): - """Main part of the algorithm. - - Compare the results of two consecutive calculations and use Newton's algorithm on the pressure by fitting the - results with a parabola and setting the next volume to calculate to the parabola minimum. - - The oldest calculation gets replaced by the most recent and a new calculation is launched that will replace - the most recent. - """ - # Computer the new Volume using Newton's algorithm and create the new corresponding structure by scaling it - new_step_data = get_step_data(self.ctx.r0.outputs.output_parameters, - self.ctx.r1.outputs.output_parameters) - scaled_structure = get_structure(self.inputs.structure, new_step_data) - self.ctx.steps.append(new_step_data) - - # Replace the older step with the latest and set the current structure - self.ctx.r0 = self.ctx.r1 - self.ctx.last_structure = scaled_structure - - inputs = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - future = self.submit(PwCalculation, **inputs) - - return ToContext(r1=future) - - def not_converged(self): - """Return True if the worflow is not converged yet (i.e. the volume changed significantly).""" - r0_out = self.ctx.r0.outputs.output_parameters - r1_out = self.ctx.r1.outputs.output_parameters - - return abs(r1_out.dict.volume - - r0_out.dict.volume) > self.inputs.volume_tolerance - - def finish(self): - """Attach the result nodes as outputs.""" - steps = { - 'step{}'.format(index + 1): step - for index, step in enumerate(self.ctx.steps) - } - bundled_steps = bundle_step_data(step0=self.ctx.step0, **steps) - - self.out('steps', bundled_steps) - self.out('structure', self.ctx.last_structure) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/scripts/simple_sync_workflow.py b/docs/pages/2019_ISSP_Chiba_Japan/scripts/simple_sync_workflow.py deleted file mode 100644 index 1a89206e..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/scripts/simple_sync_workflow.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -"""Simple workflow example""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import zip - -from aiida.engine import run, Process, calcfunction, workfunction -from aiida.orm import load_code, Dict, Float, Str -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -# Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' -PwCalculation = CalculationFactory('quantumespresso.pw') - - -@calcfunction -def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -@workfunction -def run_eos_wf(code, pseudo_family, element): - """Run an equation of state of a bulk crystal structure for the given element.""" - - # This will print the pk of the work function - print('Running run_eos_wf<{}>'.format(Process.current().pid)) - - scale_factors = (0.96, 0.98, 1.0, 1.02, 1.04) - labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - calculations = {} - - # Create an initial bulk crystal structure for the given element, using the calculation function defined earlier - initial_structure = create_diamond_fcc(element) - - # Loop over the label and scale_factor pairs - for label, factor in list(zip(labels, scale_factors)): - - # Generated the scaled structure from the initial structure - structure = rescale(initial_structure, Float(factor)) - - # Generate the inputs for the `PwCalculation` - inputs = generate_scf_input_params(structure, code, pseudo_family) - - # Launch a `PwCalculation` for each scaled structure - print('Running a scf for {} with scale factor {}'.format( - element, factor)) - calculations[label] = run(PwCalculation, **inputs) - - # Bundle the individual results from each `PwCalculation` in a single dictionary node. - # Note: since we are 'creating' new data from existing data, we *have* to go through a `calcfunction`, otherwise - # the provenance would be lost! - inputs = { - label: result['output_parameters'] - for label, result in calculations.items() - } - eos = create_eos_dictionary(**inputs) - - # Finally, return the results of this work function - result = {'initial_structure': initial_structure, 'eos': eos} - - return result - - -def run_eos(code=load_code('qe-6.4.1-pw@localhost'), - pseudo_family='SSSP', - element='Si'): - """Helper function to run EOS WorkChain.""" - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - -if __name__ == '__main__': - run_eos() diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_computer_code_setup.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_computer_code_setup.rst deleted file mode 100644 index feef829c..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_computer_code_setup.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _2019_chiba_appendix_computer_code_setup: - -Setting up computers and codes -============================== - -To setup a computer, please refer to the `online documentation `_. -Instructions to setup a code for a computer can be `found here `_. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_input_validation.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_input_validation.rst deleted file mode 100644 index f41e6e0c..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_input_validation.rst +++ /dev/null @@ -1,94 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Calculation input validation -============================ - -This appendix shows additional ways to debug errors with Quantum ESPRESSO, namely -how to take advantage of a powerful tool to validate the input to Quantum ESPRESSO -and possibly suggest the correct name to misspelled keywords. - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. -Let's check for example this input dictionary, where we inserted two mistakes: - -.. code:: python - - parameters_dict = { - 'CTRL': { - 'calculation': 'scf', - 'restart_mode': 'from_scratch', - }, - 'SYSTEM': { - 'nat': 2, - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - -The two mistakes in the dictionary are the following: - -- We wrote a wrong namelist name (``CTRL`` instead of ``CONTROL``). -- We inserted the number of atoms explicitly: while that is indeed how the - number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system, - since this information is already contained within the StructureData. - -After we correct the dictionary ``parameters_dict``, we could run the calculation directly. -Alternatively, we can use a tool of the the `aiida-quantumespresso` plugin, -the 'input helper', which checks the inputs before submitting the calculation. - -In your script, after you define ``parameters_dict``, -you can validate it with the following command (note that you need to pass also an input crystal structure, -``structure``, to allow the validator to perform all the needed checks): - -.. code:: python - - PwCalculation = CalculationFactory('quantumespresso.pw') - validated_dict = PwCalculation.input_helper(parameters_dict, structure=structure) - parameters = Dict(dict=validated_dict) - - -The ``input_helper`` method will check for the correctness of the input parameters. -If misspelling or incorrect keys are detected, the method raises an exception, -which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, -without providing the namelists (useful if you don't remember where to put some variables in the input file). -Hence, you can provide to ``input_helper`` a dictionary like this one: - -.. code:: python - - parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, - } - - validated_dict = PwCalculation.input_helper( - parameters_dict, - structure=structure, - flat_mode=True - ) - -If you print ``validated_dict``, it will look like the following: - -.. code:: python - - { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_queries.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_queries.rst deleted file mode 100644 index 46cd91eb..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_queries.rst +++ /dev/null @@ -1,89 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Queries in AiiDA - Examples and Exercises -========================================= - -Optional exercises on relationships (Task 3) --------------------------------------------- - -**Hint for the exercises:** - -- You can have projections on properties of more than one entity in your query. - You just have to add the ``project`` key (specifying the list of properties that you want to project) along with the corresponding entity when you append it. - -**Exercises:**   Try to write the following queries: - -- Find all descendants of a ``StructureData`` with a certain ``uuid`` (descendants indicate all nodes that can be reached by following outgoing links starting from a given node, with any number of hops). - For every match, print both the ``StructureData`` node and the descendant. - -- Find all the ``FolderData`` nodes created by a specific user. - -Optional exercises on attributes and extras (Task 4) ----------------------------------------------------- - -**Hint for the exercises:** - -- You can easily order or limit the number of the results by using the ``order_by()`` and ``limit()`` methods of the QueryBuilder. - For example, we can order all our ``CalcJobNode``s by their ``id`` and limit the result to the first 10 as follows: - - .. code:: python - - qb = QueryBuilder() - qb.append(CalcJobNode, tag='calc') - qb.order_by({'calc': 'id'}) - qb.limit(10) - qb.all() - -**Exercises:** - -- Write a code snippet to check how many pseudopotentials you have for each element. - -- Smearing contribution to the total energy for calculations: - - 1. Write a query that returns the smearing contribution to the energy stored in some instances of ``Dict`` output by a ``CalcJobNode`` generated by running a ``PwCalculation`` process. - 2. Extend the previous query to also get the input structures of the ``CalcJobNode``. - 3. Which structures have a smearing contribution to the energy smaller or equal to ``-0.02``? - -Summarizing what we learned by now - An example ------------------------------------------------ - -At this point you should be able to do queries with projections, joins and filters. -Moreover, you saw how to apply filters and projections even on attributes and extras. -Let's discover the full power of the ``QueryBuilder`` with a complex graph query that allows you to project various properties from different nodes and apply different filters and joins. - -Imagine that you would like to get the smearing energy for all the calculations that have a smearing energy below a given value and have a Sn\ :sub:`2`\ O\ :sub:`3` as input. -Moreover, besides the smearing energy, you would like to print the units of this energy and the formula of the structure that was given to the calculation. -The graphical representation of this query can be seen in :numref:`fig_qb_example_2_2019` and the actual query follows: - -.. _2019_chiba_fig_qb_example_2_2019: -.. figure:: include/images/qb_example_2.png - :width: 50% - :align: center - - Complex graph query. - -.. code:: python - - qb = QueryBuilder() - qb.append( - StructureData, - project=['extras.formula'], - filters={'extras.formula': 'BaO3Ti'}, - tag='structure' - ) - qb.append( - CalcJobNode, - tag='calculation', - with_incoming='structure' - ) - qb.append( - Dict, - tag='results', - filters={'attributes.energy_smearing':{'<=':-1e-8}}, - project=[ - 'attributes.energy_smearing', - 'attributes.energy_smearing_units', - ], - with_incoming='calculation' - ) - qb.all() diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_restarting_calculations.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_restarting_calculations.rst deleted file mode 100644 index bb06ea24..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_restarting_calculations.rst +++ /dev/null @@ -1,90 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -Restarting calculations -======================= - -Up till now, we have presented several cases where, for example, the wrong input parameters were passed to a calculation, causing it to fail. -Often we had to retype a lot of code, to setup a new calculation with the correct inputs. -In addition, there are many real-life scenarios where one would need to restart calculations from completed calculations with largely the same inputs. -For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to relaunch a calculation from one that has already completed, while having the chance to add or change inputs before launching it. -As an example, let us first submit a total energy calculation using a parameters dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, - } - -In this case, we set a very low number of self consistent iterations (3), which is too small to be able to reach the desired accuracy of 10\ :sup:`-14`. -This means the calculation will not converge and will not be successful, despite there not being any actual mistake in the parameters dictionary. - -To easily restart from the previous calculation, instead of retyping all the inputs, you can simply use the ``get_builder_restart`` method of the ``CalcJobNode``. -Just like the ``get_builder`` method of the ``Process`` class, this will create an instance of the ``ProcessBuilder``, except this one will have all the inputs pre-populated based on the node. -To do so, simply load the node of a terminated calculation job in a ``verdi shell``, that you want to restart from, e.g.: - -.. code:: python - - failed_calculation = load_node('') - restart_builder = failed_calculation.get_builder_restart() - -If you simply type ``restart_builder``, you can see that all the inputs have already been set to those that were used for the original calculation. -The only thing that now remains to be done, is to replace those inputs that caused the failure in the first place. - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['ELECTRONS']['electron_maxstep'] = 80 - restart_builder.parameters = Dict(dict=parameters) - -Simply giving the calculation some more steps in the convergence cycle will probably already fix the problem. - -.. note:: - - We have to create a new ``Dict`` node as we are changing its contents, and the original input is immutable as it would break the provenance. - -However, in this Quantum ESPRESSO example even more can be done, by using the results of the previous calculation to speed up the restart. -To do so, we simply have to set the input ``parent_folder`` to the remote working directory of the old calculation, i.e.: - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['CONTROL']['restart_mode'] = 'restart' - restart_builder.parameters = Dict(dict=parameters) - restart_builder.parent_folder = failed_calculation.outputs.remote_folder - -Note that this particular step is specific to a ``PwCalculation``, but the restart builder concept works for any calculation class. -Any of the inputs can be changed or set. -For example, you may also want to record that this calculation is a restart, in addition to the provenance graph, by setting the label or description: - -.. code:: python - - restart_builder.metadata.label = 'Restart from PwCalculation<{}>'.format(failed_calculation.pk) - -Ultimately whatever needs to be changed for the restart is up to you, but the restart builder makes it a lot easier. -Finally, to submit the restart, since it is a process builder, it works exactly as any other builder: - -.. code:: python - - from aiida.engine import launch - results, node = launch.run.get_node(restart_builder) - -You can now inspect the restarted calculation to verify that this time it actually completed successfully. -Using the restart builder, the required code to setup a calculation is much shorter than the one needed to launch a new one from scratch. -There is no need to load or create many of the inputs such as the pseudopotentials, structures and k-points, -because they were reused from the first calculation. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_structure_data.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_structure_data.rst deleted file mode 100644 index f212cc20..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_structure_data.rst +++ /dev/null @@ -1,280 +0,0 @@ -.. _2019_chiba_appendix_structure_data: - -General comments ----------------- - -This section contains an example of how you can use the -:class:`~aiida:aiida.orm.nodes.data.structure.StructureData` object -to create complex crystals. - -With the :class:`~aiida:aiida.orm.nodes.data.structure.StructureData` class we did not -try to have a full set of features to manipulate crystal structures. -Indeed, other libraries such as `ASE `_ exist, -and we simply provide easy -ways to convert between the ASE and the AiiDA formats. On the other hand, -we tried to define a "standard" format for structures in AiiDA, that can be -used across different codes. - - -How to use ``StructureData`` -------------------------------- - -Take a look at the following example:: - - alat = 4. # angstrom - cell = [[alat, 0., 0.,], - [0., alat, 0.,], - [0., 0., alat,], - ] - s = StructureData(cell=cell) - s.append_atom(position=(0.,0.,0.), symbols='Fe') - s.append_atom(position=(alat/2.,alat/2.,alat/2.), symbols='O') - -With the commands above, we have created a crystal structure ``s`` with -a cubic unit cell and lattice parameter of 4 angstrom, and two atoms in the -cell: one iron (Fe) atom in the origin, and one oxygen (O) at the center of -the cube (this cell has been just chosen as an example and most probably does -not exist). - -.. note:: As you can see in the example above, both the cell coordinates and - the atom coordinates are expressed in angstrom, and the position of - the atoms are given in a global absolute reference frame. - -In this way, any periodic structure can be defined. If you want to import -from ASE in order to specify the coordinates, e.g., in terms of the crystal -lattice vectors, see the guide on the conversion to/from ASE below. - -When using the :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` -method, further parameters can be passed. In particular, one can specify -the mass of the atom, particularly important if you want e.g. to run a -phonon calculation. If no mass is specified, the mass provided by -`NIST `_ (retrieved in October 2014) -is going to be used. The list of -masses is stored in the module :py:mod:`aiida.common.constants`, in the -``elements`` dictionary. - -Moreover, in the :class:`~aiida:aiida.orm.nodes.data.structure.StructureData` class -of AiiDA we also support the storage of crystal structures with alloys, -vacancies or partial occupancies. -In this case, the argument of the parameter ``symbols`` -should be a list of symbols, if you want to consider an alloy; -moreover, you must pass a ``weights`` list, with the same length as ``symbols``, -and with values between 0. (no occupancy) and 1. (full occupancy), to specify -the fractional occupancy of that site for each of the symbols specified -in the ``symbols`` list. The sum of -all occupancies must be lower or equal to one; if the sum is lower than one, -it means that there is a given probability of having a vacancy at that -specific site position. - -As an example, you could use:: - - s.append_atom(position=(0.,0.,0.),symbols=['Ba','Ca'],weights=[0.9,0.1]) - -to add a site at the origin of a structure ``s`` consisting of an alloy of -90% of Barium and 10% of Calcium (again, just an example). - -The following line instead:: - - s.append_atom(position=(0.,0.,0.),symbols='Ca',weights=0.9) - -would create a site with 90% probability of being occupied by Calcium, and -10% of being a vacancy. - -Utility properties ``s.is_alloy`` and ``s.has_vacancies`` can be used to -verify, respectively, if more than one element if given in the symbols list, -and if the sum of all weights is smaller than one. - -.. note:: if you pass more than one symbol, the property ``s.is_alloy`` will - always be ``True``, even if only one symbol has occupancy 1 and - all others have occupancy zero:: - - >>> s = StructureData(cell=[[4,0,0],[0,4,0],[0,0,4]]) - >>> s.append_atom(position=(0.,0.,0.), symbols=['Fe', 'O'], weights=[1.,0.]) - >>> s.is_alloy - True - - -Internals: Kinds and Sites --------------------------- -Internally, the :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` -method works by manipulating the kinds and sites of the current structure. -Kinds are instances of the :class:`~aiida:aiida.orm.nodes.data.structure.Kind` class and -represent a chemical species, with given properties (composing element or -elements, occupancies, mass, ...) and identified -by a label (normally, simply the element chemical symbol). - -Sites are instances of the :class:`~aiida:aiida.orm.nodes.data.structure.Site` class -and represent instead each single site. Each site refers -to a :class:`~aiida:aiida.orm.nodes.data.structure.Kind` to -identify its properties (which element it is, the mass, ...) and to its three -spatial coordinates. - -The :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` works in -the following way: - -* It creates a new :class:`~aiida:aiida.orm.nodes.data.structure.Kind` - class with the properties passed as parameters - (i.e., all parameters except ``position``). - -* It tries to identify if an identical Kind already exists in the list - of kinds of the structure (e.g., in the same atom with the same mass was - already previously added). Comparison of kinds is performed using - :py:meth:`aiida.orm.nodes.data.structure.Kind.compare_with`, and in particular - it returns ``True`` if the mass and the list of symbols and of weights are - identical (within a threshold). If an identical kind ``k`` is found, - it simply adds a new site referencing to kind ``k`` and with the provided - ``position``. Otherwise, it appends ``k`` to the list of kinds of the current - structure and then creates the site referencing to ``k``. The name of the - kind is chosen, by default, equal to the name of the chemical symbol (e.g., - "Fe" for iron). - -* If you pass more than one species for the same chemical symbol, but e.g. with - different masses, a new kind is created and the name is obtained postponing - an integer to the chemical symbol name. For instance, the following lines:: - - s.append_atom(position = [0,0,0], symbols='Fe', mass = 55.8) - s.append_atom(position = [1,1,1], symbols='Fe', mass = 57) - s.append_atom(position = [1,1,1], symbols='Fe', mass = 59) - - will automatically create three kinds, all for iron, with names ``Fe``, - ``Fe1`` and ``Fe2``, and masses 55.8, 57. and 59. respecively. - -* In case of alloys, the kind name is obtained concatenating all chemical - symbols names (and a X is the sum of weights is less than one). The same - rules as above are used to append a digit to the kind name, if needed. - -* Finally, you can simply specify the kind_name to automatically generate a - new kind with a specific name. This is the case if you want a name different - from the automatically generated one, or for instance if you want to create - two different species with the same properties (same mass, symbols, ...). - This is for instance the case in Quantum ESPRESSO in order to describe an - antiferromagnetic cyrstal, with different magnetizations on the different - atoms in the unit cell. - - In this case, you can for instance use:: - - s.append_atom(position = [0,0,0], symbols='Fe', mass = 55.845, name='Fe1') - s.append_atom(position = [2,2,2], symbols='Fe', mass = 55.845, name='Fe2') - - To create two species ``Fe1`` and ``Fe2`` for iron, with the same mass. - - .. note:: You do not need to specify explicitly the mass if the default one - is ok for you. However, when you pass explicitly a name and it coincides - with the name of an existing species, all properties that you - specify must be identical to the ones of the existing species, or the - method will raise an exception. - - .. note:: If you prefer to work with the - internal :class:`~aiida:aiida.orm.nodes.data.structure.Kind` - and :class:`~aiida:aiida.orm.nodes.data.structure.Site` classes, - you can obtain the same - result of the two lines above with:: - - from aiida.orm.nodes.data.structure import Kind, Site - s.append_kind(Kind(symbols='Fe', mass=55.845, name='Fe1')) - s.append_kind(Kind(symbols='Fe', mass=55.845, name='Fe1')) - s.append_site(Site(kind_name='Fe1', position=[0.,0.,0.])) - s.append_site(Site(kind_name='Fe2', position=[2.,2.,2.])) - - -Conversion to/from ASE ----------------------- - -If you have an AiiDA structure ``s``, you can get an ``ase.Atom`` object by -just calling the :meth:`aiida:aiida.orm.nodes.data.structure.StructureData.get_ase` -method:: - - ase_atoms = s.get_ase() - -.. note:: As we support alloys and vacancies in AiiDA, while ``ase.Atom`` does not, - it is not possible to export to ASE a structure with vacancies or alloys. - -If instead you have as ASE Atoms object and you want to load the structure -from it, just pass it when initializing the class:: - - StructureData = DataFactory('structure') - # or: - # from aiida.orm import StructureData - aiida_structure = StructureData(ase = ase_atoms) - -Creating multiple species -+++++++++++++++++++++++++ - -We implemented the possibility of specifying different Kinds (species) in the -``ase.atoms`` and then importing them. - -In particular, if you specify atoms with different mass in ASE, during the -import phase different kinds will be created:: - - >>> import ase - >>> StructureData = DataFactory("structure") - >>> ase_structure = ase.Atoms('Fe2') - >>> ase_structure[0].mass = 55. - >>> ase_structure[1].mass = 56. - >>> ase_structure.cell = cell # defines a periodic cell - >>> s = StructureData(ase=ase_structure) - >>> for kind in s.kinds: - >>> print(kind.name, kind.mass) - Fe 55.0 - Fe1 56.0 - -Moreover, even if the mass is the same, but you want to get different species, -you can use the ASE ``tags`` to specify the number to append to the element -symbol in order to get the species name:: - - >>> import ase - >>> StructureData = DataFactory("structure") - >>> ase_structure = ase.Atoms('Fe2') - >>> ase_structure[0].tag = 1 - >>> ase_structure[1].tag = 2 - >>> ase_structure.cell = cell - >>> s = StructureData(ase=ase_structure) - >>> for kind in s.kinds: - >>> print(kind.name) - Fe1 - Fe2 - -.. note:: in complicated cases (multiple tags, masses, ...), - it is possible that exporting a AiiDA structure - to ASE and then importing it again will not perfectly preserve the kinds and - kind names. - -Conversion to/from pymatgen ---------------------------- - -AiiDA structure can be converted to pymatgen's `Molecule`_ and -`Structure`_ objects by using, accordingly, -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen_molecule` -and -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen_structure` -methods:: - - pymatgen_molecule = aiida_structure.get_pymatgen_molecule() - pymatgen_structure = aiida_structure.get_pymatgen_structure() - -A single method -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen` can be -used for both tasks: converting periodic structures (periodic boundary -conditions are met in all three directions) to pymatgen's Structure and -other structures to pymatgen's Molecule:: - - pymatgen_object = aiida_structure.get_pymatgen() - -It is also possible to convert pymatgen's Molecule and Structure -objects to AiiDA structures:: - - StructureData = DataFactory("structure") - from_mol = StructureData(pymatgen_molecule=mol) - from_struct = StructureData(pymatgen_structure=struct) - -Also in this case, a generic converter is provided:: - - StructureData = DataFactory("structure") - from_mol = StructureData(pymatgen=mol) - from_struct = StructureData(pymatgen=struct) - -.. note:: Converters work with version 3.0.13 or later of - pymatgen. Earlier versions may cause errors. - -.. _Molecule: http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Molecule -.. _Structure: http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Structure diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_upf_data.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_upf_data.rst deleted file mode 100644 index 0e99ddb1..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_upf_data.rst +++ /dev/null @@ -1,123 +0,0 @@ -.. _2019_chiba_appendix_upf_data: - -Introduction: Pseudopotential families -++++++++++++++++++++++++++++++++++++++ - -The procedure of attaching a pseudopotential file to each atomic species -can easily become tedious. In many situations, you will not produce a different -pseudopotential file for every calculation you perform; most likely, when you start a project -you will stick to a pseudopotential file that is adequate for what you need. -Furthermore, in a high-throughput calculation, you will like to do calculations -over several elements while keeping the same functional. -That is also part of the reason why there are several projects -(like the `PSLibrary `_ -or `GBRV `_ -to name a few), that intend to develop a set of pseudopotentials that covers most -of the periodic table for different functionals. - -For this reason we introduced the concept of *pseudopotential families*. -Each family is a set of pseudopotentials that are grouped together in a special type of -`AiiDA Group of nodes`. Within each family, at most one pseudopotential -can be present for a given chemical element. - -A pseudopotential family does not necessarily have to cover the whole periodic table. -This means that you can create a pseudopotential family containing only the -pseudopotentials for a few elements that you are interested in. - -.. note:: - In principle, you can group different kinds of pseudopotentials into the same family. - It is your responsibility to group only those with the same type, - or obtained using the same functionals, approximations and / or levels of theory. - -Creating a pseudopotential family -+++++++++++++++++++++++++++++++++ - -.. note:: - The following commands are specific to the `Quantum ESPRESSO - interface `_. - For interfaces to other codes, please refer to the respective plugin documentation. - -In the following, we will create a pseudopotential family. -First, you need to collect the pseudopotential files which should go into the family in a -single folder -- we'll call it ``path/to/folder``. You can then add the family to -the AiiDA database with ``verdi``:: - - verdi data upf uploadfamily path/to/folder name_of_the_family "some description for your convenience" - -where ``name_of_the_family`` should be a unique name for the family, -and the final parameter is a string that is set in the ``description`` field of the group. - -If the a pseudopotential family with the same ``name_of_the_family`` exists already, -the pseudopotentials in the folder will be added to the existing group. -The code will raise an error if you try to add two (different) pseudopotentials for the same element. - -After the upload (which may take some seconds, so please be patient) -the pseudopotential family will be ready for use. - -.. hint:: - If you upload pseudopotentials which are already present in your database, - AiiDA will use the existing ``UPFData`` node instead of creating a duplicate one. - You can use the optional flag ``--stop-if-existing`` to instead abort - (without changing anything in the database) if an existing pseudopotential is found. - - -Getting the list of existing families -+++++++++++++++++++++++++++++++++++++ -To see wich pseudopotential families already exist in the database, type - -.. code-block:: console - - $ verdi data upf listfamilies - -Add a ``-d`` (or ``--with-description``) flag if you also want to read the description of each family. - -You can also filter the groups to get only a list of those containing a given set of elements -using the ``-e`` option. For instance, if you want to get only the families containing the -elements ``Ba``, ``Ti`` and ``O``, use - -.. code-block:: console - - $ verdi data upf listfamilies -e Ba Ti O - - -For more information on the command line options, type - -.. code-block:: console - - $ verdi data upf listfamilies -h - - -Manually adding pseudopotentials -++++++++++++++++++++++++++++++++ - -If you do not want to use pseudopotentials from a family, it is also possible to manually -add them to the database (even though we discourage this in general). - -A possible way of doing it is the following: we start by creating a list of -pseudopotential filenames that we need to use:: - - raw_pseudos = [ - "Ba.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF", - "Ti.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF", - "O.pbesol-n-rrkjus_psl.0.1-tested-pslib030.UPF"] - -In this simple example, we expect the pseudopotentials to be in the same folder -of the script. Then, we loop over the filenames and add them to the AiiDA database. -The ``get_or_create`` method checks if the pseudopotential is already in the database -and either stores it, or just returns the node already present in the database. -The second value returned is a boolean and tells us if the pseudopotential was -already present or not. - -:: - - UpfData = DataFactory('upf') - pseudos_to_use = [] - - for filename in raw_pseudos: - absname = os.path.abspath(filename) - pseudo, created = UpfData.get_or_create(absname, use_first=True) - pseudos_to_use.append(pseudo) - -.. note:: - When the pseudopotential is created, it is parsed and the elements to which it refers is stored - in the database and can be accessed using the ``pseudo.element`` property, as shown above. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_workflow_logic.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_workflow_logic.rst deleted file mode 100644 index ee1b7699..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/appendix_workflow_logic.rst +++ /dev/null @@ -1,118 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -.. _2019_chiba_workflow_logic: - -More workflow logic: while loops and conditional statements -=========================================================== - -In the previous sections, you have been introduced to ``WorkChains``, and the reason for using them over 'standard' work functions. -However, in the :ref:`original example<2019_chiba_workchainsimple>`, the ``spec.outline`` was quite simple, with a 'static' sequence of two steps. -Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). -To support this scenario, the ``spec.outline`` can support logic: `while` loops and `if/elif/else` blocks. -The simplest way to explain it is to show an example: - -.. code:: python - - from aiida.engine import if_, while_ - - spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), - ) - -that would *roughly* correspond, in a python syntax, to: - -.. code:: python - - s1() - if isA(): - s2() - elif isB(): - s3() - else: - s4() - s5() - while condition(): - s6() - -The only constraint is that condition functions (in the example above ``isA``, ``isB`` and ``condition``) must be class methods that returns ``True`` or ``False`` depending on whether the condition is met or not. - -A suggestion on how to write new workchains: use the outline to help you in designing the logic. -First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. -Then, define one by one the methods. - -.. _2019_chiba_convpressure: - -Pressure convergence --------------------- - -For example, we have prepared a simple workflow (using work chains, work functions and calculation functions) to optimize the lattice parameter of silicon efficiently using Newton's algorithm on the energy derivative, i.e. the pressure :math:`p=-dE/dV`. -You can download this code :download:`here <../scripts/pressure_convergence.py>` -The outline looks like this: - -.. code:: python - - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - -This outline already roughly explains the algorithm: after an initialization (``setup``) and putting the first step (number zero) in the ctx (``put_step0_in_ctx``), a function to move to the next step is called (``move_next_step``). -This is iterated while a given convergence criterion is not met (``not_converged``). -Finally, some reporting is done, including returning some output nodes (``finish``). - -If you are interested in the details of the algorithm, you can inspect the file. -The main ideas are described here: - -``setup`` -Generate a ``pw.x`` calculation for the input structure (with volume -(V)), and one for a structure where the volume is :math:`V+4 \mbox{\normalfont\AA}^3` (just to get a closeby volume). -Store the results in the context as ``r0`` and ``r1`` - -``put_step0_in_ctx`` -Store in the context :math:`V`, :math:`E(V)` and :math:`dE/dV` for the first calculation ``r0`` - -``move_next_step`` -This is the most important function. Calculate :math:`V`, :math:`E(V)` and :math:`dE/dV` for ``r1``. -Also, estimate :math:`d^2E/dV^2` from the finite difference of the first derivative of ``r0`` and ``r1`` (helper functions to achieve this are provided). -Get the :math:`a`, :math:`b` and :math:`c` coefficients of a parabolic fit :math:`E = aV^2 + bV + c` and estimate the expected minimum of the EOS function as the minimum of the fit :math:`V_0 = -b / 2a`. -Finally, replace ``r0`` with ``r1`` in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume :math:`V_0`, that will be stored in the context replacing ``r1``. -In this way, at the next iteration ``r0`` and ``r1`` will contain the latest two simulations. -Finally, at each step some relevant information (coefficients :math:`a`, :math:`b` and :math:`c`, volumes, energies, energy derivatives, ...) are stored in a list called ``steps``. -This whole list is stored in the context because it provides quantities to be preserved between different work chain steps. - -``not_converged`` -Return ``True`` if convergence has not been achieved yet. -Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold ``volume_tolerance``. - -``finish`` -This is the final step. -Mainly, we return the output nodes: ``steps`` with the list of results at each step, and ``structure`` with the final converged structure. - -The results returned in ``steps`` can be used to represent the evolution of the minimisation algorithm. -A possible way to visualize it is presented in :numref:`fig_convpressure` obtained with an initial lattice constant of `alat = 5.2`. - -.. _2019_chiba_fig_convpressure: -.. figure:: include/images/convergence_pressure.png - - Example of results of the convergence algorithm presented in this section. - The bottom plot is a zoom near the minimum. - The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. - The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/calculations.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/calculations.rst deleted file mode 100644 index aeedd14e..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/calculations.rst +++ /dev/null @@ -1,627 +0,0 @@ -.. _2019_chiba_calculations: - -Submit, monitor and debug calculations -====================================== - -In this section we'll be learning how to create new data in AiiDA. - -We will use the `Quantum Espresso `_ package to launch a simple `density functional theory `_ calculation of a silicon crystal using the :doi:`PBE exchange-correlation functional <10.1103/PhysRevB.54.16533>` and check its results. -While we're going to debug these issues 'manually' here, workflows (which you'll encounter later in this tutorial) can help you avoid these issues systematically. - -Note that besides the ``aiida-quantumespresso`` plugin, AiiDA comes with plugins for a range of other codes, all of which are listed in the `AiiDA plugin registry `_. - -Computer setup --------------- - -In a production environment, AiiDA would typically be running on your work station or laptop, while launching calculations -on remote high-performance compute resources that you have SSH access to. -For this reason AiiDA has the concept of a ``Computer`` to run calculations on. - -To keep things simple, Quantum ESPRESSO (together with several other *ab initio* codes) has been installed directly in the -``codes`` subfolder of your virtual machine, -and you are going to launch your first calculations on the same computer where AiiDA is installed. -Nevertheless, we're now going to set up this computer for launching calculations: - -.. code:: bash - - verdi computer setup --config computer.yml - -where ``computer.yml`` is a configuration file in the `YAML format `_) that you can :download:`download here `. This is its content: - -.. literalinclude:: include/configuration/computer.yml - -.. note:: - When used without the ``--config`` option, ``verdi computer setup`` will prompt you for the required information, just like you have seen when :ref:`setting up a profile<2019_chiba_setup_verdi_quicksetup>`. - The configuration file should work for the virtual machine that comes with this tutorial - but may need to be adapted when you are running AiiDA in a different environment, - as explained in :ref:`this appendix<2019_chiba_appendix_computer_code_setup>`. - -Finally, you need to provide AiiDA with information on how to access the ``Computer``. -For remote computers with ``ssh`` transport, this would involve e.g. an SSH key. -For ``local`` computers, this is just a "formality" (press enter to confirm the default cooldown time): - -.. code:: bash - - verdi computer configure local localhost - -.. note:: - - For remote computers with ``ssh`` transport, use ``verdi computer configure ssh`` instead of ``verdi computer configure local``. - -Your ``localhost`` computer should now show up in - -.. code:: bash - - verdi computer list - -Before proceeding, test that it works: - -.. code:: bash - - verdi computer test localhost - - -Code setup ----------- - -Now that we have our localhost set up, let's configure the ``Code``, namely the ``pw.x`` executable. -As with the computer, we have prepared a configuration file for you to :download:`download `. -This is its content: - -.. literalinclude:: include/configuration/code.yml - :language: yaml - -Once you have the configuration file in your local working environment, set up the code: - -.. code:: bash - - verdi code setup --config code.yml - -.. warning:: - - The configuration should work for the virtual machine that comes with this tutorial. - If you are following this tutorial in a different environment, you will need to install Quantum ESPRESSO and adapt the configuration to your needs, - as explained in :ref:`this appendix<2019_chiba_appendix_computer_code_setup>`. - -Similar to the computers, you can list all the configured codes with: - -.. code:: bash - - verdi code list - -Verify that it now contains a code named ``qe-6.4.1-pw`` that we just configured. -You can always check the configuration details of an existing code using: - -.. code:: bash - - verdi code show qe-6.4.1-pw - -.. note:: - - The ``generic`` profile has already a number of other codes configured. - See ``verdi -p generic code list``. - - -The AiiDA daemon ----------------- - -First of all, check that the AiiDA daemon is actually running. -The AiiDA daemon is a program that - - * runs continuously in the background - * waits for new calculations to be submitted - * transfers the inputs of new calculations to your compute resource - * checks the status of your calculation at the compute resource, and - * retrieves the results from the compute resource - -Check the status of the daemon process by typing in the terminal: - -.. code:: bash - - verdi daemon status - -If the daemon is running, the output should look like - -.. code:: bash - - Profile: default - Daemon is running as PID 2050 since 2019-04-30 12:37:12 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 2055 2.147 0 2019-04-30 12:37:12 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers - -If this is not the case, type in the terminal - -.. code:: bash - - verdi daemon start - -to start the daemon. - -Creating a new calculation --------------------------- - -In the following, we'll be working in the ``verdi shell``. -As you go along, feel free to keep track of your commands by -copying them into a python script ``test_pw.py``. - -.. note:: - - The ``verdi shell`` imports a number of AiiDA internals so that you as the user don't have to. - You can also make those available to a python script, by running it using - - .. code:: bash - - verdi run - - -Every calculation sent to a cluster is linked to a *code*, which describes -the executable file to be used as well as some metadata. -Let's have a look at the codes already installed on your machine: - -.. code:: bash - - verdi code list - -There should be a number of them. Here, we're interested in the "PWscf" executable of Quantum Espresso, i.e. in codes for the ``quantumespresso.pw`` plugin: - -.. code:: bash - - verdi code list -P quantumespresso.pw - -Pick the correct codename, that might look like, e.g. -``qe-6.4.1-pw@localhost`` and load it in the verdi shell. - -.. code:: python - - code = load_code("") - -.. note:: - - ``load_code`` returns an object of type ``Code``, which is the general AiiDA class for describing simulation codes. - -Let's build the inputs for a new ``PwCalculation`` (defined by the ``quantumespresso.pw`` plugin, the default plugin for the code you chose before) - -.. code:: python - - builder = code.get_builder() - -As the first step, assign a (short) label or a (long) description to your -calculation, that you might find convenient in the future. - -.. code:: python - - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calc with Quantum ESPRESSO on Si" - -This information will be saved in the database for later queries or -inspection. Note that you can press TAB after writing ``builder.`` to -see all inputs available for this calculation. -In order to figure out which data type is expected for a particular input, such as ``builder.structure``, -and whether the input is optional or required, use ``builder.structure??``. - -Now, specify the number of machines (a.k.a. cluster nodes) -you are going to run on and the maximum time allowed for the -calculation. -The general options grouped under ``builder.metadata.options`` are independent of -the code or plugin, and will be passed to the scheduler that handles the -queue on your compute resource . - -.. code:: python - - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - -Again, to see the list of available options, type -``builder.metadata.options.`` and hit the TAB button. - -Preparation of inputs -~~~~~~~~~~~~~~~~~~~~~ - -A Quantum Espresso calculation needs a number of inputs: - -1. `Pseudopotentials `_ -2. a structure -3. a mesh in reciprocal space (k-points) -4. a number of input parameters - -These are mirrored in the inputs of the ``aiida-quantumespresso`` plugin -(see `documentation `_). -We'll start with the structure, k-points, and pseudopotentials -and leave the input parameters as the last thing to setup. - -.. admonition:: Exercise - - Use what you learned in the previous section to load the ``structure`` and ``kpoints`` inputs for your calculation: - - * Use a silicon crystal structure - * Define a ``2x2x2`` mesh of k-points. - - Note: If you just copy and paste code that you executed previously, - this may result in duplication of information on your database. - In fact, you can re-use an existing structure stored in your database [#f1]_. Use a combination of the bash command - ``verdi data structure list`` and of the shell command ``load_node()`` - to get an object representing the structure created earlier. - -Attaching the input information to the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once you've created a ``structure`` node and a ``kpoints`` node, -attach it to the calculation: - -.. code:: python - - builder.structure = structure - builder.kpoints = kpoints - -.. note:: - - The builder accepts both *stored* and *unstored* data nodes. - AiiDA will take care of storing the unstored nodes upon submission. - If you decide not to submit, nothing will be stored in the database. - -PWscf also needs information on the pseudopotentials, -in the form of a dictionary, where keys are the names of the elements and the values are the corresponding ``UpfData`` objects containing the information on the pseudopotential. -However, instead of creating the dictionary by hand, we can use a helper function that picks the right pseudopotentials for our structure from a pseudopotential *family*. -You can list the preconfigured families from the command line: - -.. code:: bash - - verdi data upf listfamilies - -Pick the one you configured earlier (or one of the ``SSSP`` families that -we provide) and link it to the calculation using the command: - -.. code:: python - - from aiida.orm.nodes.data.upf import get_pseudos_from_structure - builder.pseudos = get_pseudos_from_structure(structure, '') - -Print the content of the `pseudos` namespace, e.g. ``print(builder.pseudos)`` to see what the helper function created. - -Preparing and debugging input parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Finally, we need to specify a number of input parameters (i.e. plane wave cutoffs, convergence thresholds, etc.). -to launch the Quantum ESPRESSO calculation. -The structure of the parameter dictionary closely follows the structure of the `PWscf input file `_. - -Since these are often the parameters to tune in a calculation, -let's **introduce a few mistakes intentionally** -and use this part of the tutorial to learn how to debug problems. - -Define a set of input parameters for Quantum ESPRESSO, preparing a -dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, - } - -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, -except for an invalid key ``mickeymouse``. When Quantum ESPRESSO -receives an unrecognized key, it will stop. -By default, the AiiDA plugin will *not* validate your input and simply pass -it on to the code. - -Let's wrap the ``parameters_dict`` python dictionary in an AiiDA ``Dict`` node and see what happens. - -.. code:: python - - builder.parameters = Dict(dict=parameters_dict) - -Simulate submission -~~~~~~~~~~~~~~~~~~~ - -At this stage, you have created in memory (it's not yet stored in the -database) the input of the graph shown below. The outputs will -be created by the daemon later on. - -.. figure:: include/images/verdi_graph/si/graph-full.png - :alt: - -In order to check which input files AiiDA creates, -we can perform a *dry run* of the submission process. -Let's tell the builder that we want a dry run and that -we don't want to store the provenance of the dry run: - -.. code:: python - - builder.metadata.dry_run = True - builder.metadata.store_provenance = False - -It's time to run: - -.. code:: python - - from aiida.engine import run - run(builder) - -.. note:: - - Instead of using the builder, you can also simply pass the calculation class - as the first argument, followed by the inputs as keyword arguments, e.g.: - - .. code:: python - - run(PwCalculation, structure=structure, pseudos={'Si': pseudo_node}, ....) - - The builder is simply a convenience wrapper providing tab-completion in the shell and automatic help strings. - -This creates a folder of the form ``submit_test/[date]-0000[x]`` in the -current directory. In your second terminal: - - * open the input file ``aiida.in`` within this folder - * compare it to input data nodes you created earlier - * verify that the `pseudo` folder contains the needed pseudopotentials - * have a look at the submission script ``_aiidasubmit.sh`` - -.. note:: - - The files created by a dry run are only intended for inspection - and cannot be used to correct the inputs of your calculation. - -Submitting the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Up to now we've just been playing around and our calculation has been kept -in memory and not in the database. -Now that we have inspected the input files and convinced ourselves that -Quantum ESPRESSO will have all the information it needs to perform the -calculation, we will submit the calculation properly. -Doing so will make sure that all inputs are stored in the database, -will run and store the calculation and link the outputs to it. - -Let's revert the following values in our builder to their defaults: - -.. code:: python - - builder.metadata.dry_run = False - builder.metadata.store_provenance = True - -And then rely on the submit machinery of AiiDA, - -.. code:: python - - from aiida.engine import submit - calculation = submit(builder) - -As soon as you have executed these lines, the ``calculation`` variable contains a ``PwCalculation`` instance, already submitted to the daemon. - -.. note:: - - You may have noticed that we used ``submit`` here instead of ``run``. - The difference is that ``submit`` will hands over the calculation to the daemon running in the background, - while ``run`` will execute all tasks in the current shell. - - All processes in AiiDA (you will soon get to know more) can be "launched" using one of available functions: - - * run - * run_get_node - * run_get_pk - * submit - - which are explained in more detail in the `online documentation `_. - - -The calculation is now stored in the database and was assigned a "database primary key" or ``pk`` (``calculation.pk``) as well as a UUID (``calculation.uuid``). -See the :ref:`previous section <2019-aiida-identifiers>` for more details on these identifiers. - -Note that while AiiDA will prevent you from changing the content of stored nodes, -the concept of "extras" allows you to set extra attributes, e.g. as a way of -labelling nodes and providing information for querying. - -For example, let's add an extra attribute called ``element``, with value ``Si``: - -.. code:: python - - calculation.set_extra("element", "Si") - -In the mean time, after you submitted your calculation, the daemon -picked it up and started to: generate the input files, submit the calculation -to the queue, wait for it to run and finish, retrieve the output files, -parse them, store them in the database and set the state of the -calculation to ``Finished``. - -.. note:: - - If the daemon is not running, the calculation will remain in the - ``NEW`` state until you start the daemon. - -Checking the status of the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can check the calculation status from the command line: - -.. code:: bash - - verdi process list - -.. note:: - - Since you are running your DFT calculation directly on the VM, - ``verdi`` commands can be a bit slow until the calculation finishes. - -If you don't see any calculation in the -output, the calculation you submitted has already finished. - -By default, the command only prints calculations that are still active [#f2]_. -Let's also list your finished calculations (and limit those only -to the one created in the past day): - -.. code:: bash - - verdi process list -a -p1 - -as explained in the first section. - -Similar to the dry run, we can also inspect the input files of the *actual* -calculation: - -.. code:: bash - - verdi calcjob inputls -c - -for the ``pk_number`` of your calculation. This will show the -contents of the input directory (``-c`` prints directories in color). -Check the content of input files with - -.. code:: bash - - verdi calcjob inputcat | less - -Troubleshooting ---------------- - -Your calculation should end up in a FAILED state -(last column of ``verdi process list -a -p1``), and correspondingly the -error code near the "Finished" status of the State should be non-zero, - -.. code:: bash - - $ verdi process list -a -p1 - PK Created Process label Process State Process status - ---- --------- --------------- ---------------- ---------------- - 2060 3m ago PwCalculation ⏹Finished [300] - ... - $ # Anything but [0] after the Finished state signals a failure - -This was expected, since we used an invalid key in the input parameters. -Situations like this happen in real life, so AiiDA provides -tools to trace back to the source of the problem and correct it. - -A first way to proceed is to inspect the output file of -PWscf. - -.. code:: bash - - verdi calcjob outputcat | less - -This might be enough to understand the reason why the calculation -failed. - -AiiDA provides further tools for troubleshooting in a more compact way. -For any calculation, both successful and failed, you can get a summary by: - -.. code:: bash - - $ verdi process show - Property Value - ----------- -------------------------------------------------------------------------------- - type PwCalculation - state Finished [300] Both the stdout and XML output files could not be read or parsed. - pk 2060 - uuid cd874e9d-94f7-4fc8-88c5-decdfc031491 - label PW test - description My first AiiDA calc with Quantum ESPRESSO on Si - ctime 2019-12-13 06:14:07.374401+00:00 - mtime 2019-12-13 06:14:19.775021+00:00 - computer [2] localhost - - Inputs PK Type - ---------- ---- ------------- - pseudos - Si 2003 UpfData - code 2056 Code - kpoints 2058 KpointsData - parameters 2059 Dict - structure 2057 StructureData - - Outputs PK Type - ----------------- ---- -------------- - output_parameters 2064 Dict - output_trajectory 2063 TrajectoryData - remote_folder 2061 RemoteData - retrieved 2062 FolderData - - Log messages - --------------------------------------------- - There are 3 log messages for this calculation - Run 'verdi process report 2060' to see them - -The last part of the output alerts you to the fact that there -are some log messages waiting for you, if you run -``verdi process report ``. - -Let's now correct our input parameters dictionary by leaving out the invalid -key and see if our calculation succeeds: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, - } - builder.parameters = Dict(dict=parameters_dict) - calculation = submit(builder) - -If you have been using the separate script approach, modify -the script to remove the faulty input and run it again with: - -.. code:: bash - - verdi run test_pw.py - -Use ``verdi process list -a -p1`` to verify that -the calculation reaches the finished status, with exit code zero. - -Using the calculation results ------------------------------ - -Now you can access the results as you have seen earlier. For example, -note down the pk of the calculation so that you can load it in the -``verdi shell`` and check the total energy with the commands: - -.. code:: python - - calculation = load_node() - calculation.res.energy - -Besides writing input files, running the software for you, storing the output -files, and connecting it all together in your provenance graph, -many AiiDA plugins will parse the output of your code and make output values -of interest available through an output dictionary node (as depicted in the -graph above). -In the case of the ``aiida-quantumespresso`` plugin this output node -is available at ``calculation.outputs.output_parameters`` and you can access -all the available attributes (not only the energy) using: - -.. code:: python - - calculation.outputs.output_parameters.attributes - -While the name of this output dictionary node can be chosen by the plugin, -AiiDA provides the "results" shortcut ``calculation.res`` -that plugin developers can use to provide what they consider the result of the -calculation. - - -.. rubric:: Footnotes - -.. [#f1] In order to avoid duplication of KpointsData, you would first need to learn how to query the database, therefore we will ignore this issue for now. -.. [#f2] A process is considered active if it is either ``Created``, ``Running`` or ``Waiting``. If a process is no longer active, but terminated, it will have a state ``Finished``, ``Killed`` or ``Excepted``. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/first_taste.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/first_taste.rst deleted file mode 100644 index 79192f88..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/first_taste.rst +++ /dev/null @@ -1,262 +0,0 @@ -A first taste -============= - -Let's start with a quick demo of how AiiDA can make your life easier as a computational scientist. - -To get started, type ``workon aiida`` in your terminal to enter the *virtual environment* where AiiDA is installed. -You have entered the the virtual environment when the prompt starts with ``(aiida)``, e.g.:: - - (aiida) username@hostname:~$ - -.. note:: - - You need to retype ``workon aiida`` whenever you open a new terminal. - -We'll be using the ``verdi`` command-line interface, -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - -Here are some first tasks for you: - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on any ``verdi`` command, simply append the ``-h`` or ``--help`` flag: - - .. code:: bash - - verdi -h - -Getting help ------------- - -There are a number of helpful resources available to you for getting more information about AiiDA. -Please consider: - - * consulting the extensive `AiiDA documentation `_ - * asking in the `Slack channel of the tutorial `_ - * asking your neighbor - * asking a tutor - * opening a new issue on the `tutorial issue tracker `_ - -Importing a structure and running a calculation ------------------------------------------------ - -Let's download a structure from the `Crystallography Open Database `_ and import it into AiiDA: - -.. code:: bash - - wget http://crystallography.net/cod/9008565.cif - verdi data structure import ase 9008565.cif - - -Each piece of data in AiiDA gets a PK number (a "primary key") that identifies it in your database. -The PK is printed to screen by the ``verdi data structure import`` command. -**Mark down the PK for your structure and use it to replace the placeholders in what follows.** - -.. tip:: - - You can view the structure either `online `_ - or by executing the ``jmol 9008565.cif`` command on the virtual machine (or preferably on your local machine for better performance in case that ``jmol`` is installed). - -.. Let jason/jianxing test speed of SSH forwarding - potentially mention jupyter - -The following short python script sets up a self-consistent field calculation for the Quantum ESPRESSO code: - -.. literalinclude:: include/snippets/demo_calcjob.py - -Download the :download:`demo_calcjob.py ` script using ``wget`` to your working directory. -It contains a few placeholders for you to fill in: - - #. The VM already has a number of codes preconfigured. Use ``verdi code list`` to find the label for the "PW" code and use it in the script. - #. Replace the PK of the structure with the one you noted down earlier. - #. The VM already a number of pseudopotential families installed. - Replace the PP family name with the one for the "SSSP efficiency" library found via ``verdi data upf listfamilies``. - -Then submit the calculation using: - -.. code:: bash - - verdi run demo_calcjob.py - -From this point onwards, the AiiDA daemon will take care of your calculation: creating the necessary input files, running the calculation, and parsing its results. -It should take less than one minute to complete. - -Analyzing the outputs of a calculation --------------------------------------- - -Let's have a look how your calculation is doing: - -.. code:: bash - - verdi process list # shows only running processes - verdi process list --all # shows all processes - -Again, your calculation will get a PK, which you can use to get more information on it: - -.. code:: bash - - verdi process show - -As you can see, AiiDA has tracked all the inputs provided to the calculation, allowing you (or anyone else) to reproduce it later on. -AiiDA's record of a calculation is best displayed in the form of a provenance graph - -.. figure:: include/images/demo_calc.png - :width: 100% - - Provenance graph for a single Quantum ESPRESSO calculation. - -You can generate such a provenance graph for any calculation or data in AiiDA by running: - -.. code:: bash - - verdi node graph generate - -Try to reproduce the figure using the PK of your calculation. - -.. note:: - - By default, AiiDA uses UUIDs to label nodes in provenance graphs (more about UUIDs vs PKs later). - Try using the ``-h`` option to figure out how to switch to the PK identifier. - - -You might wonder what happened under the hood, e.g. where to find the actual input and output files of the calculation. -You will learn more about this later -- until then, here are a few useful to try: - -.. code:: bash - - verdi calcjob inputcat # shows the input file of the calculation - verdi calcjob outputcat # shows the output file of the calculation - verdi calcjob res # shows the parsed output - -A few questions you could answer using these commands (optional) - * How many atoms did the structure contain? - How many electrons? - * How many k-points were specified? How many k-points were actually computed? Why? - * How many SCF iterations were needed for convergence? - * How long did Quantum ESPRESSO actually run (wall time)? - -.. tip:: - - Use the ``grep`` command to filter the terminal output by keywords, e.g., ``verdi calcjob res 175 | grep wall_time``. - - -From calculations to workflows ------------------------------- - -AiiDA can help you run individual calculations but it is really designed to help you run workflows that involve several calculations, while automatically keeping track of the provenance for full reproducibility. -As the final step, let's actually run such a workflow. - -Let's have a look at the workflows that are currently installed: - -.. code:: bash - - verdi plugin list aiida.workflows - - -The ``quantumespresso.pw.band_structure`` workflow from the `aiida-quantumespresso `_ plugin computes the electronic band structure for a given atomic structure. -Let AiiDA tell you which inputs it takes and which outputs it produces: - -.. code:: bash - - verdi plugin list aiida.workflows quantumespresso.pw.band_structure - -.. literalinclude:: include/snippets/demo_bands.py - -Download the :download:`demo_bands.py ` snippet, replace the PK, and run it using - -.. code:: bash - - verdi run demo_bands.py - -This workflow will: - - #. Determine the primitive cell of the input structure - #. Run a calculation on the primitive cell to relax both the cell and the atomic positions (``vc-relax``) - #. Refine the symmetry of the relaxed structure, and find a standardised primitive cell using SeeK-path_ - #. Run a self-consistent field calculation on the refined structure - #. Run a band structure calculation at fixed Kohn-Sham potential along a standard path between high-symmetry k-points determined by SeeK-path_ - -The workflow uses the PBE exchange-correlation functional with suitable pseudopotentials and energy cutoffs from the `SSSP library version 1.1 `_. - - -.. _SeeK-path: https://www.materialscloud.org/work/tools/seekpath - -.. K-point mesh is selected to have a minimum k-point density of 0.2 Å-1 - A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations - -The workflow should take ~10 minutes on your virtual machine. -You may notice that ``verdi process list`` now shows more than one entry. -While you wait for the workflow to complete, -let's start exploring its provenance. - -The full provenance graph obtained from ``verdi node graph generate`` will already be rather complex (you can try!), -so let's try browsing the provenance interactively instead. - -Start the AiiDA REST API: - -.. code:: bash - - verdi restapi - -and open the |provenance browser|. If you can't see any useful -information, please ask to tutors, because restapi v4 may have some -issue on the day of this tutorial. - -.. |provenance browser| raw:: html - - Materials Cloud provenance browser - -.. note:: - - In order for the provenance browser to work, you need to configure SSH to tunnel port 5000 from your VM to your local laptop (see here :ref:`2019_chiba_connect`). - - -The provenance browser is a Javascript application that connects to the AiiDA REST API. -Your data never leaves your computer. - -.. some general comment on importance of the graph? -.. a sentence on how to continue from here - -Browse your AiiDA database. - * Start by finding your structure in Data => StructureData - * Use the provenance browser to explore the steps of the ``PwBandStructureWorkChain`` - -.. note:: - - When perfoming calculations for a publication, you can export your provenance graph using ``verdi export create`` and upload it to the `Materials Cloud Archive `_, enabling your peers to explore the provenance of your calculations online. - -Once the workchain is finished, use ``verdi process show `` to inspect the ``PwBandStructureWorkChain`` and find the PK of its ``band_structure`` output. -Use this to produce a PDF of the band structure: - -.. code:: bash - - verdi data bands export --format mpl_pdf --output band_structure.pdf - - -.. figure:: include/images/si_bands.png - :width: 80% - - Band structure computed by the `PwBandStructure` workchain. - -.. note:: - The ``BandsData`` node does contain information about the Fermi energy, so the energy zero in your plot will be arbitrary. - You can produce a plot with the Fermi energy set to zero (as above) using the following code in a jupyter notebook: - - .. code:: ipython - - %aiida - %matplotlib inline - - scf_params = load_node() # REPLACE with PK of "scf_parameters" output - fermi_energy = scf_params.dict.fermi_energy - - bands = load_node() # REPLACE with PK of "band_structure" output - bands.show_mpl(y_origin=fermi_energy, plot_zero_axis=True) - - - -What next? ----------- - -You now have a first taste of the type of problems AiiDA tries to solve. -Now let's continue with the in-depth tutorial and learn more about the ``verdi``, ``verdi shell`` and ``python`` interfaces to AiiDA. -Please follow the link to the :ref:`2019_chiba_verdi_cli` section. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/code.yml b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/code.yml deleted file mode 100644 index e9bdaad2..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/code.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" -on_computer: true -computer: "localhost" -remote_abs_path: "/usr/local/bin/pw.x" -prepend_text: "ulimit -s unlimited" -append_text: " " diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/computer.yml b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/computer.yml deleted file mode 100644 index 61dedbc7..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/computer.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -description: "localhost" -label: "localhost" -hostname: "localhost" -transport: local -scheduler: "direct" -work_dir: "/home/max/.aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "1" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/neighbor-config.yml b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/neighbor-config.yml deleted file mode 100644 index ae7a0c26..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/neighbor-config.yml +++ /dev/null @@ -1,6 +0,0 @@ ---- -username: compute -key_filename: /home/max/.ssh/aiida-tutorial-compute.pem -timeout: 30 # timout for SSH before giving up -key_policy: AutoAddPolicy # add to known hosts automatically -safe_interval: 5 # Minimum time (s) between consecutive connections diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/neighbor.yml b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/neighbor.yml deleted file mode 100644 index 9c311820..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/neighbor.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -label: neighbor -description: Your neighbor's VM. -hostname: IP_ADDRESS # << REPLACE -transport: ssh # connects via SSH -scheduler: slurm # use SLURM scheduler -work_dir: "/tmp/aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/configuration/qe.yml 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a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/images/verdi_graph/graph.svg +++ /dev/null @@ -1,2678 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - 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a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/demo_bands.py b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/demo_bands.py deleted file mode 100644 index 57eaa03a..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/demo_bands.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Compute a band structure with Quantum ESPRESSO - -Uses the PwBandStructureWorkChain provided by aiida-quantumespresso. -""" -from aiida.engine import submit -PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure') - -results = submit( - PwBandStructureWorkChain, - code=Code.get_from_string("qe-6.4.1-pw@localhost"), - structure=load_node(), # REPLACE -) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/demo_calcjob.py b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/demo_calcjob.py deleted file mode 100644 index 42bd55ac..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/demo_calcjob.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Run SCF calculation with Quantum ESPRESSO""" -from aiida.orm.nodes.data.upf import get_pseudos_from_structure -from aiida.engine import submit - -code = Code.get_from_string('') # REPLACE -builder = code.get_builder() - -# Select structure -structure = load_node() # REPLACE -builder.structure = structure - -# Define calculation -parameters = { - 'CONTROL': { - 'calculation': 'scf', # self-consistent field - }, - 'SYSTEM': { - 'ecutwfc': 30., # wave function cutoff in Ry - 'ecutrho': 240., # density cutoff in Ry - }, -} -builder.parameters = Dict(dict=parameters) - -# Select pseudopotentials -builder.pseudos = get_pseudos_from_structure(structure, '') # REPLACE - -# Define K-point mesh in reciprocal space -KpointsData = DataFactory('array.kpoints') -kpoints = KpointsData() -kpoints.set_kpoints_mesh([4,4,4]) -builder.kpoints = kpoints - -# Set resources -builder.metadata.options.resources = {'num_machines': 1} - -# Submit the job -calcjob = submit(builder) -print('Submitted CalcJob with PK=' + str(calcjob.pk)) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_create_diamond_fcc.py b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_create_diamond_fcc.py deleted file mode 100644 index 0ad3d483..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_create_diamond_fcc.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import absolute_import -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - try: - symbol = element.value - except AttributeError: - symbol = str(element) - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_create_rescaled.py b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_create_rescaled.py deleted file mode 100644 index c7d0a617..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_create_rescaled.py +++ /dev/null @@ -1,11 +0,0 @@ -from __future__ import absolute_import -from aiida.engine import workfunction - - -@workfunction -def create_rescaled(element, scale): - """Workfunction to create and immediately rescale a crystal structure of a given element.""" - structure = create_diamond_fcc(element) - rescaled = rescale(structure, scale) - - return rescaled diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_rescale.py b/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_rescale.py deleted file mode 100644 index 99761a77..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/include/snippets/workflows_rescale.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import absolute_import -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/querybuilder.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/querybuilder.rst deleted file mode 100644 index 67c02145..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/querybuilder.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _2019_chiba_querybuilder: - -Queries in AiiDA: The Querybuilder -================================== - -This part of the tutorial is provided only in interactive mode through a -Jupyter notebook, which you will be able to run in your browser. -For instructions on starting the Jupyter server, please refer to the -:ref:`setup section`. Once the server is running, -:download:`download this tutorial notebook <../notebooks/querybuilder-tutorial.ipynb>` -and open it in Jupyter. For instructions on downloading these files on -a machine through which you are connected through SSH, refer to -:ref:`this section`. - -The notebook will show you how the ``QueryBuilder`` can be used to query your -database for specific data. It will demonstrate certain concepts and then ask -you to use those to perform certain queries on your own database. Some of these -question cells will have partial solutions that you will have to complete. - -Once you have finished the notebook, you can download a -:download:`notebook with the solutions <../notebooks/querybuilder-solutions.ipynb>` -but try not to use them at first! - -See below for a rendered version of the notebook: - -.. toctree:: - - QueryBuilder Notebook <../notebooks/querybuilder-tutorial.ipynb> diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/setup.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/setup.rst deleted file mode 100644 index 8b75ef0d..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/setup.rst +++ /dev/null @@ -1,201 +0,0 @@ -Getting set up -============== - -Choice of web browser ---------------------- - -During the use of AiIDA infrastructure, we sometimes or often use web -browser. The recommended browser is Chrome or Firefox. Safari on macOS -is not recommended. - -.. _2019_chiba_connect: - -Connect to your virtual machine -------------------------------- - -The steps below explain how to connect to your personal Quantum Mobile -virtual machine (VM) on Virtualbox using the `Secure Shell -`_ protocol. The software -on this VM already includes a pre-configured AiiDA installation as -well as some test data for the tutorial. It's recommended for you to -place the ssh key you will create in a folder dedicated to your ssh -configuration, to do so: - -- If not already present, create a ``.ssh`` directory in your home - (``mkdir ~/.ssh``), and set its permissions: ``chmod 700 ~/.ssh`` -- Generate private and pubilc ssh key pair by ``ssh-keygen -f - aiida_tutorial``. -- Copy the public key to VM by ``ssh-copy-id -i - ~/.ssh/aiida_tutorial.pub max@127.0.0.1 -p 2222`` and type the - default password ``moritz``. - - -Linux and MacOS -~~~~~~~~~~~~~~~ - -After the ssh key files are in place, add the following block to your -``~/.ssh/config`` file: - -.. code:: bash - - Host aiidatutorial - Hostname 127.0.0.1 - Port 2222 - User max - IdentityFile ~/.ssh/aiida_tutorial - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - ServerAliveInterval 120 - -Afterwards you can connect to VM using this simple command: - -.. code:: console - - ssh aiidatutorial - -.. note:: - - Here's a copy-paste ready command for you to use directly with zero - configuration: - - .. code:: console - - ssh \ - -i ~/.ssh/aiida_tutorial \ - -L 8888:localhost:8888 \ - -L 5000:localhost:5000 \ - -o ServerAliveInterval=120 \ - -X -C \ - max@127.0.0.1 -p 2222 - -.. note:: - - On MacOS you need to install `XQuartz - `_ in order to use - X-forwarding. - -Windows 10 -~~~~~~~~~~ - -If you're running Windows 10, it is recommended to consider -`installing the Windows Subsystem for Linux (WSL) -`_ (and -then follow the instructions above for linux). `VcXsrv -`_ is also installed with -WSL in order to use X-forwarding. Some WSL setup (networking and -X-forwarding) is written `here -`_. -Short summary of the configuration is as follows. After the ssh key -files are in place, add the following block to your ``~/.ssh/config`` -file: - -.. code:: bash - - Host aiidatutorial - Hostname 127.0.0.1 - Port 2222 - User max - IdentityFile ~/.ssh/aiida_tutorial - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - RemoteForward 6010 localhost:6000 - ServerAliveInterval 120 - -``RemoteForward`` is for X-forwarding with VcXsrv. In this -configuration, in WSL - -.. code:: bash - - $ echo 'export DISPLAY=:0.0' >> ~/.bashrc - -and in Quantum Mobile VM - -.. code:: bash - - $ echo 'export DISPLAY=:10.0' >> ~/.bashrc - -.. _2019_chiba_setup_jupyter: - -Start jupyter -------------- - -Once connected to your virtual machine, type in the remote terminal - -.. code:: bash - - workon aiida - -This will enable the virtual environment in which AiiDA is installed, -allowing you to use AiiDA. Now type in the same shell: - -.. code:: bash - - jupyter notebook --no-browser - -This will run a server with a web application called ``jupyter``, -which is used to create interactive python notebooks. In order to -connect to the jupyter notebook server: - - - Copy the URL that has been printed to the terminal (similar to - ``http://localhost:8888/?token=2a3ba37cd1...``). - - Open a web browser **on your laptop** and paste the URL. - - You will see a list of folders on your personal VM. - -While keeping the first ``ssh`` connection running, open a second -``ssh`` connection in a separate terminal and execute ``workon aiida`` -there as well. We will use the second terminal to directly interact -with the virtual machine on the command line, while we use the first -one to only serve the jupyter notebook. - -.. note:: - - You can safely ignore all warnings related to port forwarding when - opening a second ssh connection. Those are caused by the fact - that the ports are now already in use which in this context is - perfectly fine. - - -.. _2019_chiba_setup_downloading_files: - -Downloading files ------------------ - -Throughout this tutorial, you will encounter links to download python -scripts, jupyter notebooks and more. These files should be downloaded -to the environment/working directory you use to run the tutorial. In -particular, when running the tutorial on a Linux VM, copy the link -address and download the files to the machine using ``wget`` in the -terminal: - -.. code:: bash - - wget - -where you replace ```` with the actual HTTPS URL copied from the -tutorial text in your browser. This will download the file to the -current directory. - - -Troubleshooting ---------------- - -- If you encounter errors such as ``ImportError: No module named - aiida`` or ``No command ’verdi’ found``, double check that you - have loaded the virtual environment with ``workon aiida`` before - launching ``python``, ``ipython`` or the ``jupyter`` notebook - server. Your command line prompt should start with ``(aiida)``, - e.g., ``(aiida) max@workhorse:~$``. - -- If your browser cannot connect to the jupyter notebook server, check - that you have correctly configured SSH tunneling/forwarding as - described above. Keep in mind that you need to start the jupyter - server from the terminal connected to the VM, while the web browser - should be opened locally on your laptop. - -- See the `jupyter notebook documentation - `_ - for compatibility of jupyter with various web browsers. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/verdi_cmdline.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/verdi_cmdline.rst deleted file mode 100644 index 6800eec2..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/verdi_cmdline.rst +++ /dev/null @@ -1,542 +0,0 @@ -.. _2019_chiba_verdi_cli: - -Verdi command line -================== - -This part of the tutorial will familiarize you with the ``verdi`` command-line interface (CLI), -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``-h/--help`` flag, e.g.: - - .. code:: bash - - verdi quicksetup -h - -More details on ``verdi`` can be found in the `online documentation `_. - - -.. _2019_chiba_setup_verdi_quicksetup: - -Setting up a profile --------------------- - -After installing AiiDA, the first step is to create a "profile". -Typically, you would be using one profile per independent research project. - -The easiest way of setting up a new profile is through ``verdi quicksetup``. -Let's set up a new profile that we will use throughout this tutorial: - -.. code:: bash - - verdi quicksetup - -This will prompt you for some information, such as the name of the profile, your name, etc. -The information about you as a user will be associated with all the data that you create in AiiDA -and is important for attribution, when you start sharing your data with others. -After you have answered all the questions, a new profile will be created, along -with the required database and repository. - -.. note:: - - ``verdi quicksetup`` is a user-friendly wrapper around the ``verdi setup`` command that provides more control over the profile setup. - As explained in `the documentation `_, ``verdi setup`` expects certain external resources, such as the database to already have been pre-configured. - ``verdi quicksetup`` will try to do this for you, but may not be successful in certain environments. - -To see this profile, and any others that may have been configured, run: - -.. code:: bash - - verdi profile list - - Info: configuration folder: /home/max/.aiida - * generic - quicksetup - -Each line, ``generic`` and ``quicksetup`` in this example, corresponds to a profile. -The one marked with an asterisk is the "default" profile, meaning that any ``verdi`` command that you execute will be applied to that profile. - -.. note:: - - The output you get may differ. - The ``generic`` profile is pre-configured on the virtual machine and built only for the first part of the tutorial. - -Let's change the default profile to the newly created ``quicksetup`` for the rest of the tutorial: - -.. code:: bash - - verdi profile setdefault quicksetup - -From now on, all ``verdi`` commands will apply to the ``quicksetup`` profile. - -.. note:: - - To quickly perform a single command on a profile that is not the default, use the ``-p/--profile`` option: - For example, ``verdi -p generic code list`` will display the codes for the ``generic`` profile, despite it not being the current default profile. - - -Importing data --------------- - -Before we start running calculations ourselves, we are going to look at an AiiDA database already created by someone else. -Let's import one from the web (due to slow network from Japan to -Europe, we temporarily put the aiida data on Kyoto university web server): - -.. code:: bash - - verdi import http://phonondb.mtl.kyoto-u.ac.jp/aiida_tutorial/aiida_tutorial_2019_05_perovskites_v0.3.aiida - -But the original data is found here: - -.. code:: bash - - https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2019_05_perovskites_v0.3.aiida - -Contrary to most databases, AiiDA databases contain not only *results* of calculations but also their inputs and information on how a particular result was obtained. -This information, the *data provenance*, is stored in the form of a *directed acyclic graph* (DAG). -In the following, we are going to introduce you to different ways of browsing this graph and will ask you to find out some information regarding the database you just imported. - -.. _2019_chiba_aiidagraph: - -Your first AiiDA graph ----------------------- - -:numref:`2019_chiba_fig_graph_input_only` shows a typcial example of a calculation represented in an AiiDA graph. -Have a look to the figure and its caption before moving on. - -.. _2019_chiba_fig_graph_input_only: -.. figure:: include/images/verdi_graph/batio3/graph-input.png - :width: 100% - - Graph with all inputs (data, circles; and code, diamond) to the Quantum ESPRESSO calculation (square) that you will create in the :ref:`calculations` section of this tutorial. - -.. _2019_chiba_fig_graph: -.. figure:: include/images/verdi_graph/batio3/graph-full.png - :width: 100% - - Same as :numref:`2019_chiba_fig_graph_input_only`, but also with the outputs that the engine will create and connect automatically. - The ``RemoteData`` node is created during submission and can be thought as a symbolic link to the remote folder in which the calculation runs on the cluster. - The other nodes are created when the calculation has finished, after retrieval and parsing. - The node with linkname 'retrieved' contains the raw output files stored in the AiiDA repository; all other nodes are added by the parser. - Additional nodes (symbolized in gray) can be added by the parser (e.g. an output ``StructureData`` if you performed a relaxation calculation, a ``TrajectoryData`` for molecular dynamics etc.). - -:numref:`2019_chiba_fig_graph_input_only` was created manually but you can generate a similar graph automatically by passing the **identifier** of a calculation node to ``verdi node graph generate ``. -Identifiers in AiiDA come in three forms: - - * "Primary Key" (PK): An integer, e.g. ``723``, that identifies your entity within your database (automatically assigned) - * `Universally Unique Identifier `_ (UUID): A string, e.g. ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` that identifies your entity globally (automatically assigned) - * Label: A human-readable string, e.g. ``test_qe_calculation`` (manually assigned) - -Any ``verdi`` command that expects an identifier will accept a PK, a UUID or a label (although not all entities have a label by default). -While PKs are often shorter than UUIDs and can be easier to remember, they are only unique within your database. -**Whenever you intend to share your data with others, use UUIDs to refer to nodes.** - -.. note:: - For UUIDs, it is sufficient to specify a subset (starting at the beginning) as long as it can already be uniquely resolved. - For more information on identifiers in ``verdi`` and AiiDA in general, see the `documentation `_. - -For the remainder of this section, fields enclosed in angular brackets, such as ````, are placeholders that you should replace before executing the command. -With that in mind, let's generate a graph for the calculation node with UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``: - -.. code:: bash - - verdi node graph generate - -This command will create a file with the naming scheme ``.dot.pdf`` that can be viewed with any PDF document viewer. -You can open this file on the Materials Cloud VM by using ``evince`` or, if the ssh connection is too slow, copy it via ``scp`` to your local machine. -To do so, if you are using Linux/Mac OS X, you can type in your *local* machine: - -.. code:: bash - - scp aiidatutorial: - -and then open the file. - - -The provenance browser ----------------------- - -While the ``verdi`` CLI provides full access to the data underlying the provenance graph, a more intuitive tool for browsing AiiDA graphs is the interactive provenance browser available on `Materials Cloud `__. - -In order to use it, we first need to start the `AiiDA REST API `_: - -.. code:: bash - - verdi restapi - * Serving Flask app "aiida.restapi.run_api" (lazy loading) - * Environment: production - WARNING: Do not use the development server in a production environment. - Use a production WSGI server instead. - * Debug mode: off - * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) - -Now you can connect the provenance browser to your local REST API: - -- Open the |provenance_browser| on your laptop -- In the form, paste the (local) URL ``http://127.0.0.1:5000/api/v4`` - of our REST API -- Click "GO!" - -.. |provenance_browser| raw:: html - - provenance explorer - -If you can't see any useful information, please ask to tutors, because -restapi v4 may have some issue on the day of this tutorial. - -Once the provenance browser javascript application has been loaded by your browser, it is communicating directly with the REST API and your data never leaves your computer. - -.. note:: - In order for this to work on your laptop, while the REST API is running on the virtual machine, we've enabled SSH tunneling for port ``5000`` in :ref:`2019_chiba_connect`. - -Start by clicking on the Details of a ``CalcJobNode`` and use the graph explorer to complete the exercise below. -If you ever get lost, just go to the "Details" tab, enter ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` and click on the "GO" button. - -.. admonition:: Exercise - - Use the provenance browser in order to figure out: - - - When was the calculation run and who run it? - - Was it a serial or a parallel calculation? How many MPI processes were used? - - What inputs did the calculation take? - - What code was used and what was the name of the executable? - - How many calculations were performed using this code? - - -Processes ---------- - -Anything that 'runs' in AiiDA, be it calculations or workflows, is considered a ``Process``. -To get a list of currently running processes, use: - -.. code:: bash - - verdi process list - -.. note:: - - The first time you run this command, it might take a few seconds. - Subsequent calls will be faster. - -which should be empty: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - - Total results: 0 - - Info: last time an entry changed state: never - -Let's see whether there are any *finished* processes in the database by passing the ``-S/--process-state`` flag: - -.. code:: bash - - verdi process list -S finished - -This command will list all the processes that have a process state ``Finished`` and should look something like: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- --------------- --------------- ---------------- - 648 1872D ago PwCalculation ⏹ Finished [0] - 1333 1872D ago PwCalculation ⏹ Finished [0] - 1273 1872D ago PwCalculation ⏹ Finished [0] - 971 1872D ago PwCalculation ⏹ Finished [0] - 488 1872D ago PwCalculation ⏹ Finished [0] - 1869 1872D ago PwCalculation ⏹ Finished [0] - ... - - Total results: 171 - - Info: last time an entry changed state: never - -Processes can be in any of the following states: - - * ``Created`` - * ``Waiting`` - * ``Running`` - * ``Finished`` - * ``Excepted`` - * ``Killed`` - -The first three states are 'active' states, meaning the process is not done yet, and the last three are 'terminal' states. -Once a process is in a terminal state, it will never become active again. -The `official documentation `_ contains more details on process states. - -In order to list processes of *all* states, use the ``-a/--all`` flag: - -.. code:: bash - - verdi process list -a - -This command will list all the processes that have *ever* been launched. -As your database will grow, so will the output of this command. -To limit the number of results, you can use the ``-p/--past-days `` option, that will only show processes that were created ``NUM`` days ago. -For example, this lists all processes launched since yesterday: - -.. code:: bash - - verdi process list -a -p1 - -.. _2019-aiida-identifiers: - -Each row of the output identifies a process with some basic information about its status. -For a more detailed list of properties, you can use ``verdi process show``, but to address any specific process, you need an identifier for it. - -Let's revisit the process with the UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``, this time using the CLI: - -.. code:: bash - - verdi process show ce81c420-7751-48f6-af8e-eb7c6a30cec - -Producing the output: - -.. code:: bash - - Property Value - ----------- ------------------------------------ - type PwCalculation - state Finished [0] - pk 630 - uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 - label - description - ctime 2014-10-27 17:51:21.781045+00:00 - mtime 2019-05-09 14:10:09.307986+00:00 - computer [1] daint - - Inputs PK Type - ---------- ---- ------------- - pseudos - Ba 1092 UpfData - O 1488 UpfData - Ti 1855 UpfData - code 631 Code - kpoints 498 KpointsData - parameters 629 Dict - settings 500 Dict - structure 1133 StructureData - - Outputs PK Type - ----------------------- ---- ------------- - output_kpoints 1455 KpointsData - output_parameters 789 Dict - output_structure 788 StructureData - output_trajectory_array 790 ArrayData - remote_folder 1811 RemoteData - retrieved 787 FolderData - -You can use the PKs shown for the inputs and outputs to get more information about those nodes. - -.. warning:: - - Since the inputs and outputs are ``Data`` nodes, not ``Process`` nodes, use ``verdi node show`` instead. - - -Dict and CalcJobNode -~~~~~~~~~~~~~~~~~~~~ - -Let's investigate some of the nodes appearing in the graph. -From the inputs of the process, let's choose the node of type ``Dict`` with input link name ``parameters`` and type in the terminal: - -.. code:: bash - - verdi data dict show - -where ```` is the PK of the node. - -A ``Dict`` contains a dictionary (i.e. key–value pairs), stored in the database in a format ready to be queried. -We will learn how to run queries later on in this tutorial. -The command above will print the content dictionary, containing the parameters used to define the input file for the calculation. -You can compare the dictionary with the content of the raw input file to Quantum ESPRESSO (that was generated by AiiDA) via the command: - -.. code:: bash - - verdi calcjob inputcat - -where you provide the identifier of the calculation node. -Check the consistency of the parameters written in the input file and those stored in the ``Dict`` node. -Even if you don't know the meaning of the input flags of a Quantum ESPRESSO calculation, you should be able to see how the input dictionary has been converted to Fortran namelists. - -The previous command just printed the content of the 'default' input file ``aiida.in``. -To see a list of all the files used to run a calculation (input file, submission script, etc.) instead type: - -.. code:: bash - - verdi calcjob inputls - -Adding a ``--color`` flag allows you to easily distinguish files from folders by a different coloring. -Once you know the name of the file you want to visualize, you can call the ``verdi calcjob inputcat [PATH]`` command specifying the path. -For instance, to see the submission script, you can do: - -.. code:: bash - - verdi calcjob inputcat _aiidasubmit.sh - -StructureData -~~~~~~~~~~~~~ - -Now let us focus on ``StructureData`` objects, which represent a crystal structure. -We can consider for instance the input structure to the calculation we were considering before (it should have the UUID ``3a4b1270``). -Such objects can be inspected interactively by means of an atomic viewer such as the one provided by ``ase``. -AiiDA however supports several other viewers such as ``xcrysden``, ``jmol``, and ``vmd``. -Type in the terminal: - -.. code:: bash - - verdi data structure show --format ase - -to show the selected structure, although it will take a few seconds to appear. -You should be able to rotate the view with the right mouse button. - -.. note:: - - If you receive some errors, make sure you started your SSH connection with the ``-X`` or ``-Y`` flag. - -Alternatively -- especially if viewing the structure over SSH is too slow -- you can export the content of a structure node in various formats, such as ``xyz`` or ``xsf``, and then view them on your local machine. -For example, to view the file with the ``xcrysden`` application, first export the structure node in the ``xsf`` format: - -.. code:: bash - - # verdi data structure export --format xsf > .xsf - verdi data structure export --format xsf 254e5a86 > 254e5a86.xsf - -Then **copy** the ``xsf``-file to your local machine and open it with the ``xcrysden`` application: - -.. code:: bash - - xcrysden --xsf 254e5a86.xsf - -.. note:: - - You will need to have the ``xcrysden`` application installed on your local machine for this to work. - -Codes and computers -~~~~~~~~~~~~~~~~~~~ - -Let us focus now on the nodes of type ``Code``. -A code represents (in the database) the actual executable used to run the calculation. -Find the identifier of such a node in the graph and type: - -.. code:: bash - - verdi code show - -The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc. -To show a list of all available codes type: - -.. code:: bash - - verdi code list - -If you want to show all codes, including hidden ones and those created by other users, use ``verdi code list -a -A``. -Now, among the entries of the output you should also find the code just shown. - -Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command: - -.. code:: bash - - verdi computer list -a - -The ``-a`` flag shows all computers, also the one imported in your database but that you did not configure, i.e. to which you don't have access. -Details about each computer can be obtained by the command: - -.. code:: bash - - verdi computer show - -Now you have the tools to answer the question: what is the scheduler installed on the computer where the calculations of the graph have run? - -Calculation results -~~~~~~~~~~~~~~~~~~~ - -The results of a calculation can be accessed directly from the calculation node. -Type in the terminal: - -.. code:: bash - - verdi calcjob res - -which will print the output dictionary of the 'scalar' results parsed by AiiDA at the end of the calculation. -Note that this is actually a shortcut for: - -.. code:: bash - - verdi data dict show - -where ``IDENTIFIER`` refers to the ``Dict`` node attached as an output of the calculation node, with link name ``output_parameters``. -By looking at the output of the command, what is the Fermi energy of the calculation with UUID ``ce81c420``? - -Similarly to what you did for the calculation inputs, you can access the output files via the commands: - -.. code:: bash - - verdi calcjob outputls - -and - -.. code:: bash - - verdi calcjob outputcat - -Use the latter to verify that the Fermi energy that you have found in the last step has been extracted correctly from the output file - -.. hint:: - - Filter the lines containing the string 'Fermi', e.g. using ``grep``, to isolate the relevant lines. - -The results of calculations are stored in two ways: ``Dict`` objects are stored in the database, which makes querying them very convenient, whereas ``ArrayData`` objects are stored on the disk. -Once more, use the command ``verdi data array show `` to determine the Fermi energy obtained from calculation with the UUID ``ce81c420``. -This time you will need to use the identifier of the output ``ArrayData`` of the calculation, with link name ``output_trajectory_array``. -As you might have realized the difference now is that the whole series of values of the Fermi energy calculated after each relax/vc-relax step are stored. -The choice of what to store in ``Dict`` and ``ArrayData`` nodes is made by the parser of ``pw.x`` implemented in the ``aiida-quantumespresso`` plugin. - -*(Optional section)* Comments -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -AiiDA offers the possibility to attach comments to a any node, in order to be able to remember more easily its details. -Node with UUID prefix ``ce81c420`` should have no comments, but you can add a very instructive one by typing in the terminal: - -.. code:: bash - - verdi node comment add "vc-relax of a BaTiO3 done with QE pw.x" -N - -Now, if you ask for a list of all comments associated to that calculation by typing: - -.. code:: bash - - verdi node comment show - -the comment that you just added will appear together with some useful information such as its creator and creation date. -We let you play with the other options of ``verdi comment`` command to learn how to update or remove comments. - -AiiDA groups of calculations ----------------------------- - -In AiiDA, calculations (and more generally nodes) can be organized in groups, which are particularly useful to assign a set of calculations or data to a common project. -This allows you to have quick access to a whole set of calculations with no need for tedious browsing of the database or writing complex scripts for retrieving the desired nodes. -Type in the terminal: - -.. code:: bash - - verdi group list -a -A - -to show a list of **all** groups that exist in the database. -Choose the PK of the group named ``tutorial_pbesol`` and look at the calculations that it contains by typing: - -.. code:: bash - - verdi group show - -In this case, we have used the name of the group to organize calculations according to the pseudopotential that has been used to perform them. -Among the rows printed by the last command you will be able to find the calculation we have been inspecting until now. - -If instead you wanted to know all the groups to which a specific node belongs, use the ``-N/--node`` option: - -.. code:: bash - - verdi group list -N diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/verdi_shell.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/verdi_shell.rst deleted file mode 100644 index 5377c505..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/verdi_shell.rst +++ /dev/null @@ -1,400 +0,0 @@ -Verdi shell and AiiDA objects -============================= - -In this section we will use an interactive IPython environment with all the basic AiiDA classes already loaded. -We propose two realizations of such a tool. -The first consists of a special IPython shell where all the AiiDA classes, methods and functions are accessible. -Type in the terminal - -.. code:: bash - - verdi shell - -For all the everyday AiiDA-based operations, i.e. creating, querying, and using AiiDA objects, the ``verdi shell`` is probably the best tool. -In this case, we suggest that you use two terminals, one for the ``verdi shell`` and one to execute bash commands. - - -The second option is based on Jupyter notebooks and is probably most suitable to the purposes of our tutorial. -Go to the browser where you have opened ``jupyter`` and click ``New`` → ``Python 3`` (top right corner). -This will open an IPython-based Jupyter notebook, made of cells in which you can type portions of python code. -The code will not be executed until you press ``Shift+Enter`` from within a cell. -Type in the first cell - -.. code:: ipython - - %aiida - -and execute it. -This will set exactly the same environment as the ``verdi shell``. -The notebook will be automatically saved upon any modification and when you think you are done, you can export your notebook in many formats by going to ``File`` → ``Download as``. -We suggest you to have a look at the drop-down menus ``Insert`` and ``Cell`` where you will find the main commands to manage the cells of your notebook. - -.. note:: - - The ``verdi shell`` and Jupyter notebook are completely equivalent. - Use either according to your personal preference. - -You will still sometimes need to type command-line instructions in ``bash`` in the first terminal you opened. -To differentiate these from the commands to be typed in the ``verdi shell``, the latter will be marked in this document by a green background, like: - -.. code:: python - - some verdi shell command - -while command-line instructions in ``bash`` to be typed into a terminal will be written with a blue background: - -.. code:: bash - - some bash command - -Alternatively, to avoid changing terminal, you can execute ``bash`` commands within the ``verdi shell`` or the notebook by adding an exclamation mark before the command itself: - -.. code:: ipython - - !some bash command - -.. note:: - - The background colors of the code sections may be displayed differently by different browsers. - For the color scheme mentioned here, we recommend using Google Chrome. - -.. _2019_chiba_loadnode: - -Loading a node --------------- - -Most AiiDA objects are represented by nodes, identified in the database by its ``PK`` (an integer). -You can access a node using the following command in the shell: - -.. code:: python - - node = load_node(PK) - -Load a node using one of the calculation ``PK`` s visible in the graph you displayed in the previous section of the tutorial. -Then get the energy of the calculation with the command - -.. code:: python - - node.res.energy - -You can also type - -.. code:: python - - node.res. - -and then press ``TAB`` to see all the available output results of the calculation. - -Loading specific kinds of nodes -------------------------------- - -Pseudopotentials -~~~~~~~~~~~~~~~~ - -From the graph you generated in section :ref:`2019_chiba_aiidagraph`, find the ``UUID`` of the pseudopotential files (``UpfData``). -Load one of them and show what elements it corresponds to by typing: - -.. code:: python - - upf = load_node("") - upf.element - -All methods of ``UpfData`` are accessible by typing ``upf.`` and then pressing ``TAB``. - -k-points -~~~~~~~~ - -A set of k-points in the Brillouin zone is represented by an instance of the ``KpointsData`` class. -Choose one from the graph of produced in section :ref:`2019_chiba_aiidagraph`, load it as ``kpoints`` and inspect its content: - -.. code:: python - - kpoints.get_kpoints_mesh() - -Then get the full (explicit) list of k-points belonging to this mesh using - -.. code:: python - - kpoints.get_kpoints_mesh(print_list=True) - -If this throws an ``AttributeError``, it means that the kpoints instance does not represent a regular mesh but rather a list of k-points defined by their crystal coordinates (typically used when plotting a band structure). -In this case, get the list of k-points coordinates using - -.. code:: python - - kpoints.get_kpoints() - -Conversely, if the `KpointsData` node `does` actually represent a mesh, this method is the one, that when called, will throw an ``AttributeError``. - -If you prefer Cartesian (rather than crystal) coordinates, type - -.. code:: python - - kpoints.get_kpoints(cartesian=True) - -For later use in this tutorial, let us try now to create a kpoints instance, to describe a regular (2 x 2 x 2) mesh of k-points, centered at the Gamma point (i.e. without offset). -This can be done with the following commands: - -.. code:: python - - KpointsData = DataFactory('array.kpoints') - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh] * 3) - kpoints.store() - -This function loads the appropriate class defined in a string (here ``array.kpoints``). -Therefore, ``KpointsData`` is not a class instance, but the kpoints class itself! - -While it is also possible to import ``KpointsData`` directly, it is recommended to use the ``DataFactory`` function instead, as this is more future-proof: -even if the import path of the class changes in the future, its entry point string (``array.kpoints``) will remain stable. - - -Parameters -~~~~~~~~~~ - -Dictionaries with various parameters are represented in AiiDA by ``Dict`` nodes. -Get the PK and load the input parameters of a calculation in the graph produced in section :ref:`2019_chiba_aiidagraph`. -Then display its content by typing - -.. code:: python - - params = load_node('') - YOUR_DICT = params.get_dict() - YOUR_DICT - -Modify the python dictionary ``YOUR_DICT`` so that the wave-function cutoff is now set to 20 Ry. -Note that you cannot modify an object already stored in the database. -To write the modified dictionary to the database, create a new object of class ``Dict``: - -.. code:: python - - Dict = DataFactory('dict') - new_params = Dict(dict=YOUR_DICT) - -where ``YOUR_DICT`` is the modified python dictionary. -Note that ``new_params`` is not yet stored in the database. -In fact, typing ``new_params`` in the verdi shell will print a string notifying you of its 'unstored' status. -Let's finish by storing the ``new_params`` dictionary node in the datbase: - -.. code:: python - - new_params.store() - -Structures -~~~~~~~~~~ - -Find a structure in the graph you generated in section :ref:`2019_chiba_aiidagraph` and load it. -Display its chemical formula, atomic positions and species using - -.. code:: python - - structure.get_formula() - structure.sites - -where ``structure`` is the structure you loaded. -If you are familiar with ASE and PYMATGEN, you can convert this structure to those formats by typing - -.. code:: python - - structure.get_ase() - structure.get_pymatgen() - -Let’s try now to define a new structure to study, specifically a silicon crystal. -In the ``verdi shell``, define a cubic unit cell as a 3 x 3 matrix, with lattice parameter `a`\ :sub:`lat`\ `= 5.4` Å: - -.. code:: python - - alat = 5.4 - the_cell = [[alat/2, alat/2, 0.], [alat/2, 0., alat/2], [0., alat/2, alat/2]] - -.. note:: - - Default units for crystal structure cell and coordinates in AiiDA are Å (Ångström). - -Structures in AiiDA are instances of the class ``StructureData``: load it in the verdi shell - -.. code:: python - - StructureData = DataFactory('structure') - -Now, initialize the class instance (i.e. the actual structure we want to study) by the command - -.. code:: python - - structure = StructureData(cell=the_cell) - -which sets the cubic cell defined before. -From now on, you can access the cell with the command - -.. code:: python - - structure.cell - -Finally, append each of the 2 atoms of the cell command. -You can do it using commands like - -.. code:: python - - structure.append_atom(position=(0., 0., 0.), symbols="Si") - structure.append_atom(position=(alat/4., alat/4., alat/4.), symbols="Si") - -for the first ‘Si’ atom. -You can access and inspect the structure sites with the command - -.. code:: python - - structure.sites - -If you make a mistake, start over from -``structure = StructureData(cell=the_cell)``, or equivalently use -``structure.clear_kinds()`` to remove all kinds (atomic species) and sites. -Alternatively, AiiDA structures can also be converted directly from ASE structures [#f1]_ using - -.. code:: python - - from ase.spacegroup import crystal - ase_structure = crystal('Si', [(0, 0, 0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90], primitive_cell=True) - structure = StructureData(ase=ase_structure) - -Now you can store the new structure object in the database with the command: - -.. code:: python - - structure.store() - -Finally, we can also import the silicon structure from an external (online) repository such as the Crystallography Open Database (COD): - -.. code:: python - - from aiida.tools.dbimporters.plugins.cod import CodDbImporter - importer = CodDbImporter() - for entry in importer.query(formula='Si', spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print("Formula", structure.get_formula()) - print("Unit cell volume: ", structure.get_cell_volume()) - -In that case two duplicate structures are found for 'Si'. -A more in-depth tutorial can be found in :ref:`this appendix<2019_chiba_appendix_structure_data>`. - -Accessing inputs and outputs ----------------------------- - -Load again the calculation node used in Section :ref:`2019_chiba_loadnode`: - -.. code:: python - - calc = load_node(PK) - -Then type - -.. code:: python - - calc.inputs. - -and press ``TAB``: you will see all the link names between the calculation and its input nodes. -You can use a specific linkname to access the corresponding input node, e.g.: - -.. code:: python - - calc.inputs.structure - -Similarly, if you type: - -.. code:: python - - calc.outputs. - -and then ``TAB``, you will list all output link names of the calculation. -One of them leads to the structure that was the input of ``calc`` we loaded previously: - -.. code:: python - - calc.outputs.output_structure - -Note that links have a single name, that was assigned by the calculation that used the corresponding input or produced the corresponding output, as illustrated in section :ref:`2019_chiba_aiidagraph`. - -For a more programmatic approach, you can get a represenation of the inputs and outputs of a node, say ``calc``, through the following methods: - -.. code:: python - - calc_incoming = calc.get_incoming() - calc_outgoing = calc.get_outgoing() - -These methods will return an instance of the ``LinkManager`` class. -You can iterate over the neighboring nodes by calling the ``.all()`` method: - -.. code:: python - - for entry in calc.get_outgoing(): - print(entry.link_label, entry.link_type, entry.node) - -each entry returned by ``.all()`` is a ``LinkTriple``, a named tuple, from which you can get the link label and type and the neighboring node itself. -If you print one, you will see something like: - -.. code:: python - - LinkTriple(node=, link_type=, link_label=u'output_parameters') - -There are many other convenience methods on the ``LinkManager``. -For example if you are only interested in the link labels you can use: - -.. code:: python - - calc.get_outgoing().all_link_labels() - -which will return a list of all the labels of the outgoing links. -Likewise, ``.all_nodes()`` will give you a list of all the nodes to which links are going out from the ``calc`` node. -If you are looking for the node with a specific label, you can use: - -.. code:: python - - calc.get_outgoing().get_node_by_label('output_parameters') - -The ``get_outgoing`` and ``get_incoming`` methods also support filtering on various properties, such as the link label. -For example, if you only want to get the outgoing links whose label starts with ``output``, you can do the following: - -.. code:: python - - calc.get_outgoing(link_label_filter='output%').all_nodes() - - -Pseudopotential families ------------------------- - -Pseudopotentials in AiiDA are grouped in 'families' that contain one single pseudo per element. -We will see how to work with UPF pseudopotentials (the format used by Quantum ESPRESSO and some other codes). -Download and untar the SSSP pseudopotentials via the commands (due to slow network from Japan to Europe, we temporarily put the aiida data on Kyoto university web server): - -.. code:: bash - - wget http://phonondb.mtl.kyoto-u.ac.jp/aiida_tutorial/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz - -But the original data is found here: - -.. code:: bash - - https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz - - -Then you can upload the whole set of pseudopotentials to AiiDA by using the following ``verdi`` command: - -.. code:: bash - - verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' - -In the command above, ``SSSP_efficiency_pseudos`` is the folder containing the pseudopotentials, ``'SSSP'`` is the name given to the family, and the last argument is its description. -Finally, you can list all the pseudo families present in the database with - -.. code:: bash - - verdi data upf listfamilies - -A more in-depth tutorial about working with ``UpfData`` nodes and pseudopotential families can be found in :ref:`this appendix<2019_chiba_appendix_upf_data>`. - - -.. rubric:: Footnotes - -.. [#f1] We purposefully do not provide advanced commands for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks already exist (such as ASE or pymatgen). diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sections/workflows.rst b/docs/pages/2019_ISSP_Chiba_Japan/sections/workflows.rst deleted file mode 100644 index 9b1c2023..00000000 --- a/docs/pages/2019_ISSP_Chiba_Japan/sections/workflows.rst +++ /dev/null @@ -1,512 +0,0 @@ -Workflows -========= - -The aim of the last part of this tutorial is to introduce the concept of workflows in AiiDA. - -In this section, we will ask you to: - -1. Understand how to keep the provenance when running small python scripts to convert one data object into another (postprocessing, preparation of inputs, etc.) -2. Understand how to represent simple python functions in the AiiDA database -3. Learn how to write a simple workflow in AiiDA (without and with remote calculation submission) -4. Learn how to write a workflow with checkpoints: this means that, even if your workflow requires external calculations to start, they and their dependencies are managed through the daemon. - While you are waiting for the calculations to complete, you can stop and even shutdown the computer in which AiiDA is running. - When you restart, the workflow will continue from where it was. -5. (optional) Go a bit deeper in the syntax of workflows with checkpoints (``WorkChain``), e.g. implementing a convergence workflow using ``while`` loops. - -.. note:: - - This is probably the most 'complex' part of the tutorial. - We suggest that you try to understand the underlying logic behind the scripts, without focusing too much on the details of the workflows implementation or the syntax. - If you want, you can then focus more on the technicalities in a second reading. - -Introduction ------------- - -The ultimate aim of this section is to create a workflow to calculate the equation of state of silicon. -This is a very common task for an *ab initio* researcher. -An equation of state consists in calculating the total energy (E) as a function of the unit cell volume (V). -The minimal energy is reached at the equilibrium volume. -Equivalently, the equilibrium is defined by a vanishing pressure (p=-dE/dV). -In the vicinity of the minimum, the functional form of the equation of state can be approximated by a parabola. -Such an approximation greatly simplifies the calculation of the bulk modulus, that is proportional to the second derivative of the energy (a more advanced treatment requires fitting the curve with, e.g., the Birch–Murnaghan expression). - -The process of calculating an equation of state puts together several operations. -First, we need to define and store in the AiiDA database the basic structure of, e.g., bulk Si. -Next, one has to define several structures with different lattice parameters. -Those structures must be connected between them in the database, in order to ensure that their provenance is recorded. -In other words, we want to be sure that in the future we will know that if we find a bunch of rescaled structures in the database, they all descend from the same one. -How to link two nodes in the database in an easy way is the subject of :ref:`2019_chiba_provenancewf`. - -In the following sections, the newly created structures will then serve as an input for total energy calculations performed, in this tutorial, with Quantum ESPRESSO. -This task is very similar to what you have done in the previous part of the tutorial. -Finally, you will fit the resulting energies as a function of volume to get the bulk modulus. -As the EOS task is very common, we will show how to automate its computation with workflows, and how to deal with both serial and parallel (i.e., independent) execution of multiple tasks. -Finally, we will show how to introduce more complex logic in your workflows such as loops and conditional statements (:ref:`see this section<2019_chiba_convpressure>`), with an example on a convergence loop to find iteratively the minimum of an EOS. - -.. _2019_chiba_provenancewf: - -Process functions: a way to generalize provenance in AiiDA ----------------------------------------------------------- - -Imagine having a function that takes as input a string of the name of a chemical element and generates the corresponding bulk structure as a ``StructureData`` object. -The function might look like the following snippet: - -.. literalinclude:: include/snippets/workflows_create_diamond_fcc.py - :language: python - -For the equation of state you need another function that takes as input a ``StructureData`` object and a rescaling factor, and returns a ``StructureData`` object with the rescaled lattice parameter: - -.. literalinclude:: include/snippets/workflows_rescale.py - :language: python - -In order to generate the rescaled starting structures, say for five different lattice parameters you would combine the two functions. -Open a ``verdi shell``, define the two functions from the previous snippets and enter the following commands: - -.. code:: python - - initial_structure = create_diamond_fcc('Si') - rescaled_structures = [rescale(initial_structure, factor) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and store them in the database: - -.. code:: python - - initial_structure.store() - for structure in rescaled_structures: - structure.store() - -As expected, all the structures that you have created are not linked in any manner as you can verify via the ``get_incoming()``/``get_outgoing()`` methods of the ``StuctureData`` class. -Instead, you would like these objects to be connected as sketched in :numref:`2019_chiba_fig_provenance_process_functions`. - -.. _2019_chiba_fig_provenance_process_functions: -.. figure:: include/images/process_functions.png - - Typical graphs created by using calculation and work functions. - (a) The calculation function ``create_structure`` takes a ``Str`` object as input and returns a single ``StructureData`` object which is used as input for the calculation function ``rescale`` together with a ``Float`` object. - This latter calculation function returns another ``StructureData`` object, defining a crystal with a rescaled lattice constant. - (b) Graph generated by a work function that calls two calculation functions. - A wrapper work function ``create_rescaled`` calls serially the calculation functions ``create_structure`` and ``rescale``. - This relationship is stored via ``CALL`` links. - - -Now that you are familiar with AiiDA, you know that the way to connect two data nodes is through a calculation. -In order to 'wrap' python functions and automate the generation of the needed links, in AiiDA we provide you with what we call 'process functions'. -There are two variants of process functions: - - * calculation functions - * work functions - -These operate mostly the same, but they should be used for different purposes, which will become clear later. -A normal function can be converted to a calculation function by using the ``@calcfunction`` decorator [#f1]_ that takes care of storing the execution as a calculation and adding the links between the input and output data nodes. - -To turn the original functions ``create_diamond_fcc`` and ``rescale`` into calculation functions, simply change the definition as follows: - -.. code:: python - - # Add this import - from aiida.engine import calcfunction - - # Add decorators - @calcfunction - def create_diamond_fcc(element): - ... (same as above) - - @calcfunction - def rescale(structure, scale): - ... (same as above) - -Note that the only change is that the function definitions were 'decorated' with the ``@calcfunction`` line. -This is the only thing that is necessary to transform the plain python functions magically into fully-fledged AiiDA process functions. - -.. note:: - - The only additional change necessary, is that process function input and outputs need to be ``Data`` nodes, so that they can be stored in the database. - AiiDA objects such as ``StructureData``, ``Dict``, etc. carry around information about their provenance as stored in the database. - This is why we must use the special database-storable types ``Float``, ``Str``, etc. as shown in the snippet below. - -Try now to run the following script: - -.. code:: python - - from aiida.orm import Float, Str - initial_structure = create_diamond_fcc(Str('Si')) - rescaled_structures = [rescale(initial_structure, Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and check now that the output of ``initial_structure`` as well as the input of the rescaled structures point to an intermediate node, representing the execution of the calculation function, see :numref:`2019_chiba_fig_provenance_process_functions`. -For instance, you can check that the output links of ``initial_structure`` are the five ``rescale`` calculations: - -.. code:: python - - initial_structure.get_outgoing().all_nodes() - -which outputs - -.. code:: bash - - [, - , - , - , - ] - -and the inputs of each ``CalcFunctionNode`` 'rescale' are obtained with: - -.. code:: python - - for structure in initial_structure.get_outgoing().all_nodes(): - print(structure.get_incoming().all_nodes()) - -that will return - -.. code:: bash - - [, ] - [, ] - [, ] - [, ] - [, ] - -Function nesting -~~~~~~~~~~~~~~~~ -Calculation functions can be 'chained' together by wrapping them together in a work function. -The work function works almost exactly the same as a calculation function, except that it cannot 'create' data, but rather is used to 'call' calculation functions that do the calculations for it. -The calculation functions that it calls will be automatically linked in the provenance graph through 'call' links. -As an example, let us combine the two previously defined calculation functions by means of a wrapper work function called 'create_rescaled' that takes as input the element and the rescale factor. - -Type in your shell (or modify the functions defined in ``create_rescale.py`` and then run): - -.. include:: include/snippets/workflows_create_rescaled.py - :code: python - -and create an already rescaled structure by typing - -.. code:: python - - rescaled = create_rescaled(element=Str('Si'), scale=Float(0.98)) - -Now inspect the input links of ``rescaled``: - -.. code:: ipython - - In [6]: rescaled.get_incoming().all_nodes() - Out[6]: - [, - ] - -The object ``rescaled`` has two incoming links, corresponding to *two* different calculations as input. -These correspond to the calculations 'create_rescaled' and 'rescale' as shown in :numref:`2019_chiba_fig_provenance_process_functions`. -To see the 'call' link, inspect now the outputs of the ``WorkFunctionNode`` which corresponds to the ``create_rescaled`` work function. -Write down its ```` (in general, it will be different from 441), then in the shell load the corresponding node and inspect the outputs: - -.. code:: ipython - - In [12]: node = load_node() - In [13]: node.get_outgoing().all() - -You should be able to identify the two children calculations as well as the final structure (you will see the process nodes linked via CALL links: these are process-to-process links representing the fact that ``create_rescaled`` called two calculation functions). -The graphical representation of what you have in the database should match :numref:`2019_chiba_fig_provenance_process_functions`. - -.. _2019_chiba_sync: - -Run a simple workflow ---------------------- -Let us now use the work and calculation functions that we have just created to build a simple workflow to calculate the equation of state of silicon. -We will consider five different values of the lattice parameter obtained rescaling the experimental minimum, ``a=5.431``, by a factor in ``[0.96, 0.98, 1.0, 1.02, 1.04]``. -We will write a simple script that runs a series of five calculations and at the end returns the volume and the total energy corresponding to each value of the lattice parameter. -For your convenience, besides the functions that you have written so far in the file ``create_rescale.py``, we provide you with some other utilities to get the correct pseudopotential and to generate a pw input file, in :download:`common_wf.py <../scripts/common_wf.py>`. - -We have already created the following script, which you can :download:`download <../scripts/simple_sync_workflow.py>`, but please go through the lines carefully and make sure you understand them. -We suggest that you first have a careful look at it before running it: - -.. include:: ../scripts/simple_sync_workflow.py - :code: python - -If you look into the previous piece of code, you will notice that the way we submit a QE calculation is slightly different from what you have seen in the first part of the tutorial. -The following: - -.. code:: python - - calculations[label] = run(PwCalculation, **inputs) - -runs in the current python session (without the daemon), waits for its completion and returns the output in the user-defined variable ``result``. -The latter is a dictionary whose values are the output nodes generated by the work function, with the link labels as keys. -For example once the function is finished, in order to access the total energy, we need to access the ``Dict`` node which is linked via the 'output_parameters' link (see again Fig. 1 of Day 1 Tutorial, to see inputs and outputs of a Quantum ESPRESSO calculation). -Once the right node is retrieved as ``result['output_parameters']``, we need to get the ``energy`` attribute. -The global operation is achieved by the command - -.. code:: python - - result['output_parameters'].dict.energy - -To collect these results from the various calculations into a single data node, we need one final calculation function to do so. -Since the ``run_eos_wf`` is a 'workflow'-like processes, which cannot create data, this operation **cannot** simply be done in the work function body itself. -To keep the provenance **we have to** do this through a calculation function. -This is done by the ``create_eos_dictionary`` calculation function, that receives as inputs, the output parameters nodes from the completed calculations: - -.. code:: python - - @calcfunction - def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - -It constructs a new ``Dict`` node that contains a single value ``eos`` which is a list of tuples with the relevant data for each calculation. -If you were to inspect the returned data node, with for example ``verdi data dict show`` you would see something like: - -.. code:: bash - - { - "eos": [ - [ - 40.04786949775, - -308.193208771881, - "eV" - ], - [ - 37.6927343883263, - -307.990673492459, - "eV" - ], - [ - 35.4317918679613, - -307.666113525946, - "eV" - ], - [ - 45.048406674717, - -308.308206255253, - "eV" - ], - [ - 42.4991194939683, - -308.293522312459, - "eV" - ] - ] - } - -As you see, the function ``run_eos_wf`` has been decorated as a work function to keep track of the provenance. -To run the workflow it suffices to call the function ``run_eos_wf`` in a python script providing the required input parameters. -For simplicity, we have included few lines at the end of the script that invoke the function with a static choice of parameters: - -.. code:: python - - def run_eos(code=load_code('qe-6.4.1-pw@localhost'), pseudo_family='SSSP', element='Si'): - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - if __name__ == '__main__': - run_eos() - -To get a reference to the node that represents the function execution, we can ask the ``run`` function to return the node, in addition to the results. -Instead of calling the work function to run it, we can use the method ``run_get_node``: - -.. code:: python - - results, node = run_eos_wf.run_get_node(code, Str(pseudo_family), Str(element)) - print('run_eos_wf<{}> completed'.format(node.pk)) - -Run the workflow: - -.. code:: bash - - verdi run simple_sync_workflow.py - -The command above locks the shell until the full workflow has completed (we will see in a moment how to avoid this). -While the function is running, you can use (in a different shell) the command ``verdi process list`` to show ongoing and finished processes. -You can 'grep' for the ```` you are interested in. -Additionally, you can use the command ``verdi process status `` to show the tree of the calculatino functions called by the root work function with a given ````. - -Wait for the work function to finish, then call the function ``plot_eos()`` that we provided in the file ``common_wf.py`` to plot the equation of state and fit it with a Birch–Murnaghan equation. - -.. _2019_chiba_wf_multiple_calcs: - -Run multiple calculations -------------------------- - -You should have noticed that the calculations for different lattice parameters are executed serially, although they might perfectly be executed in parallel because their inputs and outputs are not connected in any way. -In the language of workflows, these calculations are executed in a synchronous (or blocking) way, whereas we would like to have them running *asynchronously* (i.e., in a non-blocking way, to run them in parallel). -One way to achieve this, is to submit the calculation to the daemon using the ``submit`` function. -Make a copy of the script ``simple_sync_workflow.py`` that we worked on in the previous section and name it ``simple_submit_workflow.py``. -To make the new script work asynchronously, simply change the following subset of lines: - -.. code:: python - - from aiida.engine import run - - [...] - - for label, factor in zip(labels, scale_factors): - - [...] - - calculations[label] = run(PwCalculation, **inputs) - - [...] - - inputs = {label: result['output_parameters'] for label, result in calculations.items()} - eos = create_eos_dictionary(**inputs) - - [...] - -replacing them with - -.. code:: python - - from aiida.engine import submit - from time import sleep - - [...] - - for label, factor in zip(labels, scale_factors): - [...] - - # Replace `run` with `submit` - calculations[label] = submit(PwCalculation, **inputs) - - [...] - - # Wait for the calculations to finish - for calculation in calculations.values(): - while not calculation.is_finished: - sleep(1) - - inputs = {label: node.get_outgoing().get_node_by_label('output_parameters') for label, node in calculations.items()} - eos = create_eos_dictionary(**inputs) - -The main differences are: - -- ``run`` is replaced by ``submit`` -- The return value of ``submit`` is not a dictionary describing the outputs of the calculation, but it is the node that represents the execution of that calculation. -- Each calculation starts in the background and calculation nodes are added to the ``calculations`` dictionary. -- At the end of the loop, when all calculations have been launched with ``submit``, another loop is used to wait for all calculations to finish before gathering the results as the final step. - -In the next section we will show you another way to achieve this, which has the added bonus that it introduces checkpoints in the work function, from which the process can be resumed should it be interrupted. -After applying the modifications, run the script. -You will see that all calculations start at the same time, without waiting for the previous ones to finish. - -If in the meantime you run ``verdi process status ``, all five calculations are already shown as output. -Also, if you run ``verdi process list``, you will see how the calculations are submitted to the scheduler. - -.. _2019_chiba_workchainsimple: - -Workchains, or how not to get lost if your computer shuts down or crashes -------------------------------------------------------------------------- - -The simple workflows that we have used so far have been launched by a python script that needs to be running for the whole time of the execution, namely the time in which the calculations are submitted, and the actual time needed by Quantum ESPRESSO to perform the calculation and the time taken to retrieve the results. -If you had killed the main python process during this time, the workflow would not have terminated correctly. -Perhaps you killed the calculation and you experienced the unpleasant consequences: intermediate calculation results are potentially lost and it is extremely difficult to restart a workflow from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way to insert checkpoints, where the main code defining a workflow can be stopped (you can even shut down the machine on which AiiDA is running!). -We call these work functions with checkpoints 'work chains' because, as you will see, they basically amount to splitting a work function in a chain of steps. -Each step is then ran by the daemon, in a way similar to the remote calculations. - -Here below you can find the basic rules that allow you to convert your workfunction-based script to a workchain-based one and a snippet example focusing on the code used to perform the calculation of an equation of state. - -.. include:: ../scripts/equation_of_state.py - :code: python - -.. warning:: - - WorkChains need to be defined in a **separate file** from the script used to run them. - E.g. save your WorkChain in ``workchains.py`` and use ``from workchains import MyWorkChain`` to import it in your script. - - Furthermore, the directory containing the WorkChain definition needs to be in the ``PYTHONPATH`` in order for the AiiDA daemon to find it. - If your ``workchains.py`` sits in ``/home/max/workchains``, add a line ``export PYTHONPATH=$PYTHONPATH:/home/max/workchains`` to the ``/home/max/.virtualenvs/aiida/bin/activate`` script, followed by ``verdi daemon restart``. - -- Instead of using decorated functions you need to define a class, inheriting from a prototype class called ``WorkChain`` that is provided by AiiDA in the ``aiida.engine`` module. - -- Within your class you need to implement a ``define`` classmethod that always takes ``cls`` and ``spec`` as inputs. - Here you specify the main information on the workchain, in particular: - - - The *inputs* that the workchain expects. - This is obtained by means of the ``spec.input()`` method, which provides as the key feature the automatic validation of the input types via the ``valid_type`` argument. - The same holds true for outputs, as you can use the ``spec.output()`` method to state what output types are expected to be returned by the workchain. - - - The ``outline`` consisting in a list of 'steps' that you want to run, put in the right sequence. - This is obtained by means of the method ``spec.outline()`` which takes as input the steps. - *Note*: in this example we just split the main execution in two sequential steps, that is, first ``run_eos`` then ``results``. - However, more complex logic is allowed, as will be explained in :ref:`another section<2019_chiba_convpressure>`. - -- You need to split your main code into methods, with the names you specified before into the outline (``run_eos`` and ``results`` in this example). - Where exactly should you split the code? - Well, the splitting points should be put where you would normally block the execution of the script for collecting results in a standard work function, namely whenever you call the method ``.result()``. - Each method should accept only one parameter, ``self``, e.g. ``def step_name(self)``. - -- You will notice that the methods reference the attribute ``ctx`` through ``self.ctx``, which is called the *context* and is inherited from the base class ``WorkChain``. - A python function or process function normally just stores variables in the local scope of the function. - For instance, in the example of :ref:`this subsection<2019_chiba_sync>`, you stored the completed calculations in the ``calculations`` dictionary, that was a local variable. - - In work chains, instead, to preserve variables between different steps, you need to store them in a special dictionary called *context*. - As explained above, the context variable ``ctx`` is inherited from the base class ``WorkChain``, and at each step method you just need to update its content. - AiiDA will take care of saving the context somewhere between workflow steps (on disk, in the database, depending on how AiiDA was configured). - For your convenience, you can also access the value of a context variable as ``self.ctx.varname`` instead of ``self.ctx['varname']``. - -- Any submission within the workflow should not call the normal ``run`` or ``submit`` functions, but ``self.submit`` to which you have to pass the process class, and a dictionary of inputs. - -- The submission in ``run_eos`` returns a future and not the actual calculation, because at that point in time we have only just launched the calculation to the daemon and it is not yet completed. - Therefore it literally is a 'future' result. - Yet we still need to add these futures to the context, so that in the next step of the workchain, when the calculations are in fact completed, we can access them and continue the work. - To do this, we can use the ``ToContext`` class. - This class takes a dictionary, where the values are the futures and the keys will be the names under which the corresponding calculations will be made available in the context when they are done. - See how the ``ToContext`` object is created and returned in ``run_eos``. - By doing this, the workchain will implicitly wait for the results of all the futures you have specified, and then call the next step *only when all futures have completed*. - -- *Return values*: While in normal process functions you attach output nodes to the node by invoking the *return* statement, in a workchain you need to call ``self.out(link_name, node)`` for each node you want to return. - Of course, if you have already prepared a dictionary of outputs, you can just use the following syntax: - - .. code:: python - - self.out_many(retdict) # Keys are link names, value the nodes - - The advantage of this different syntax is that you can start emitting output nodes already in the middle of the execution, and not necessarily at the very end as it happens for normal functions (*return* is always the last instruction executed in a function or method). - Also, note that once you have called ``self.out(link_name, node)`` on a given ``link_name``, you can no longer call ``self.out()`` on the same ``link_name``: this will raise an exception. - -Finally, the workflow has to be run. -For this you have to use the function ``run`` passing as arguments the ``EquationOfState`` class and the inputs as key-value arguments. -For example, you can execute: - -.. code:: python - - from aiida.engine import run - run(EquationOfState, element=Str('Si'), code=load_code('qe-6.4.1-pw@localhost'), pseudo_family=Str('SSSP')) - -While the workflow is running, you can check (in a different terminal) what is happening to the calculations using ``verdi process list``. -You will see that after a few seconds the calculations are all submitted to the scheduler and can potentially run at the same time. - -.. warning:: - - The calculations launched by the work chain will now be submitted to the daemon, instead of run in the same interpreter - However, by using ``run`` on the work chain itself, it will still not be able to restart. - To make the work chain save its checkpoints, you should use the ``submit`` launcher instead. - -As an additional exercise (optional), instead of running the main workflow ``EquationOfState``, try to submit it. -Note that the file where the work chains is defined will need to be globally importable (so the daemon knows how to load it) and you need to launch it (with ``submit``) from a different python file. -The easiest way to achieve this is typically to embed the workflow inside a python package. - -.. note:: - - As good practice, you should try to keep the steps as short as possible in term of execution time. - The reason is that the daemon can be stopped and restarted only between execution steps and not if a step is in the middle of a long execution. - -Finally, as an optional exercise if you have time, you can jump to :ref:`this appendix<2019_chiba_convpressure>`, which shows how to introduce more complex logic into your work chains (if conditionals, while loops etc.). -The exercise will show how to realize a convergence loop to obtain the minimum-volume structure in a EOS using the Newton's algorithm. - -.. note:: - - You might see warnings that say ``Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint``. - These are generated by the ``PwCalculation`` which is unable to save a checkpoint because it is not in a so called 'importable path'. - Simply put, this means that if AiiDA were to try and reload the class it wouldn't know which file to find it in. - To get around this you could simply put the workchain in a different file that is in the 'PYTHONPATH' and then launch it by importing it in your launch file. - In this way AiiDA knows where to find it next time it loads the checkpoint. - - -.. rubric:: Footnotes - -.. [#f1] In simple words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form ``@decorating_function_name`` on the line just before the ``def`` line of the decorated function. If you want to know more, there are many online resources explaining python decorators. diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sponsors/epfl.png b/docs/pages/2019_ISSP_Chiba_Japan/sponsors/epfl.png deleted file mode 100644 index f888916d..00000000 Binary files a/docs/pages/2019_ISSP_Chiba_Japan/sponsors/epfl.png and /dev/null differ diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sponsors/issplogo_en.jpg b/docs/pages/2019_ISSP_Chiba_Japan/sponsors/issplogo_en.jpg deleted file mode 100644 index 2a6a7980..00000000 Binary files a/docs/pages/2019_ISSP_Chiba_Japan/sponsors/issplogo_en.jpg and /dev/null differ diff --git a/docs/pages/2019_ISSP_Chiba_Japan/sponsors/marvel.png b/docs/pages/2019_ISSP_Chiba_Japan/sponsors/marvel.png deleted file mode 100644 index 92686e61..00000000 Binary files a/docs/pages/2019_ISSP_Chiba_Japan/sponsors/marvel.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/index.rst b/docs/pages/2019_Ljubljana/index.rst deleted file mode 100644 index 9011cef5..00000000 --- a/docs/pages/2019_Ljubljana/index.rst +++ /dev/null @@ -1,73 +0,0 @@ -.. _Ljubljana 2019 Homepage: - -2019, Jožef Stefan Institute, Ljubljana, Slovenia -================================================= - -+-----------------+--------------------------------------------------------------------------------+ -| Related resources | -+=================+================================================================================+ -| Virtual Machine | `Custom VM used for the Ljubljana 2019 tutorial`_ | -| | | -| | or `Quantum Mobile 19.09.0`_ | -+-----------------+--------------------------------------------------------------------------------+ -| python packages | `aiida-core 1.0.0b6`_, `aiida-quantumespresso 3.0.0a5`_ | -+-----------------+--------------------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.3`_ | -+-----------------+--------------------------------------------------------------------------------+ - -.. _Quantum Mobile 19.09.0: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.09.0 -.. _aiida-core 1.0.0b6: https://pypi.org/project/aiida-core/1.0.0b6 -.. _aiida-quantumespresso 3.0.0a5: https://github.com/aiidateam/aiida-quantumespresso/releases/tag/v3.0.0a5 -.. _Quantum Espresso 6.3: https://github.com/QEF/q-e/releases/tag/qe-6.3 -.. _Custom VM used for the Ljubljana 2019 tutorial: https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/old_tutorials/AiiDA_tutorial_2019_09_ljubljana-QE2019.ova - -.. note:: The credentials of the custom VM are the following: username: ``user``, password: ``qe2019``. - Also, when you start it, to make it more performant you might want to set 2 CPUs, and 2GB of RAM or more. - If the machine becomes slow after a few hours of use, in VirtualBox 6 you can change the settings of the - graphics controller to VMSVGA. - -These are the hands-on materials from the 1-day AiiDA tutorial, part of the -`Summer School on Advanced Materials and Molecular Modelling `__, -held on September 16-20, 2019 in Ljubljana (Slovenia). - -.. note:: During the tutorial, some of the AiiDA developers will be online and - available to help on the `qe2019-ljubljana Slack workspace `_. An invitation - link will be given during the tutorial. - -AiiDA hands-on materials -^^^^^^^^^^^^^^^^^^^^^^^^ - -Demo ----- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/setup - ./sections/first_taste - -In-depth tutorial ------------------ -In this tutorial, no in-depth tutorial has been presented. - -If you are interested, you can check the in-depth tutorial of the :ref:`Xiamen 2019 tutorial ` -or of the :ref:`EPFL 2019 tutorial `. - -Acknowledgements ----------------- - -The `Summer School on Advanced Materials and Molecular Modelling `_ was made possible by support from -the Quantum ESPRESSO foundation, MaX, and the Jožef Stefan Institute. - -.. image:: sponsors/qef.png - :target: https://foundation.quantum-espresso.org/ - :width: 200px - -.. image:: sponsors/max.png - :target: http://www.max-centre.eu/ - :width: 200px - -.. image:: sponsors/jozef-stefan.png - :target: https://www.ijs.si/ijsw/V001/JSI - :width: 180px diff --git a/docs/pages/2019_Ljubljana/sections/first_taste.rst b/docs/pages/2019_Ljubljana/sections/first_taste.rst deleted file mode 100644 index 48f9381e..00000000 --- a/docs/pages/2019_Ljubljana/sections/first_taste.rst +++ /dev/null @@ -1,444 +0,0 @@ -.. _Ljubliana 2019 first taste: - -A first taste of AiiDA -====================== - -Let's start with a quick demo of how AiiDA can make your life easier as a computational scientist. - -We'll be using the ``verdi`` command-line interface, -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - -As the first thing, open a terminal and type ``workon aiida`` to enter the "virtual environment" where AiiDA is installed. -You will know that you are in the virtual environment because each new line will start with ``(aiida)``, e.g.:: - - (aiida) user@qe2019:~$ - -Note that you will need to retype ``workon aiida`` every time you open a new terminal. - -Here are some first tasks for you: - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``--help/-h`` flag: - - .. code:: bash - - verdi -h - -Importing a structure and inspecting it ---------------------------------------- - -Let's download a structure from the `Crystallography Open Database `_ and import it into AiiDA. - -.. note:: - - You can view the structure `online `_. - - -You can download the file and import it with the following two commands: - -.. code:: bash - - wget http://crystallography.net/cod/9008565.cif - verdi data structure import ase 9008565.cif - -Each piece of data in AiiDA gets a PK number (a "primary key") -that identifies it in your database. -This is printed out on the screen by the ``verdi data structure import`` command. -Mark it down, as we are going to use it in the next commands. - -.. note:: - - In the next commands, replace the string ```` with the appropriate PK number. - -Let us first inspect the node you just created: - -.. code:: bash - - verdi node show - -You will get in output some information on the node, -including its type (``StructureData``, the AiiDA data type for storing crystal -structures), a label and a description (empty for now, can be changed), -a creation time (``ctime``) and a last modification time (``mtime``), -the PK of the node and its UUID (universally unique identifier). - -.. note:: - - **When should I use the PK and when should I use the UUID?** - - A **PK** is a short integer identifying the node and therefore easy to remember. - However, the same PK number (e.g., PK=10) - might appear in two different databases referring to two completely different - pieces of data. - - A **UUID** is a hexadecimal string that might look like this:: - - d11a4829-3e19-4978-bfcf-c28ddeb0891e - - A UUID has instead the nice feature to be globally unique: even if you export - your data and a colleague imports it, the UUIDs will remain the same - (while the PKs will typically be different). - - Therefore, use the UUID to keep a long-term reference to a node. - Feel free to use the PK for quick, everyday use (e.g. to inspect a node). - -.. note:: - All AiiDA commands accepting a PK can also accept a UUID. Check this by - trying the command before, this time with ``verdi node show ``. - - Note the following: - - - AiiDA does not require the full UUID, but just the first part of it, - as long as only one node starts with the string you provide. E.g., in the example above, - you could also say ``verdi node show d11a4829-3e19``. Most probably, instead, - ``verdi node show d1`` will return an error, since most probably - you have more than one node starting with the string ``d1``. - - - By default, if what you pass is a valid integer, AiiDA will assume it is a PK; - if at least one of the characters is not a digit, then AiiDA will assume - it is (the first part of) a UUID. - - - How to solve the issue, then, when the first part of the UUID is composed only by - digits (e.g. in ``2495301c-dd00-42d6-92e4-1a8c171bbb4a``)? Indeed, using - ``verdi node show 24953`` would look for a node with ``PK=24953``. As a solution, - just add a dash, e.g. ``verdi node show 24953-`` so that AiiDA will consider - this as the beginning of the UUID. - - - Note that you can put the dash in any part of the string, and you don't need - to respect the typical UUID pattern with 8-4-4-4-12 characters per section: - AiiDA will anyway first strip all dashes, and then put them back in the right - place, so e.g. ``verdi node show 24-95-3`` will give you the same result as - ``verdi node show 24953-``. - -- Try to use again ``verdi node show`` on the ``StructureData`` node above, - just with the first part of the UUID (that you got from the first call to - ``verdi node show`` above). - -- ``StructureData`` can be exported to file in various formats. - As an example, let's export the structure in XSF format and visualize it - with XCrySDen: - - .. code:: bash - - verdi data structure export --format=xsf > exported.xsf - xcrysden --xsf exported.xsf - - You should be visualize to see the Si supercell (8 atoms) that we downloaded - from the COD database (in CIF format), imported into AiiDA and exported back - into a different format (XSF). - -Running a calculation ---------------------- - -The following short python script sets up a self-consistent field calculation for the Quantum ESPRESSO code: - -.. literalinclude:: include/snippets/demo_calcjob.py - -Download the :download:`demo_calcjob.py ` script to your working directory. -It contains a few placeholders for you to fill in: - - #. the VM already has a number of codes preconfigured. Use ``verdi code list`` to find the label for the "PW" code and use it in the script. - #. replace the PK of the structure with the one you obtained - #. the VM already contains a number of pseudopotential families. Replace the PP family name with the one for the "SSSP efficiency" library found via ``verdi data upf listfamilies``. - -Then submit the calculation using: - -.. code:: bash - - verdi run demo_calcjob.py - -From this point onwards, the AiiDA daemon will take care of your calculation: creating the necessary input files, running the calculation, and parsing its results. - -In order to be able to do this, the AiiDA daemon must be running: to check this, you can run the command: - -.. code:: bash - - verdi daemon status - -and, if the daemon is not running, you can start it with - -.. code:: bash - - verdi daemon start - -It should take less than one minute to complete. - -Analyzing the outputs of a calculation --------------------------------------- - -Let's have a look how your calculation is doing: - -.. code:: bash - - verdi process list # shows only running processes - verdi process list --all # shows all processes - -Again, your calculation will get a PK, which you can use to get more information on it: - -.. code:: bash - - verdi process show - -As you can see, AiiDA has tracked all the inputs provided to the calculation, allowing you (or anyone else) to reproduce it later on. -AiiDA's record of a calculation is best displayed in the form of a provenance graph - -.. figure:: include/images/demo_calc.png - :width: 100% - - Provenance graph for a single Quantum ESPRESSO calculation. - -You can generate such a provenance graph for any calculation or data in AiiDA by running: - -.. code:: bash - - verdi node graph generate - -Try to reproduce the figure using the PK of your calculation. - -You might wonder what happened under the hood, e.g. where to find the actual input and output files of the calculation. -You will learn more about this later -- until then, here are a few useful commands: - -.. code:: bash - - verdi calcjob inputcat # shows the input file of the calculation - verdi calcjob outputcat # shows the output file of the calculation - verdi calcjob res # shows the parsed output - -A few questions you could answer using these commands (optional) - * How many atoms did the structure contain? - How many electrons? - * How many k-points were specified? How many k-points were actually computed? Why? - * How many SCF iterations were needed for convergence? - * How long did Quantum ESPRESSO actually run (wall time)? - -Moving to a different computer ------------------------------- - -Now, this Quantum ESPRESSO calculation ran on your (virtual) machine. -This is fine for tests, but for production calculations you'll typically want to run on a remote compute cluster. -In AiiDA, moving a calculation from one computer to another means changing one line of code. - -For the purposes of this tutorial, you'll run on the machine at the IJS -institute that you have already been using in the past days. - -.. note:: In case you don't have access to the IJS machine, you can instead use - a cloud machine that we have setup on an OpenStack cluster in Switzerland (in - the Swiss Supercomputing Centre CSCS), and that will be online (only) during - the tutorial. - - In this case, you will need to use the following files instead of the ones - discussed below: - - - Computer setup configuration file: :download:`openstack.yml ` - - Computer configure configuration file: :download:`openstack-config.yml ` - - Code setup configuration file: :download:`qe-openstack.yml ` - - In order to know how to use them, continue reading through this - section, replacing ``ijs`` with ``openstack``. The code that you will create - will be called ``qe-6.3-pw@openstack``. - -Download the :download:`ijs.yml ` setup -template, that you can also see here: - -.. literalinclude:: include/configuration/ijs.yml - -Read it to understand what is needed by AiiDA to setup a new computer. -Then, let AiiDA know about this computer (that will be called ``ijs``) by running: - -.. code:: bash - - verdi computer setup --config ijs.yml - -.. note:: - - If you're completing this tutorial at a later time and have no access to the - ``percolator.ijs.si`` machine, simply use "localhost" instead as the hostname, - and adapt the other parameters. - -AiiDA is now aware of the existence of the computer but you'll still need to let AiiDA -know how to connect to it. -AiiDA does this via `SSH `_ keys. -Your tutorial VM already contains a private SSH key for connecting -to the ``precolator.ijs.si`` machine that you set up on the first day of the tutorial, -so all that is left is to configure it in AiiDA. - -Download the :download:`ijs-config.yml ` configuration template, that -looks like this: - -.. literalinclude:: include/configuration/ijs-config.yml - -Replace the ```` placeholder with the username on percolator -that you have been given the first day of the turorial, save the file and then run: - -.. code:: bash - - verdi computer configure ssh ijs --config ijs-config.yml --non-interactive - -.. note:: Both ``verdi computer setup`` and ``verdi computer configure`` can be used interactively without - configuration files, which are provided here just to avoid typing errors. - -AiiDA should now have access to the ``percolator.ijs.si`` computer. Let's quickly test this: - -.. code:: bash - - verdi computer test ijs - -Finally, let AiiDA know about the **code** we are going to use. -We've again prepared a template that looks as follows: - -.. Add template for code -.. literalinclude:: include/configuration/qe.yml - -Download the :download:`qe.yml ` code template and run: - -.. code:: bash - - verdi code setup --config qe.yml - verdi code list # note the label of the new code you just set up! - -Now modify the code label in your ``demo_calcjob.py`` script to the label of your new code and simply run another calculation using ``verdi run demo_calcjob.py``. - -To see what is going on, AiiDA provides a command that lets you jump to the folder of the directory of the calculation on the remote computer: - -.. code:: bash - - verdi process list --all # get PK of new calculation - verdi calcjob gotocomputer - -Have a look around. - * Do you recognize the different files? - * Have a look at the submission script ``_aiidasubmit.sh``. - Compare it to the submission script of your previous calculation. - What are the differences? - -From calculations to workflows ------------------------------- - -AiiDA can help you run individual calculations but it is really designed to help you run workflows that involve several calculations, while automatically keeping track of the provenance for full reproducibility. - -As the final step, we are going to launch the ``PwBandStructure`` workflow of the ``aiida-quantumespresso`` plugin. - -.. literalinclude:: include/snippets/demo_bands.py - -Download the :download:`demo_bands.py ` snippet and run it using - -.. code:: bash - - verdi run demo_bands.py - -This workflow will: - - #. Determine the primitive cell of the input structure - #. Run a calculation on the primitive cell to relax both the cell and the atomic positions (``vc-relax``) - #. Refine the symmetry of the relaxed structure, and find a standardised primitive cell using SeeK-path_ - #. Run a self-consistent field calculation on the refined structure - #. Run a band structure calculation at fixed Kohn-Sham potential along a standard path between high-symmetry k-points determined by SeeK-path_ - -The workflow uses the PBE exchange-correlation functional with suitable pseudopotentials and energy cutoffs from the `SSSP library version 1.1 `_. - - -.. _SeeK-path: https://www.materialscloud.org/work/tools/seekpath - -.. K-point mesh is selected to have a minimum k-point density of 0.2 Å-1 - A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations - -The workflow should take ~10 minutes on your virtual machine. -You may notice that ``verdi process list`` now shows more than one entry. -While you wait for the workflow to complete, -let's start exploring its provenance. - -The full provenance graph obtained from ``verdi node graph generate`` will already be rather complex (you can try!), -so let's try browsing the provenance interactively instead. - -Start the AiiDA REST API: - -.. code:: bash - - verdi restapi - -and open the |provenance browser| (from the browser inside the virtual machine). - -.. |provenance browser| raw:: html - - Materials Cloud provenance browser - -.. note:: - - As of September 2019, the Materials Cloud provenance browser is still being developed, so some features might still not be - available or work as expected. - - -.. note:: - - The provenance browser is a Javascript application that connects to the AiiDA REST API. - Your data never leaves your computer. - -.. some general comment on importance of the graph? -.. a sentence on how to continue from here - -Browse your AiiDA database. - * Start by finding your Quantum ESPRESSO calculation (the type of node is called - a ``CalcJobNode`` in AiiDA, since it is run as a job on a scheduler). - Select ``Calculations`` in the left menu to filter for calculations only. - * Inspect the raw inputs and outputs of the calculation, and use the provenance - browser to explore the input and output nodes of the calculation and the whole - provenance of your simulations. - -.. note:: - - When perfoming calculations for a publication, you can export your provenance graph using ``verdi export create`` and upload it to the `Materials Cloud Archive `_, enabling your peers to explore the provenance of your calculations online. - -Once the workchain is finished, use ``verdi process show `` to inspect the ``PwBandStructureWorkChain`` and find the PK of its ``band_structure`` output. -Use this to produce a PDF of the band structure: - -.. code:: bash - - verdi data bands export --format mpl_pdf --output band_structure.pdf - - -.. figure:: include/images/si_bands.png - :width: 80% - - Band structure computed by the `PwBandStructure` workchain. - -.. note:: - The ``BandsData`` node does contain information about the Fermi energy, so the energy zero in your plot will be arbitrary. - You can produce a plot with the Fermi energy set to zero (as above) using the following code in a jupyter notebook: - - .. code:: ipython - - %matplotlib inline - import aiida - aiida.load_profile() - - from aiida.orm import load_node - - scf_params = load_node() # REPLACE with PK of "scf_parameters" output - fermi_energy = scf_params.dict.fermi_energy - - bands = load_node() # REPLACE with PK of "band_structure" output - bands.show_mpl(y_origin=fermi_energy, plot_zero_axis=True) - - - -What next? ----------- - -You now have a first taste of the type of problems AiiDA tries to solve. -Here are some options for how to continue: - - * Continue with the in-depth tutorial and learn more about the ``verdi``, ``verdi shell`` and ``python`` interfaces to AiiDA. - There is more than enough material to keep you busy for a day. - * Download `Quantum Mobile`_ virtual machine and try running the tutorial on your laptop instead. This will let you take the materials home and continue in your own time. - * Try `setting up AiiDA directly on your laptop `_. - - .. note:: **For advanced Linux & python users only**. - AiiDA depends on a number of services and software that require some skill to set up. - Unfortunately, we don't have the human resources to help you solve - issues related to your setup in a tutorial context. - - * Continue your work from other parts of the workshop, chat with participants and enjoy yourself :-) - - - .. _Quantum Mobile: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.08.0 diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/code.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/code.yml deleted file mode 100644 index e9bdaad2..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/code.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" -on_computer: true -computer: "localhost" -remote_abs_path: "/usr/local/bin/pw.x" -prepend_text: "ulimit -s unlimited" -append_text: " " diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/computer.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/computer.yml deleted file mode 100644 index 01938930..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/computer.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -description: "localhost" -label: "localhost" -hostname: "localhost" -transport: local -scheduler: "direct" -work_dir: "/home/max/.aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/ijs-config.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/ijs-config.yml deleted file mode 100644 index 9be56b95..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/ijs-config.yml +++ /dev/null @@ -1,6 +0,0 @@ ---- -username: # <<< Replace with the username on percolator.ijs.si that you have setup on the first day of the tutorial -key_filename: "~/.ssh/id_rsa_nsc" -timeout: 30 # timout for SSH before giving up -key_policy: RejectPolicy # Expect that you already connected once to the machine so it is in the known hosts, otherwise fail -safe_interval: 5 # Minimum time (s) between consecutive connections diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/ijs.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/ijs.yml deleted file mode 100644 index 175421fc..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/ijs.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -label: ijs -description: Percolator machine at the Jožef Stefan Institute -hostname: percolator.ijs.si -transport: ssh # connects via SSH -scheduler: slurm # use SLURM scheduler -work_dir: "/home/{username}/aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "8" -shebang: "#!/bin/bash" -prepend_text: "#SBATCH --reservation=qe2019" # Needed to use the correct reservation in SLURM -append_text: " " diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/openstack-config.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/openstack-config.yml deleted file mode 100644 index ffef5811..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/openstack-config.yml +++ /dev/null @@ -1,6 +0,0 @@ ---- -username: max -key_filename: "~/.ssh/id_rsa" -timeout: 30 # timout for SSH before giving up -key_policy: AutoAddPolicy # Expect that you already connected once to the machine so it is in the known hosts, otherwise fail -safe_interval: 5 # Minimum time (s) between consecutive connections diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/openstack.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/openstack.yml deleted file mode 100644 index caa5b733..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/openstack.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -label: openstack -description: Machine on the OpenStack cluster of the Swiss Supercomputing Center (CSCS) -hostname: 148.187.96.53 -transport: ssh # connects via SSH -scheduler: slurm # use SLURM scheduler -work_dir: "/home/{username}/aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "8" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/qe-openstack.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/qe-openstack.yml deleted file mode 100644 index c2891818..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/qe-openstack.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.3-pw" -description: "Quantum ESPRESSO 6.3" -input_plugin: "quantumespresso.pw" # input plugin for pw.x code -computer: "openstack" -on_computer: true -remote_abs_path: "/home/max/codes/q-e-90a2aa88298f5005b8ab8f5fcc354c5870481d68/bin/pw.x" -prepend_text: "ulimit -s unlimited" # set stacksize to unlimited -append_text: " " diff --git a/docs/pages/2019_Ljubljana/sections/include/configuration/qe.yml b/docs/pages/2019_Ljubljana/sections/include/configuration/qe.yml deleted file mode 100644 index 1880e8e4..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/configuration/qe.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1 (inside a Singularity container)" -input_plugin: "quantumespresso.pw" # input plugin for pw.x code -computer: "ijs" -on_computer: true -remote_abs_path: "/opt/qe-6.4.1/pw.x" # path to executable (inside singularity) -prepend_text: "ulimit -s unlimited" # set stacksize to unlimited -append_text: " " diff --git 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b/docs/pages/2019_Ljubljana/sections/include/images/process_functions.svg deleted file mode 100644 index fa89c991..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/images/process_functions.svg +++ /dev/null @@ -1,1996 +0,0 @@ - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - - - Float(0.98) - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - - CALL CALL - - - - RETURN - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - Float(0.98) - - - - - - - - - - - W1 - - - - - - CREATE RESCALED - - - - a) b) diff --git a/docs/pages/2019_Ljubljana/sections/include/images/qb_example_2.png b/docs/pages/2019_Ljubljana/sections/include/images/qb_example_2.png deleted file mode 100644 index 6a0c9117..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/qb_example_2.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/images/si_bands.png b/docs/pages/2019_Ljubljana/sections/include/images/si_bands.png deleted file mode 100644 index 80401fa9..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/si_bands.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/batio3/graph-full.png b/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/batio3/graph-full.png deleted file mode 100644 index f2c49b0e..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/batio3/graph-full.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/batio3/graph-input.png b/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/batio3/graph-input.png deleted file mode 100644 index b55ade76..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/batio3/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/graph.pdf b/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/graph.pdf deleted file mode 100644 index 50597454..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/graph.pdf and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/graph.svg b/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/graph.svg deleted file mode 100644 index aa4259db..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/graph.svg +++ /dev/null @@ -1,2678 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - diff --git a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/si/graph-full.png b/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/si/graph-full.png deleted file mode 100644 index 5cfe8472..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/si/graph-full.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/si/graph-input.png b/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/si/graph-input.png deleted file mode 100644 index 306d45d9..00000000 Binary files a/docs/pages/2019_Ljubljana/sections/include/images/verdi_graph/si/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sections/include/snippets/__init__.py b/docs/pages/2019_Ljubljana/sections/include/snippets/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_Ljubljana/sections/include/snippets/demo_bands.py b/docs/pages/2019_Ljubljana/sections/include/snippets/demo_bands.py deleted file mode 100644 index 6b24cf6f..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/snippets/demo_bands.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Compute a band structure with Quantum ESPRESSO - -Uses the PwBandStructureWorkChain provided by aiida-quantumespresso. -""" -from aiida.engine import submit -PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure') - -results = submit( - PwBandStructureWorkChain, - code=Code.get_from_string(""), # REPLACE - structure=load_node(), # REPLACE -) diff --git a/docs/pages/2019_Ljubljana/sections/include/snippets/demo_calcjob.py b/docs/pages/2019_Ljubljana/sections/include/snippets/demo_calcjob.py deleted file mode 100644 index 278731e3..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/snippets/demo_calcjob.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Run SCF calculation with Quantum ESPRESSO""" -from aiida.orm.nodes.data.upf import get_pseudos_from_structure -from aiida.engine import submit - -code = Code.get_from_string("") # REPLACE -builder = code.get_builder() - -# Select structure -structure = load_node() # REPLACE -builder.structure = structure - -# Define calculation -parameters = { - 'CONTROL': { - 'calculation': 'scf', # self-consistent field - }, - 'SYSTEM': { - 'ecutwfc': 30., # wave function cutoff in Ry - 'ecutrho': 240., # density cutoff in Ry - }, -} -builder.parameters = Dict(dict=parameters) - -# Select pseudopotentials -builder.pseudos = get_pseudos_from_structure(structure, '') # REPLACE - -# Define K-point mesh in reciprocal space -KpointsData = DataFactory('array.kpoints') -kpoints = KpointsData() -kpoints.set_kpoints_mesh([4,4,4]) -builder.kpoints = kpoints - -# Set resources -builder.metadata.options.resources = {'num_machines': 1} - -# Submit the job -calcjob = submit(builder) -print('Submitted CalcJob with PK=' + str(calcjob.pk)) diff --git a/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_create_diamond_fcc.py b/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_create_diamond_fcc.py deleted file mode 100644 index 0ad3d483..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_create_diamond_fcc.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import absolute_import -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - try: - symbol = element.value - except AttributeError: - symbol = str(element) - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure diff --git a/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_create_rescaled.py b/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_create_rescaled.py deleted file mode 100644 index c7d0a617..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_create_rescaled.py +++ /dev/null @@ -1,11 +0,0 @@ -from __future__ import absolute_import -from aiida.engine import workfunction - - -@workfunction -def create_rescaled(element, scale): - """Workfunction to create and immediately rescale a crystal structure of a given element.""" - structure = create_diamond_fcc(element) - rescaled = rescale(structure, scale) - - return rescaled diff --git a/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_rescale.py b/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_rescale.py deleted file mode 100644 index 99761a77..00000000 --- a/docs/pages/2019_Ljubljana/sections/include/snippets/workflows_rescale.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import absolute_import -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_Ljubljana/sections/setup.rst b/docs/pages/2019_Ljubljana/sections/setup.rst deleted file mode 100644 index 43605994..00000000 --- a/docs/pages/2019_Ljubljana/sections/setup.rst +++ /dev/null @@ -1,206 +0,0 @@ -Getting set up (if you are using a cloud machine) -================================================= - -Note: if you are working within a local VirtualBox machine -(like the Quantum Mobile), you can skip this page and -you can simply work within the VM. - -.. note:: The credentials of the custom VM that you can download - from the main page of this tutorial material - are the following: username: ``user``, password: ``qe2019``. - - -.. _2019_lbj_connect: - -Connect to your virtual machine -------------------------------- - -You should each have received from the instructors: - -- an IP address -- a private SSH key ``aiida_tutorial_NUM`` -- a public SSH key ``aiida_tutorial_NUM.pub`` - -The steps below explain how to use these in order to connect to your -personal virtual machine (VM) on Amazon Elastic Cloud 2 -using the `Secure Shell `_ protocol. -The software on this VM is based on `Quantum Mobile -`_ and already includes a -pre-configured AiiDA installation as well as some test data for the tutorial. - -.. note:: - - If you decide to work in pairs, one of you should forward their credentials - to the other and you should both use the same IP address and ssh key. - Since you will be sharing the VM and user account, be careful not to delete - the work of your colleague. - - -.. note:: - - Please make sure you are not running any jupyter notebook on your computer. - In this case, please kill all running kernels first. Otherwise, the port tunneling - that we are setting up will not work. - -Linux and MacOS -~~~~~~~~~~~~~~~ - -It's recommended for you to place the ssh key you received in a folder -dedicated to your ssh configuration, to do so: - -- If not already present, create a ``.ssh`` directory in your home - (``mkdir ~/.ssh``), and set its permissions: ``chmod 700 ~/.ssh`` - -Add the following block your -``~/.ssh/config`` file: - - .. code:: bash - - Host aiidatutorial - Hostname IP_ADDRESS - User max - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - ServerAliveInterval 120 - -replacing the IP address (``IP_ADDRESS``) and the ``NUM`` by -the one you received. - -Afterwards you can connect to the server using this simple command: - -.. code:: console - - ssh aiidatutorial - -.. note:: - - Here's a copy-paste ready command for you to use directly with zero - configuration: - - .. code:: console - - ssh \ - -L 8888:localhost:8888 \ - -L 5000:localhost:5000 \ - -o ServerAliveInterval=120 \ - -X -C \ - max@IP_ADDRESS - -.. note:: - - On MacOS you need to install `XQuartz `_ - in order to use X-forwarding. - -Windows -~~~~~~~ - -If you're running Windows 10, you may want to consider `installing the Windows Subsystem for Linux `_ (and then follow the instructions above). Alternatively: - -- Install the `PuTTY SSH client `_. - -- Run PuTTY - - - Put the given IP address as hostname, type ``aiidatutorial`` in "Saved Sessions" - and click "Save". - - Go to Connection > Data and put ``max`` as autologin username. - - Go to Connection > SSH > Tunnels, type ``8888`` in the - "Source Port" box, type ``localhost:8888`` in "Destination" and click "Add". - - Repeat the previous step for port ``5000`` instead of ``8888``. - - Go back to the "Session" screen, select "aiidatutorial" and click "Save" - - Finally, click "Open" (and click "Yes" on the putty security alert - to add the VM to your known hosts). - You should be redirected to a bash terminal on the virtual machine. - -.. note:: - Next time you open PuTTY, select ``aiidatutorial`` and click "Load" - before clicking "Open". - - -In order to enable X-forwarding: - -- Install the `Xming X Server for Windows `_. - -- Configure PuTTy as described in the `Xming wiki `_. - -Start jupyter -------------- - -Once connected to your virtual machine, type in the remote terminal - -.. code:: bash - - workon aiida - -This will enable the virtual environment in which AiiDA is installed, -allowing you to use AiiDA. Now type in the same shell: - -.. code:: bash - - jupyter notebook --no-browser - -This will run a server with a web application called ``jupyter``, which -is used to create interactive python notebooks. -In order to connect to the jupyter notebook server: - - - copy the URL that has been printed to the terminal (similar to ``http://localhost:8888/?token=2a3ba37cd1...``) - - open a web browser **on your laptop** and paste the URL - - You will see a list of folders on your personal VM. - -While keeping the first ``ssh`` connection running, open another ``ssh`` -connection in a second terminal and type ``workon aiida`` here too. This -terminal is the one we will actually use in this tutorial. - -.. note:: - - Our SSH configuration assumes that ``jupyter`` will serve the notebooks on port 8888. - If you want to serve notebooks on different ports, you'll also need to adjust - the SSH configuration. - -Additional useful notes ------------------------ -Read through the next sections if you are interested in knowing how to download files -into the virtual machine, how to troubleshoot typical problems, or how to get additional help. - -Downloading files -~~~~~~~~~~~~~~~~~ - -Throughout this tutorial, you will encounter links to download python scripts, jupyter notebooks and more. -These files should be downloaded to the environment/working directory you use to run the tutorial. -In particular, when running the tutorial on a linux virtual machine, copy the link address and download the files to the machine using the ``wget`` utility on the terminal: - - wget - -where you replace ```` with the actual HTTPS link that you copied from the tutorial text in your browser. -This will download that file in your current directory. - - -Troubleshooting -~~~~~~~~~~~~~~~ - -- If you get errors ``ImportError: No module named aiida`` or - ``No command ’verdi’ found``, double check that you have loaded the - virtual environment with ``workon aiida`` before launching ``python``, - ``ipython`` or the ``jupyter`` notebook server. - -- If your browser cannot connect to the jupyter notebook server, check that - you have correctly configured SSH tunneling/forwarding as described - above. Keep in mind that you need to start the jupyter server from the - terminal connected to the VM, while the web browser should be opened locally - on your laptop. - -- See the `jupyter notebook documentation `_ for compatibility of jupyter with various web browsers. - -Getting help -~~~~~~~~~~~~ - -There are a number of helpful resources available to you for getting more information about AiiDA. -Please consider: - - * consulting the extensive `AiiDA documentation `_ - * opening a new issue on the `tutorial issue tracker `_ - * asking your neighbor - * asking a tutor - -.. Add here a link if you are creating a slack channel for the tutorial diff --git a/docs/pages/2019_Ljubljana/sponsors/jozef-stefan.png b/docs/pages/2019_Ljubljana/sponsors/jozef-stefan.png deleted file mode 100644 index 5b08f939..00000000 Binary files a/docs/pages/2019_Ljubljana/sponsors/jozef-stefan.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sponsors/max.png b/docs/pages/2019_Ljubljana/sponsors/max.png deleted file mode 100644 index 2ebda134..00000000 Binary files a/docs/pages/2019_Ljubljana/sponsors/max.png and /dev/null differ diff --git a/docs/pages/2019_Ljubljana/sponsors/qef.png b/docs/pages/2019_Ljubljana/sponsors/qef.png deleted file mode 100644 index 8891f886..00000000 Binary files a/docs/pages/2019_Ljubljana/sponsors/qef.png and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet.pdf b/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet.pdf deleted file mode 100644 index 7bd31d3d..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet.pdf and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet_back.pdf b/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet_back.pdf deleted file mode 100644 index dbcc71c6..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet_back.pdf and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet_back.svg b/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet_back.svg deleted file mode 100644 index a10204a4..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/cheatsheet_back.svg +++ /dev/null @@ -1,15618 +0,0 @@ - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - -     Main attributes and methods -   - - - - - - - - - - - - - - - The - cheat sheet - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Note: each derived class inherits all the methods of the parent class -   - - - - Node - pklabeluuidctimemtimeget_incoming()get_outgoing()inputsoutputsattributesget_attribute(k)extrasget_extra(<k>)set_extra(<k>,<v>) get_comments()add_comment(<c>) store() Node IDShort labelUnique IDCreation timeModification timeGet inputGet outputAll inputs generatorAll outputs generatorQueryable attributesAttribute 'k'Queryable extrasExtra 'k'Set extra k = vAll commentsAdd comment with content <c>Save node in DB - - - - Code - load_code(<id>) get_builder()  Load code using pk, UUID, or labelReturn new builder using this code  - - - - Data - export()_exportcontent()importfile()importstring()  Export to fileExport to stringImport from fileImport from string - - - - - - Dict - dict.<k>keys()get_dict()set_dict(<dict>)  Get value for key 'k'Get all keys generatorGet all key/valuesReplace all key/values - - - - ArrayData - get_arraynames()get_array(<n>)set_array(<n>,<a>)   Names of all arraysGet array named 'n'Set/store array 'a' with name 'n' - - - - StructureData - cellsiteskinds pbc get_formula()get_cell_volume()convert(<fmt>) set_cell(<c>)set_ase(<a>)set_pymatgen(<p>) append_atom(symbols=<symb>,position=<p>) Lattice vectorsAtomic sitesSpecies with masses, symbols, ...Periodic bound. cond. along each axisChemical formulaCompute cell volumeConvert to ASE, pymatgen, ...Set lattice vectorsCreate cell from ASECreate cell from pymatgenAdd atom of type 'symb' at position 'p'   - - - - - - KpointsData - set_kpoints(<k>)  get_kpoints() set_kpoints_mesh( <m>)get_kpoints_mesh()   Set an explicit list of kpoints 'k' (optionally with weights)Get explicit list of kpts(if stored explicitly)Set an implicit mesh (e.g. 'm'=3x2x5)Get the implicit mesh(if stored implicitly)    - - - CalcJobNode - process_stateexit_statusis_finishedis_failedcomputer inputs.codeget_job_id()get_options() res.<k> Calc. process stateExit status or int codeHas calc. finished?Has calc. failed?Computer where it is runningCode used to runScheduler job IDGet # machines, MPI procs per machine, ...Value of parsed output 'k' v19.05 - - - WorkChainNode - get_state()computer inputs.codeget_job_id()get_options() res.<k> Calculation stateComputer where it is runningCode used to runScheduler job IDGet # machines, MPI procs per machine, ...Value of parsed output 'k' aiida-core v1.0.0b3 - - To load an existing node: load_node(<id>)Where <id> may be either the pk, UUID, or label To load a class, either import it from aiida.orm or use the DataFactory (returning Data subclasses) or the CalculationFactory (returning CalcJobNode subclasses) - - - - - Import aiida-core Node classes from aiida.orm using their class name: from aiida.orm import CalcJobNodefrom aiida.orm import Dict Import Data classes via the DataFactory using <label>s:: KpointsData = DataFactory("array.kpoints")MyData = DataFactory("plugin.my") Other <label>s for Data:"upf", "array", "array.bands", "dict", ... Import CalcJob classes via the CalculationFactory: PwCalculation = CalculationFactory("quantumespresso.pw") Other <label>s for Calculations:"quantumespresso.ph", "vasp.scf", ... Import WorkChain classes via the WorkflowFactory. ORM and the Factories - - - The main AiiDA Node subclasses - - Node - - - CalculationNode - - - Data - - - - - - - WorkFunctionNode - - - - CalcFunctionNode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... - ArrayData - ... - StructureData - UpfData - KpointsData - BandsData - TrajectoryData - WorkflowNode - - - - CalcJobNode - - - - - WorkChainNode - - - - - - - - - - - Code - - - - Dict, Float, Int, ... - - - ProcessNode - - - - - - - Importing classes - Useful links: Tutorial website:aiida-tutorials.readthedocs.io AiiDA documentation:aiida-core.readthedocs.io/en/latest - - - - - - - - Test1 - Test1 - Test1 - Test1 - Test1 - - diff --git a/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/required_fonts/HelveticaNeue-otf/HelveticaNeue-Black.otf b/docs/pages/2019_MARVEL_Psik_MaX/cheatsheet/required_fonts/HelveticaNeue-otf/HelveticaNeue-Black.otf deleted file mode 100644 index 981ea5a1..00000000 Binary files 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a/docs/pages/2019_MARVEL_Psik_MaX/index.rst b/docs/pages/2019_MARVEL_Psik_MaX/index.rst deleted file mode 100644 index 1931f7b3..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/index.rst +++ /dev/null @@ -1,88 +0,0 @@ -.. _EPFL 2019 Homepage: - -2019, EPFL, Lausanne, Switzerland -================================= - - -+-----------------+----------------------------------------------------------------------------+ -| Related resources | -+=================+============================================================================+ -| Virtual Machine | `Quantum Mobile 19.05.0-tutorial`_ | -+-----------------+----------------------------------------------------------------------------+ -| python packages | `aiida-core 1.0.0b3`_, `aiida-quantumespresso 3.0.0a3`_, `aiidalab 19.05`_ | -+-----------------+----------------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.3`_ | -+-----------------+----------------------------------------------------------------------------+ -| videos | `recorded lectures and invited talks`_ | -+-----------------+----------------------------------------------------------------------------+ - -.. _Quantum Mobile 19.05.0-tutorial: https://github.com/marvel-nccr/quantum-mobile/releases/tag/tutorial-2019-05 -.. _aiida-core 1.0.0b3: https://pypi.org/project/aiida-core/1.0.0b3 -.. _aiida-quantumespresso 3.0.0a3: https://github.com/aiidateam/aiida-quantumespresso/releases/tag/v3.0.0a3 -.. _aiidalab 19.05: https://github.com/aiidalab/aiidalab-metapkg/releases/tag/tutorial-2019-05 -.. _Quantum Espresso 6.3: https://github.com/QEF/q-e/releases/tag/qe-6.3 -.. _`recorded lectures and invited talks`: https://www.youtube.com/playlist?list=PL19kfLn4sO_-QtPaHAA8KByFluT2vvlG0 - -These are the notes of the `AiiDA tutorial on writing reproducible workflows for computational materials science `__ which took place from May 21-24, 2019 at EPF Lausanne, Switzerland. - -While participants of the tutorial used virtual machines on a cloud service, you can follow the tutorial just as well using the Quantum Mobile VirtualBox image linked above. The image already contains all the required software. - -Writing reproducible workflows for computational materials science -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Sections --------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/setup - ./sections/verdi_cmdline - ./sections/verdi_shell - ./sections/calculations - ./sections/querybuilder - ./sections/workflows - ./sections/bands - -Appendices ----------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/appendix_computer_code_setup - ./sections/appendix_input_validation - ./sections/appendix_restarting_calculations - ./sections/appendix_queries - ./sections/appendix_workflow_logic - -Acknowledgements ----------------- - -The `AiiDA tutorial on writing reproducible workflows for computational materials science `__ was made possible by support from MARVEL, Psi-k, MaX, swissuniversitites and INTERSECT, and kindly hosted by EPFL. - -.. image:: sponsors/marvel.png - :target: http://nccr-marvel.ch - :width: 105px - -.. image:: sponsors/psi-k-crystal_low.png - :target: http://psi-k.net - :width: 55px - -.. image:: sponsors/max.png - :target: http://www.max-centre.eu/ - :width: 135px - -.. image:: sponsors/intersect.png - :target: http://intersect-project.eu/ - :width: 125px - -.. image:: sponsors/swissuniversities.png - :target: https://swissuniversities.ch - :width: 115px - -.. image:: sponsors/epfl.png - :target: https://epfl.ch - :width: 105px diff --git a/docs/pages/2019_MARVEL_Psik_MaX/notebooks/bandstructure.ipynb b/docs/pages/2019_MARVEL_Psik_MaX/notebooks/bandstructure.ipynb deleted file mode 100644 index 35032823..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/notebooks/bandstructure.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A real world workchain example: electronic band structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%aiida\n", - "\n", - "from datetime import datetime, timedelta\n", - "\n", - "from aiida.tools.dbimporters.plugins.cod import CodDbImporter\n", - "from aiida.engine import launch\n", - "\n", - "PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating the electronic band structure with an AiiDA workchain\n", - "This tutorial will show how useful a workchain can be in performing a well defined task, such as computing and visualizing the electronic band structure for a simple crystal structure. The goal of this tutorial is not to show you the intricacies of the actual workchain itself, but rather to serve as an example that workchains can simplify standard workflows in computational materials science. The workchain that we will use here will employ Quantum Espresso's pw.x code to calculate the charge densities for several crystal structures and compute a band structure from those. Many choices that normally face the researcher before being able to perform this calculation, such as the selection of suitable pseudo potentials, energy cutoff values, k-point grids and k-point paths along high symmetry points, are now performed automatically by the workchain. All that remains for the user to do is to simply define a structure, pass it to the workchain and sit back!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we import the crystal structure of Al as an example, and run the PwBandStructureWorkChain for that structure. The estimated run time is noted in a comment in the calculation cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading the COD importer so we can directly import structure from COD id's\n", - "importer = CodDbImporter()\n", - "# Make sure here to define the correct codename that corresponds to the pw.x code installed on your machine of choice\n", - "codename = 'qe-6.3-pw@localhost'\n", - "code = Code.get_from_string(codename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Importing example crystal structures from COD to AiiDA structure objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Al COD ID='9008460'\n", - "structure_Al = importer.query(id='9008460')[0].get_aiida_structure()\n", - "\n", - "structure_Al.get_formula()\n", - "\n", - "# The following structure can be used instead of Al, but will take much longer on the AWS machine.\n", - "# CaF2 COD ID='1000043' -- approximately 1/2 hour to run\n", - "# h-BN COD ID='9008997' -- approximately 45 mins to run\n", - "# GaAs COD ID='9008845' -- approximately 2 hours to run " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now we run the bandstructure workchain for the selected structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bandstructure workchain follows the following protocol:\n", - "* Determine the primitive cell of the input structure\n", - "* Run a vc-relax to relax the structure\n", - "* Refine the symmetry of the relaxed structure to ensure the primitive cell is used and run a self-consistent field calculation on it\n", - "* Run a non self-consistent field band structure calculation along a path of high symmetry k-points determined by [seekpath](http://materialscloud.org/tools/seekpath)\n", - "\n", - "Numerical parameters for the default 'theos-ht-1.0' protocol are determined as follows: \n", - "* Suitable pseudopotentials and energy cutoffs are automatically searched from the [SSSP library](http://materialscloud.org/sssp) installed on your machine (it uses the efficiency version 1.1).\n", - "* K-point mesh is selected to have a minimum k-point density of 0.2 Å-1\n", - "* A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations\n", - "\n", - "In case the pseudopotentials are not installed, they can be downloaded in a terminal as:\n", - "\n", - " wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz\n", - " tar -zxvf SSSP_efficiency_pseudos.tar.gz\n", - " \n", - "The protocol looks for a UPF file with a specific hash code, that is unique for each different file. \n", - "You can check that you have the right\n", - "one by performing a search in the database:\n", - "\n", - " qb=QueryBuilder()\n", - " qb.append(UpfData, filters={'attributes.md5':{'==':'cfc449ca30b5f3223ec38ddd88ac046d'}})\n", - " len(qb.all())\n", - "\n", - "'md5' is a searchable attribute of the pseudopotential data object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fermi_energy = results['scf_parameters'].dict.fermi_energy\n", - "results['band_structure'].show_mpl(y_origin=fermi_energy, plot_zero_axis=True)\n", - "\n", - "print(\"\"\"Final crystal symmetry: {spacegroup_international} (number {spacegroup_number})\n", - "Extended Bravais lattice symbol: {bravais_lattice_extended}\n", - "The system has inversion symmetry: {has_inversion_symmetry}\"\"\".format(\n", - " **results['seekpath_parameters'].get_dict()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to use a different pseudopotential family (or version of the family) (for instance [SSSP v1.0](https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz) instead of the default SSSP v1.1) you can pass an additional parameter when calling the WorkChain, as follows:\n", - " \n", - " protocol = Dict(dict={\n", - " 'name':'theos-ht-1.0', \n", - " 'modifiers': {\n", - " 'pseudo' : 'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - "\n", - "(note that only some values are accepted for pseudo, that you can find [here](https://github.com/aiidateam/aiida-quantumespresso/blob/b02250146576eb573ccb45d05047075f54853f9d/aiida_quantumespresso/utils/protocols/pw.py#L24))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al,\n", - " protocol=Dict(dict={\n", - " 'name':'theos-ht-1.0',\n", - " 'modifiers': {\n", - " 'pseudo':'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/pages/2019_MARVEL_Psik_MaX/notebooks/querybuilder-template.ipynb b/docs/pages/2019_MARVEL_Psik_MaX/notebooks/querybuilder-template.ipynb deleted file mode 100644 index 65dd70ae..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/notebooks/querybuilder-template.ipynb +++ /dev/null @@ -1,973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AiiDA's QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from datetime import datetime, timedelta\n", - "import numpy as np\n", - "from aiida import load_profile\n", - "from matplotlib import gridspec, pyplot as plt\n", - "load_profile()\n", - "from aiida.orm import load_node, Node, Group, Computer, User, CalcJobNode, Code\n", - "from aiida.plugins import CalculationFactory, DataFactory\n", - "\n", - "PwCalculation = CalculationFactory('quantumespresso.pw')\n", - "StructureData = DataFactory('structure')\n", - "KpointsData = DataFactory('array.kpoints')\n", - "Dict = DataFactory('dict')\n", - "UpfData = DataFactory('upf')\n", - "\n", - "def plot_results(query_res):\n", - " \"\"\"\n", - " :param query_res: The result of an instance of the QueryBuilder\n", - " \"\"\"\n", - " smearing_unit_set,magnetization_unit_set,pseudo_family_set = set(), set(), set()\n", - " # Storing results:\n", - " results_dict = {}\n", - " for pseudo_family, formula, smearing, smearing_units, mag, mag_units in query_res:\n", - " if formula not in results_dict:\n", - " results_dict[formula] = {}\n", - " # Storing the results:\n", - " results_dict[formula][pseudo_family] = (smearing, mag)\n", - " # Adding to the unit set:\n", - " smearing_unit_set.add(smearing_units)\n", - " magnetization_unit_set.add(mag_units)\n", - " pseudo_family_set.add(pseudo_family)\n", - "\n", - " # Sorting by formula:\n", - " sorted_results = sorted(results_dict.items())\n", - " formula_list = next(zip(*sorted_results))\n", - " nr_of_results = len(formula_list)\n", - "\n", - " # Checks that I have not more than 3 pseudo families.\n", - " # If more are needed, define more colors\n", - " #pseudo_list = list(pseudo_family_set)\n", - " if len(pseudo_family_set) > 3:\n", - " raise Exception('I was expecting 3 or less pseudo families')\n", - "\n", - " colors = ['b', 'r', 'g']\n", - "\n", - " # Plotting:\n", - " plt.clf()\n", - " fig=plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None)\n", - " gs = gridspec.GridSpec(2,1, hspace=0.01, left=0.1, right=0.94)\n", - "\n", - " # Defining barwidth\n", - " barwidth = 1. / (len(pseudo_family_set)+1)\n", - " offset = [-0.5+(0.5+n)*barwidth for n in range(len(pseudo_family_set))]\n", - " # Axing labels with units:\n", - " yaxis = (\"Smearing energy [{}]\".format(smearing_unit_set.pop()),\n", - " \"Total magnetization [{}]\".format(magnetization_unit_set.pop()))\n", - " # If more than one unit was specified, I will exit:\n", - " if smearing_unit_set:\n", - " raise ValueError('Found different units for smearing')\n", - " if magnetization_unit_set:\n", - " raise ValueError('Found different units for magnetization')\n", - " \n", - " # Making two plots, the top one for the smearing, the bottom one for the magnetization\n", - " for index in range(2):\n", - " ax=fig.add_subplot(gs[index])\n", - " for i,pseudo_family in enumerate(pseudo_family_set):\n", - " X = np.arange(nr_of_results)+offset[i]\n", - " Y = np.array([thisres[1][pseudo_family][index] for thisres in sorted_results])\n", - " ax.bar(X, Y, width=0.2, facecolor=colors[i], edgecolor=colors[i], label=pseudo_family)\n", - " ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15*index+5)\n", - " ax.set_xlim(-0.5, nr_of_results-0.5)\n", - " ax.set_xticks(np.arange(nr_of_results))\n", - " if index == 0:\n", - " plt.setp(ax.get_yticklabels()[0], visible=False)\n", - " ax.xaxis.tick_top()\n", - " ax.legend(loc=3, prop={'size': 18})\n", - " else:\n", - " plt.setp(ax.get_yticklabels()[-1], visible=False)\n", - " for i in range(0, nr_of_results, 2):\n", - " ax.axvspan(i-0.5, i+0.5, facecolor='y', alpha=0.2)\n", - " ax.set_xticklabels(list(formula_list),rotation=90, size=14, ha='center')\n", - " plt.show()\n", - "\n", - "def generate_query_graph(qh, out_file_name):\n", - "\n", - " def draw_vertice_settings(idx, vertice, **kwargs):\n", - " \"\"\"\n", - " Returns a string with all infos needed in a .dot file to define a node of a graph.\n", - " :param node:\n", - " :param kwargs: Additional key-value pairs to be added to the returned string\n", - " :return: a string\n", - " \"\"\"\n", - " if vertice['entity_type'].startswith('process'):\n", - " shape = \"shape=polygon,sides=4\"\n", - " elif vertice['entity_type'].startswith('data.code'):\n", - " shape = \"shape=diamond\"\n", - " else:\n", - " shape = \"shape=ellipse\"\n", - " filters = kwargs.pop('filters', None)\n", - " additional_string = \"\"\n", - " if filters:\n", - " additional_string += '\\nFilters:'\n", - " for k,v in filters.items():\n", - " additional_string += \"\\n {} : {}\".format(k,v)\n", - "\n", - "\n", - " label_string = \" ('{}')\".format(vertice['tag'])\n", - "\n", - " labelstring = 'label=\"{} {}{}\"'.format(\n", - " vertice['entity_type'], #.split('.')[-2] or 'Node',\n", - " label_string,\n", - " additional_string)\n", - " #~ return \"N{} [{},{}{}];\".format(idx, shape, labelstring,\n", - " return \"{} [{},{}];\".format(vertice['tag'], shape, labelstring)\n", - " nodes = {v['tag']:draw_vertice_settings(idx, v, filters=qh['filters'][v['tag']]) for idx, v in enumerate(qh['path'])}\n", - " links = [(v['tag'], v['joining_value'], v['joining_keyword']) for v in qh['path'][1:]]\n", - "\n", - " with open('temp.dot','w') as fout:\n", - " fout.write(\"digraph G {\\n\")\n", - " for l in links:\n", - " fout.write(' {} -> {} [label=\" {}\"];\\n'.format(*l))\n", - " for _, n_values in nodes.items():\n", - " fout.write(\" {}\\n\".format(n_values))\n", - "\n", - " fout.write(\"}\\n\")\n", - " import os\n", - " os.system('dot temp.dot -Tpng -o {}'.format(out_file_name))\n", - "\n", - "def store_formula_in_extra():\n", - " from aiida.orm import QueryBuilder\n", - " qb = QueryBuilder()\n", - " qb.append(StructureData, filters={'extras':{'!has_key':'formula'}})\n", - " for structure, in qb.all():\n", - " structure.set_extra('formula', structure.get_formula(mode='count'))\n", - "\n", - "store_formula_in_extra()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction to the QueryBuilder\n", - "As you will use `AiiDA` to run your calculations, the database that stores all the data and the provenance will quickly grow to be very large. To help you find the needle that you might be looking for in this big haystack, we need an efficient search tool. `AiiDA` provides a tool to do exactly this: the `QueryBuilder`. The `QueryBuilder` acts as the gatekeeper to your database, to whom you can ask questions about the contents of your database (also referred to as queries), by specifying what are looking for. In this part of the tutorial, we will focus on how to use the `QueryBuilder` to make these queries and understand/use the results.\n", - "\n", - "In order to use the `QueryBuilder`, we first need to import it. We can accomplish this by executing the `import` statement in the following cell. Go ahead and select the next cell, and press `Shift+Enter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida.orm import QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we can ask the `QueryBuilder` questions about our database, we first need to create an instance of it (i.e., we create a new query):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an instance of our `QueryBuilder` which we named `qb`, we are ready to start asking about the content of our database. For example, we may want to know exactly how many nodes there are in our database. To tell `qb` that we are interested in all the occurrences of the `Node` class in our database, we `append` it to the list of objects it should find. \n", - "\n", - "*Note*: The method is called `append` because, as we will see later, you can append multiple nodes to a `QueryBuilder` instance consecutively to search in the graph, as if you had a list: what we are doing is querying a graph, and for every vertice of the graph in our subquery, we will use one `append` call. But we'll see this use better in a few steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(Node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now narrowed down the scope of `qb` to just the nodes that are present in our database. To learn how many nodes there are exactly, all we have to do is to ask `qb` to count them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now as you may have learned in previous sections of the tutorial, nodes come in different kinds and flavors. For example, all our crystal structures that we have stored in the database are saved in nodes that are of the type `StructureData`. If instead of all the nodes, we would rather like to count only the crystal structure nodes, we simply tell our `QueryBuilder` to narrow its scope only to objects of type `StructureData`. Since we want to create a new independent query, we must create a new instance of the `QueryBuilder`. In the next cell, we have typed part of the code to count all the structure nodes. See if you can finish the line with the comment, to tell the `QueryBuilder` that you are only interested in `StructureData` nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "qb.append() # How do we finish this line to tell the query builder to count only the structure nodes?\n", - "qb.count()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.count()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of just counting how many structure nodes we have, we may also actually want to see some of them. This is as easy as telling our `QueryBuilder` that we are not interested in the `count` but rather that we want to retrieve `all` the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this command is very literal and does in fact retrieve **all** the structure nodes that are stored in your database, which may be very slow if your database becomes very large. One solution is to simply tell the `QueryBuilder` that we are, for example, only interested in 5 structure nodes. This can be done with the `limit` method as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to simply use the concept of array slicing, native to python, to specify a subset of the total return set to be returned. Notice that this example can be very slow in big databases. When you want performance, use the functionality native to the QueryBuilder like `limit`, that limit the number of results directly at the database level!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(None)\n", - "qb.all()[:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to know a little bit more about the retrieved structure nodes, we can loop through our results. This allows you, for instance, to print the formula of the structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "for structure, in qb.all():\n", - " print(structure.get_formula())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just a simple example how we can employ the `QueryBuilder` to get details about the contents of our database. We have now seen simple queries for the `Node` and `StructureData` classes, but the same rules apply to all the `AiiDA` node classes. For example we may want to count the number of entries for each of the node classes in the following list:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class_list = [Node, StructureData, KpointsData, Dict, UpfData, Code]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the tools we have learned so far, we can build a table of the number of occurrences of each of these node classes that are stored in our database. We simply loop over the `class_list` and create a `QueryBuilder` for each and count the entries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for class_name in class_list:\n", - " qb = QueryBuilder()\n", - " qb.append(class_name)\n", - "#TUT_USER_START\n", - " print() # Finish this line to print the results!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - " print('{:>15} | {:6}'.format(class_name.__name__, qb.count()))\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, you should see something like the following, where of course the numbers may differ for your database\n", - "\n", - "Class name | Entries\n", - "---------------|--------\n", - " Node | 10273 \n", - " StructureData | 271 \n", - " KpointsData | 953 \n", - " Dict | 2922 \n", - " UpfData | 85 \n", - " Code | 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Projection and filters\n", - "Up until now we have always asked the `QueryBuilder` to return the node class. However, we might not necessarily be interested in all the node's properties, but rather just a selected set or even just a single property. We can tell the `QueryBuilder` which properties we would like to be returned, by asking it to **project** those properties in the result. For example, we may only want to get the `uuid`s of a set of nodes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['uuid'])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By using the `project` keyword in the `append` call, we are informing the `QueryBuilder` that we are only interested in the `uuid` property of the `Node` class. Note that the value that we assign to `project` is a list, since we may want to specify more than one property. See if you can get the `QueryBuilder` to return *both* the `id` and the `uuid` of the first 5 `Node`s in the following cell.\n", - "\n", - "**Note**: in the context of the QueryBuilder, the `id` is the `pk` of the node." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_USER_START\n", - "qb.append(Node, project=)#? What should the value be for the project key\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Node, project=['id', 'uuid'])\n", - "#TUT_SOLUTION_END\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To give you an idea of the various properties that you can project for some of the `AiiDA` node classes you can consult the following table.\n", - "Note that this is by no means an exhaustive list:\n", - "\n", - "Class | Properties\n", - "---------|-----------\n", - "Node | `id`, `uuid`, `node_type`, `label`, `description`, `ctime`, `mtime`\n", - "Computer | `id`, `uuid`, `name`, `hostname`, `description`, `transport_type`, `scheduler_type`\n", - "User | `id`, `email`, `first_name`, `last_name`, `institution`\n", - "Group | `id`, `uuid`, `label`, `type_string`, `time`, `description`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same properties can also be used to *filter* for specific nodes in your database. Indeed, up until now, we only asked the `QueryBuilder` to return *all* the instances of a certain type of node, or at best a limited number of those (without specifying which ones). But in general we might be interested in a very specific node. For example, we may have the `id` of a certain node and we would like to know when it was created and last modified. We can tell the `QueryBuilder` to select nodes that only match that criterion, by telling it to **filter** based on that property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['ctime', 'mtime'], filters={'id': {'==': 1}})\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the syntax of the `filters` keyword. The value is a dictionary, where the keys indicate the node property that it is supposed to operate on, in this case the `id` property. The value of that key is again itself a dictionary, where the key indicates the logical operator `==` and the value corresponds to the value of the property.\n", - "\n", - "You may have multiple criteria that you want to filter for, in which case you can use the logical `or` and `and` operators. Let's say, for example, that you want the `QueryBuilder` to retrieve all the `StructureData` nodes that have a certain `label` **and** were created no longer than 12 days ago. You can translate this criterion by making use of the `and` operator which allows you to specify multiple filters that all have to be satisfied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(\n", - " Node, \n", - " filters={\n", - " 'and': [\n", - " {'ctime': {'>': datetime.now() - timedelta(days=12)}},\n", - " {'label': {'==':'graphene'}}\n", - " ]\n", - " }\n", - ")\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will have noticed that the `>` operator, and its related operators, can work with python `datetime` objects. These are just a few of the operators that `QueryBuilder` understands. Below you find a table with some of the logical operators that you can use:\n", - "\n", - "Operator | Data type | Example | Description\n", - "---------------------|-----------------------|------------------------------------|------------------\n", - "`==` | all | `{'==': '12'}` | equality operator\n", - "`in` | all | `{'in':['FINISHED', 'PARSING']}` | member of a set\n", - "`<`, `>`, `<=`, `>=` | float, int, datetime | `{'>': 5.2}` | size comparison operator\n", - "`like` | char, str | `{'like': 'calculation%'}` | string comparison, `%` is wildcard\n", - "`ilike` | char, str | `{'ilike': 'caLCulAtion%'}` | string comparison, capital insensitive\n", - "`or` | | `{'or': [{'<': 5.3}, {'>': 6.3}]}` | logical or operator\n", - "`and` | | `{'and': [{'>=': 2}, {'<=': 6}]}` | logical and operator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, try to write a query below that will retrieve all `Group` nodes whose `label` property starts with the string `tutorial`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your query here\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'tutorial%'}})\n", - "qb.limit(5)\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining relationships between query clauses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how we can use the `QueryBuilder` to search the database for entries of a specific node type, potentially projecting only specific properties and filtering for certain property values. However, our nodes do not live in a vacuum, but they are part of a graph and thus are linked one another. Therefore, we typically want to be able to search for nodes based on a certain relationship that they might have with other nodes. Consider for example that you have a `StructureData` node that was produced by some calculation. How would we be able to retrieve that calculation?\n", - "\n", - "To accomplish this, we need to be able to tell the `QueryBuilder` what the relationship is between the nodes that we are interested in. With the `QueryBuilder`, we can do the following to find all the structure nodes that have been created as an output by a `PwCalculation` process.\n", - "\n", - "**IMPORTANT NOTE**: In the graph, we are not looking for a `PwCalculation` process (since processes do not live in the graph, as you have learnt). We are actually looking for a `CalcJobNode` whose `process_type` column indicates that it was run by a `PwCalculation` process. Since this is a very common pattern, the `QueryBuilder` allows to directly append the `PwCalculation` process class as a shortcut, but it internally unwraps this into a query for a `CalcJobNode` with the appropriate filter on the `process_type`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are looking for pairs of nodes, we need to `append` the second node as well to the `QueryBuilder` instance. In the next line, to specify the relationship between the nodes, we need to be able to reference back to the `CalcJobNode` that is matched by the previous clause: therefore, we give it a `tag` with the `tag` keyword." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(StructureData, with_incoming='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal was to find `StructureData` nodes, so we `append` that to the `qb`. However, we didn't want to find just any `StructureData` nodes; they had to be an output of `PwCalculation`. \n", - "\n", - "Note how we expressed this relation by the `with_incoming` keyword, because we want a `StructureData` node having an *incoming* link from the `CalcJobNode` referenced by the `calculation` tag (i.e., the `StructureData` must be an *output* of the calculation).\n", - "\n", - "Now all we have to do is execute the query to retrieve our structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we did can be visualized schematically, thanks to a little tool we have written for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query1.png')\n", - "Image(filename='query1.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `with_incoming` keyword is only one of many potential relationships that exist between the various `AiiDA` nodes and that are implemented in the `QueryBuilder`. The table below gives an overview of the implemented relationships, which nodes they are defined for and what relation it implicates. The full documentation can be found [on this page of the AiiDA documentation](https://aiida-core.readthedocs.io/en/latest/querying/querybuilder/append.html#joining-entities)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entity from\t| Entity to\t| Relationship | Explanation\n", - "------------|-----------|------------------|------------\n", - "Node | Node | with_outgoing | One node as input of another node\n", - "Node | Node | with_incoming | One node as output of another node\n", - "Node | Node | with_descendants | One node as the ancestor of another node\n", - "Node | Node | with_ancestors | One node as descendant of another node\n", - "Group | Node | with_node | The group of a node\n", - "Node | Group | with_group | The node is a member of a group\n", - "Computer | Node | with_node | The computer of a node\n", - "Node | Computer | with_computer | The node of a computer\n", - "User | Node | with_node | The creator of a node is a user\n", - "Node | User | with_user | The node was created by a user" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, see if you can write a query that will return all the `UpfData` nodes that are a member of a `Group` whose name starts with the string `SSSP`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'SSSP%'}}, tag='group')\n", - "qb.append(UpfData, with_group='group')\n", - "qb.all()\n", - "#TUT_SOLUTION_END\n", - "# Here I also visualize what's going on:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query2.png')\n", - "Image(filename='query2.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Attributes and extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In section 2, we showed you how you can `project` specific properties of a `Node` and gave a list of properties that a `Node` instance possesses. Since then, we have come across a lot of different `AiiDA` data nodes, such as `StructureData` and `UpfData`, that were secretly `Node`'s in disguise. Or to put it correctly, as `AiiDA` employs the object-oriented programming paradigm, both `StructureData` and `UpfData` are examples of subclasses of the `Node` class and therefore inherit its properties. That means that whatever property a `Node` has, both `StructureData` and `UpfData` will have too. However, there is a semantic difference between a `StructureData` node and a `UpfData`, and so we may want to add a property to one that would not make sense for the other. To solve this, `AiiDA` introduces the concept of `attributes`. These are similar to properties, except that they are specific to the `Node` type that they are attached to. This allows you to add an `attribute` to a certain node, without having to change the implementation of all the others.\n", - "\n", - "For example, the `Dict` nodes that are generated as output of `PwCalculation`'s may have an attribute named `wfc_cutoff`. To project for this particular `attribute`, one can use exactly the same syntax as shown in section 2 for the regular `Node` properties, and one has to only prepend `attributes.` to the attribute name. Demonstration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='pw')\n", - "qb.append(Dict, with_incoming='pw', project=[\"attributes.wfc_cutoff\"])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not every `Dict` node has to have this attribute, in which case the `QueryBuilder` will return the python keyword `None`. Similar to the `attributes`, nodes also can have `extras`, which work in the same way, except that `extras` are mutable, which means that their value can be changed even after a node instance has been stored.\n", - "\n", - "If you are not sure which attributes a given node has, you can use the `.attributes` property to simply retrieve them all. It will return a dictionary with all the attributes the node has.\n", - "\n", - "Note that a node also has a number of additional methods. For instance, you can do `list(node.attributes_keys())` to get only the attribute keys, or `node.get_attribute('wfc_cutoff')` to get the value of a single attribute (these two variants are more efficient if the node has a lot of attributes and you don't need all data). Similarly, for extras, you have `node.extras`, `list(node.extras_keys())`, and `node.get_attribute('SOME_EXTRA_KEY')`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation)\n", - "node, = qb.first()\n", - "node.attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chemical-element symbol of a pseudopotential, that is represented by a `UpfData` node, is stored in the `element` attribute. Using the knowledge that filtering on attributes works exactly as for normal node properties, see if you can write a query that will search your database for pseudopotentials for silicon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_SOLUTION_START\n", - "qb.append(UpfData, filters={'attributes.element': {'==': 'Si'}})\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**For more exercises on relationships and attributes/extras, have a look at the appendix section on queries.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 5. A small high-throughput study" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The following section assumes that a specific dataset is present in your current AiiDA profile. If you are not running this script on the Virtual Machine of the AiiDA tutorial, this script will not produce the desired output.\n", - "You can download the Virtual Machine image from [https://aiida-tutorials.readthedocs.io](https://aiida-tutorials.readthedocs.io) along with the tutorial text (choose the correct version of the tutorial, depending on which version of AiiDA you want to try).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part of the tutorial, we will focus on how to systematically retrieve, parse and analyze the results of\n", - "multiple calculations using AiiDA. We know you’re able to do this yourself, but to save time, a set of calculations\n", - "have already been done with AiiDA for you on 57 perovskites, using three different pseudopotential families (LDA,\n", - "PBE and PBESOL, all from GBRV 1.2).\n", - "\n", - "**Note**: if you are not following the tutorial on the official virtual machine that comes with this data, but you are working with your own database, you first have to import this archive (download it from: https://drive.google.com/open?id=1eqJaJr7q1VsYYvGxqHxmDN7gBMs534rz) using `verdi import`\n", - "\n", - "These calculations are spin-polarized (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. The idea of this part of the tutorial is to “screen” for magnetic and metallic perovskites in a “high-throughput” way. As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. Once more check the list of groups in your database by typing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!verdi group list -A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The calculations needed for this task were put in three different groups whose names start with \"tutorial\" (one for each pseudopotential family). The main task is to make a plot showing, for all perovskites and for each pseudopotential family, the total magnetization and the $-TS$ contribution from the smearing to the total energy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start building the query\n", - "So we first of all need to instantiate a QueryBuilder instance. We `append` the groups of interest, which means that we select only groups that start with the string `tutorial_`. We can execute the query after this append (this will not affect the final results) and check whether we have retrieved 3 groups." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiating QB:\n", - "qb = QueryBuilder()\n", - "# Appending the groups I care about:\n", - "qb.append(Group, filters={'label':{'like':'tutorial_%'}}, project='label', tag='group')\n", - "# Visualize:\n", - "print(\"Groups:\", ', '.join([g for g, in qb.all()]))\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query3.png')\n", - "Image(filename='query3.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the calculations that are members of each group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# I want every PwCalculation that is a member of the specified groups:\n", - "#TUT_USER_START\n", - "qb.append(PwCalculation, tag='calculation', with_group=) # Complete the function call with the correct relationship-tag!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(PwCalculation, tag='calculation', with_group='group')\n", - "#TUT_SOLUTION_END\n", - "#Visualize\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query4.png') \n", - "Image(filename='query4.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the structures that are input of the calculation. \n", - "We extend the query to include the structures that are input of the calculations that match the query so far.\n", - "This means that we `append` StructureData, and defining the relationship with the calculation with corresponding keyword.\n", - "\n", - "For simplicity the formulas have been added in the extras of each structure node under the key `formula`. \n", - "(The function that does this is called `store_formula_in_extra` and can be found in the top cell of this notebook. It also uses the QueryBuilder!)\n", - "\n", - "Project the formula, stored in the extras under the key `formula`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(StructureData, project=, tag='structure', with_outgoing=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(StructureData, project=['extras.formula'], tag='structure', with_outgoing='calculation')\n", - "#TUT_SOLUTION_END\n", - "# Visualize:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query5.png')\n", - "Image(filename='query5.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the output of the calculation\n", - "Every successful PwCalculation outputs a `Dict` node that stores the parsed results as key-value pairs. You can find these pairs among the attributes of the `Dict` node. To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. For convenience, the units are also added as key/value pairs (with the same key name, but with `_units` appended). \n", - "Extend the query so that also the output `Dict` of each calculation is returned. Project only\n", - "the attributes relevant to your analysis.\n", - "\n", - "In particular, project (in this order):\n", - "\n", - "* The smearing contribution \n", - "* The units of the smearing contribution\n", - "* The magnetization \n", - "* The units of the magnetization\n", - "\n", - "(to know the projection keys, you can try to load one `CalcJobNode` from one of the groups, get its output `Dict` and inspect its `attributes` as discussed before, to see the key-value pairs that have been parsed)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(Dict, tag='results', project=['attributes.energy_smearing', ...], with_incoming=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Dict, tag='results',\n", - " project=['attributes.energy_smearing', 'attributes.energy_smearing_units',\n", - " 'attributes.total_magnetization', 'attributes.total_magnetization_units',\n", - " ], with_incoming='calculation'\n", - " )\n", - "#TUT_SOLUTION_END\n", - "\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query6.png') \n", - "Image(filename='query6.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print the query results\n", - "We can print the results to see if everything worked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = qb.all()\n", - "for item in results:\n", - " print(', '.join(map(str, item)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results\n", - "Getting a long list is not always helpful, and a graph can be much more clear and useful. To help you, we have already prepared a function that visualizes the results of the query. Run the following cell and, if you did everything correctly, you should get a graph with the results of your queries!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_results(results)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/__init__.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/common_wf.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/common_wf.py deleted file mode 100644 index 4a75d0ee..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/common_wf.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper functions.""" -from __future__ import absolute_import -import numpy as np -from aiida.plugins import CalculationFactory, DataFactory -from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def generate_scf_input_params(structure, code, pseudo_family): - """Construct a builder for the `PwCalculation` class and populate its inputs. - - :return: `ProcessBuilder` instance for `PwCalculation` with preset inputs - """ - parameters = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, # Important that this stays to get stress - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - - kpoints = KpointsData() - kpoints.set_kpoints_mesh([2, 2, 2]) - - builder = PwCalculation.get_builder() - builder.code = code - builder.structure = structure - builder.kpoints = kpoints - builder.parameters = Dict(dict=parameters) - builder.pseudos = validate_and_prepare_pseudos_inputs( - structure, pseudo_family=pseudo_family) - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calculation of Silicon with Quantum ESPRESSO" - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - - return builder - - -def birch_murnaghan(V, E0, V0, B0, B01): - """Compute energy by Birch Murnaghan formula.""" - r = (V0 / V)**(2. / 3.) - return E0 + 9. / 16. * B0 * V0 * (r - 1.)**2 * (2. + (B01 - 4.) * (r - 1.)) - - -def fit_birch_murnaghan_params(volumes_, energies_): - """Fit Birch Murnaghan parameters.""" - from scipy.optimize import curve_fit - - volumes = np.array(volumes_) - energies = np.array(energies_) - params, covariance = curve_fit( - birch_murnaghan, - xdata=volumes, - ydata=energies, - p0=( - energies.min(), # E0 - volumes.mean(), # V0 - 0.1, # B0 - 3., # B01 - ), - sigma=None) - return params, covariance - - -def plot_eos(eos_pk): - """ - Plots equation of state taking as input the pk of the ProcessCalculation - printed at the beginning of the execution of run_eos_wf - """ - import pylab as pl - from aiida.orm import load_node - eos_calc = load_node(eos_pk) - - data = [] - for V, E, units in eos_calc.outputs.eos.dict.eos: - data.append((V, E)) - - data = np.array(data) - params, _covariance = fit_birch_murnaghan_params(data[:, 0], data[:, 1]) - - vmin = data[:, 0].min() - vmax = data[:, 0].max() - vrange = np.linspace(vmin, vmax, 300) - - pl.plot(data[:, 0], data[:, 1], 'o') - pl.plot(vrange, birch_murnaghan(vrange, *params)) - - pl.xlabel("Volume (ang^3)") - # I take the last value in the list of units assuming units do not change - pl.ylabel("Energy ({})".format(units)) # pylint: disable=undefined-loop-variable - pl.show() diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/create_rescale.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/create_rescale.py deleted file mode 100644 index 508df377..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/create_rescale.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper CalcFunctions for equation of state.""" -from __future__ import absolute_import -from aiida.engine import calcfunction - - -@calcfunction -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - symbol = element.value - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure - - -@calcfunction -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/equation_of_state.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/equation_of_state.py deleted file mode 100644 index 703dc7e2..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/equation_of_state.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -"""Equation of State WorkChain.""" -from __future__ import absolute_import -from six.moves import zip - -from aiida.engine import WorkChain, ToContext, calcfunction -from aiida.orm import Code, Dict, Float, Str, StructureData -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -PwCalculation = CalculationFactory('quantumespresso.pw') -scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) -labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - -@calcfunction -def get_eos_data(**kwargs): - """Store EOS data in Dict node.""" - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -class EquationOfState(WorkChain): - """WorkChain to compute Equation of State using Quantum Espresso.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super(EquationOfState, cls).define(spec) - spec.input('code', valid_type=Code) - spec.input('element', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.output('initial_structure', valid_type=StructureData) - spec.output('eos', valid_type=Dict) - spec.outline( - cls.run_eos, - cls.results, - ) - - def run_eos(self): - """Run calculations for equation of state.""" - # Create basic structure and attach it as an output - initial_structure = create_diamond_fcc(self.inputs.element) - self.out('initial_structure', initial_structure) - - calculations = {} - - for label, factor in zip(labels, scale_facs): - - structure = rescale(initial_structure, Float(factor)) - inputs = generate_scf_input_params(structure, self.inputs.code, - self.inputs.pseudo_family) - - self.report( - 'Running an SCF calculation for {} with scale factor {}'. - format(self.inputs.element, factor)) - future = self.submit(PwCalculation, **inputs) - calculations[label] = future - - # Ask the workflow to continue when the results are ready and store them in the context - return ToContext(**calculations) - - def results(self): - """Process results.""" - inputs = { - label: self.ctx[label].get_outgoing().get_node_by_label( - 'output_parameters') - for label in labels - } - eos = get_eos_data(**inputs) - - # Attach Equation of State results as output node to be able to plot the EOS later - self.out('eos', eos) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/plot_calculation_results.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/plot_calculation_results.py deleted file mode 100644 index 628f8a7f..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/plot_calculation_results.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot results of your calculations.""" -from __future__ import absolute_import -from __future__ import print_function -import sys -from argparse import ArgumentParser -from six.moves import map, range, zip - -import numpy as np -from matplotlib import gridspec, pyplot as plt - - -def plot_results(inputfname, outputfname=None, plotformat=None): # pylint: disable=too-many-locals,too-many-statements,too-many-branches - """ - :param inputfile: the filename of the inputfile - :param (opt) outputfile: - The (optional) name for the outputfile. - :param (opt) format: - The (optional) file format, if not clear from the input file name - - Reads the input file provided by the user. - The format should be comma separated values: - formula, pseudo_family, smearing, smearing_units, magnetization, magnetization_units - - - This order has to be respected! - - If an outputfile is provided, use matplotlib.pyplot.savefig to save the figure - Else, calls matplotlib.pyplot.show to show in screen - """ - - # Reading the input file given to me: - with open(inputfname) as f: - lines = f.readlines() - - # Definining the variables I need to read the units and pseudo families - smearing_unit_set = set() - magnetization_unit_set = set() - pseudo_family_set = set() - - # Storing results: - results_dict = {} - - # Looping through results: - for line in lines: - try: - (formula, pseudo_family, smearing, smearing_units, magnetization, - magnetization_units) = [i.strip() for i in line.split(',')] - smearing, magnetization = list( - map(float, (smearing, magnetization))) - except Exception as e: # pylint: disable=broad-except - print("There was an {} reading line:\n" - "{}" - "Exception: {}\n" - "Skipping this line\n".format(type(e), line, e)) - continue - - # If this is a new formula, not present in results, - # Creating the key-value pair: - if formula not in results_dict: - results_dict[formula] = {} - - # Storing the results: - results_dict[formula][pseudo_family] = (smearing, magnetization) - - # Adding to the unit set: - smearing_unit_set.add(smearing_units) - magnetization_unit_set.add(magnetization_units) - pseudo_family_set.add(pseudo_family) - - # Sorting by formula: - sorted_results = sorted(results_dict.items()) - formula_list = list(zip(*sorted_results))[0] - nr_of_results = len(formula_list) - - # Checks that I have not more than 3 pseudo families. - # If more are needed, define more colors - - if len(pseudo_family_set) > 3: - raise Exception('I was expecting 3 or less pseudo families') - - colors = ['b', 'r', 'g'] - - # Plotting: - gs = gridspec.GridSpec(2, 1, hspace=0.01, left=0.1, right=0.94) - fig = plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None) - - # Defining barwidth - barwidth = 1. / (len(pseudo_family_set) + 1) - offset = [ - -0.5 + (0.5 + n) * barwidth for n in range(len(pseudo_family_set)) - ] - - # Axing labels with units: - yaxis = ("Smearing energy [{}]".format(smearing_unit_set.pop()), - "Total magnetization [{}]".format(magnetization_unit_set.pop())) - # If more than one unit was specified, I will exit: - if smearing_unit_set: - raise Exception('Found different units for smearing') - if magnetization_unit_set: - raise Exception('Found different units for magnetization') - - # Making two plots, one for the smearing, the lower for magnetization - for index in range(2): - ax = fig.add_subplot(gs[index]) - for i, pseudo_family in enumerate(pseudo_family_set): - X = np.arange(nr_of_results) + offset[i] - Y = np.array([ - thisres[1][pseudo_family][index] for thisres in sorted_results - ]) - - ax.bar(X, - Y, - width=0.2, - facecolor=colors[i], - edgecolor=colors[i], - label=pseudo_family) - ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15 * index + 5) - ax.set_xlim(-0.5, nr_of_results - 0.5) - ax.set_xticks(np.arange(nr_of_results)) - - if index: - plt.setp(ax.get_yticklabels()[-1], visible=False) - else: - plt.setp(ax.get_yticklabels()[0], visible=False) - ax.xaxis.tick_top() - ax.legend(loc=3, prop={'size': 18}) - - for i in range(0, nr_of_results, 2): - ax.axvspan(i - 0.5, i + 0.5, facecolor='y', alpha=0.2) - ax.set_xticklabels(list(formula_list), - rotation=90, - size=14, - ha='center') - - if outputfname is not None: - plt.savefig(outputfname, format=plotformat) - else: - plt.show() - - -if __name__ == '__main__': - parser = ArgumentParser() - parser.add_argument('inputfile', - help='The input file with the data to plot') - parser.add_argument('-o', - '--outputfile', - help='The outputfile to plot results to', - default=None) - parser.add_argument( - '-f', - '--format', - default=None, - help= - 'The format to plot, if outputfile is specified and no valid extension is provided' - ) - parsed_args = parser.parse_args(sys.argv[1:]) - plot_results(inputfname=parsed_args.inputfile, - outputfname=parsed_args.outputfile, - plotformat=parsed_args.format) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/plot_convergence_pressure.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/plot_convergence_pressure.py deleted file mode 100644 index 725133d3..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/plot_convergence_pressure.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot parabola using matplotlib.""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import range -from aiida.orm import load_node - - -def parabola(x, a, b, c): - """Parabola.""" - return a * x**2 + b * x + c - - -def show_parabola_results(pk): # pylint: disable=too-many-locals - """Plot (and show) parabola using matplotlib.""" - out_p = load_node(pk).outputs.steps.get_dict() - - step0 = out_p['step0'] - steps = out_p['steps'] - - import numpy as np - import pylab as pl - - data = [] - data.append((step0['V'], step0['E'])) - for step in steps: - data.append((step['V'], step['E'])) - - data = np.array(data) - min_V = data[:, 0].min() - max_V = data[:, 0].max() - - min_E = data[:, 1].min() - max_E = data[:, 1].max() - - min_V -= 10. - max_V += 10. - min_E -= 0.1 - max_E += 0.5 - - V_range = np.linspace(min_V, max_V, 500) - - for subplot in range(2): - pl.subplot(1, 2, 1 + subplot) - - pl.plot(data[:1, 0], data[:1, 1], 'o', color='red') - pl.annotate('0', xy=(data[0, 0], data[0, 1])) - - pl.plot(data[1:, 0], data[1:, 1], 'o', color='blue') - - color = 0.8 - for idx, step in enumerate(steps, start=1): - pl.plot(V_range, - parabola(V_range, step['a'], step['b'], step['c']), - color=(color, color, color)) - parabola_Vmin = -step['b'] / 2. / step['a'] - parabola_Emin = parabola(parabola_Vmin, step['a'], step['b'], - step['c']) - pl.plot([parabola_Vmin], [parabola_Emin], - 'x', - color=(color, color, color)) - pl.annotate(str(idx), xy=(step['V'], step['E'])) - pl.axvline(step['V'], color=(1., 0.7, 1.), linestyle='--') - color -= 0.25 - if color <= 0.: - color = 0. - - pl.axis([min_V, max_V, min_E, max_E]) - if subplot == 1: - # some zoom - pl.xlim((41.5, 44.)) - pl.ylim((-259.47, -259.45)) - pl.xlabel('Volume (ang^3)') - pl.ylabel('Energy (eV)') - - pl.show() - - -if __name__ == '__main__': - import sys - try: - pk_value = int(sys.argv[1]) - except (IndexError, ValueError): - print( - 'Pass as parameter the PK of the WorkChain calculating the pressure', - file=sys.stderr) - print('convergence to generate the plot.', file=sys.stderr) - sys.exit(1) - show_parabola_results(pk_value) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/pressure_convergence.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/pressure_convergence.py deleted file mode 100644 index 89f42195..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/pressure_convergence.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding: utf-8 -*- -"""Pressure convergence WorkChain""" -from __future__ import absolute_import -from __future__ import print_function -from aiida.engine import WorkChain, ToContext, while_, calcfunction, workfunction -from aiida.orm import Code, Float, Str, StructureData -from aiida.plugins import CalculationFactory, DataFactory - -from common_wf import generate_scf_input_params -from create_rescale import rescale - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def get_energy_first_derivative(stress): - """Return the energy first derivative from the stress. - - :param stress: the stress tensor in GPa - :return: first derivative of the energy in eV/Å^3 - """ - from numpy import trace - - GPa_to_eV_over_ang3 = 1. / 160.21766208 - - # Get the pressure (GPa) - pressure = trace(stress) / 3. - - # Pressure is -dE/dV; moreover p in kbar, we need to convert it to eV/Å^3 to be consistent - dE = -pressure * GPa_to_eV_over_ang3 - - return dE - - -def get_volume_energy_and_derivative(output_parameters): - """Return the volume, energy and energy derivative for given `PwCalculation` output parameters. - - Volume is in Å^3, energy in eV and energy derivative eV/Å^3 - - :param output_parameters: the `output_parameters` `Dict` result node of a `PwCalculation` - :return: tuple with volume, energy and energy derivative - """ - V = output_parameters.dict.volume - E = output_parameters.dict.energy - dE = get_energy_first_derivative(output_parameters.dict.stress) - - return V, E, dE - - -def get_energy_second_derivative(output_parameters_one, output_parameters_two): - """Return second derivative of the energy with respect to the volume given the results of two `PwCalculations`. - - The derivate is computed using the finite differences method. - - :param output_parameters_one: the `output_parameters` `Dict` result node of the first `PwCalculation` - :param output_parameters_two: the `output_parameters` `Dict` result node of the second `PwCalculation` - :return: the second derivative of the energy with respect to the volume - """ - dE1 = get_energy_first_derivative(output_parameters_one.dict.stress) - dE2 = get_energy_first_derivative(output_parameters_two.dict.stress) - V1 = output_parameters_one.dict.volume - V2 = output_parameters_two.dict.volume - - return (dE2 - dE1) / (V2 - V1) - - -def get_parabola_coefficients(V, E, dE, ddE): - """Return coefficients of a parabola fit to E = a*V^2 + b*V + c for the given volume and energy. - - :param V: volume in Å^3 - :param E: energy in eV - :param dE: the first derivative of the energy with respect to the volume - :param ddE: the second derivative of the energy with respect to the volume - :return: coefficients a, b and c - """ - a = ddE / 2. - b = dE - ddE * V - c = E - V * dE + V**2 * ddE / 2. - - return a, b, c - - -@workfunction -def get_structure(structure, step_data=None): - """Return a scaled version of the given structure, where the new volume is determined by the given step data.""" - initial_volume = structure.get_cell_volume() - - if step_data is None: - new_volume = initial_volume + 4. # In Å^3 - else: - # Minimum of a parabola - new_volume = -step_data.dict.b / 2. / step_data.dict.a - - scale_factor = (new_volume / initial_volume)**(1. / 3.) - scaled_structure = rescale(structure, Float(scale_factor)) - return scaled_structure - - -@calcfunction -def get_step_data(parameters_first, parameters_second=None): - """Generate a dictionary with the step parameters from the output parameters of a completed `PwCalculation`.""" - - # If the parameters of the second calculation are not passed, this is for the first step and we only return - # a dictionary with the volume, energy and derivative - if parameters_second is None: - V, E, dE = get_volume_energy_and_derivative(parameters_first) - return Dict(dict={'V': V, 'E': E, 'dE': dE}) - - # Otherwise, this is an iteration step and we return the difference between the steps - V, E, dE = get_volume_energy_and_derivative(parameters_first) - ddE = get_energy_second_derivative(parameters_first, parameters_second) - a, b, c = get_parabola_coefficients(V, E, dE, ddE) - - return Dict(dict={ - 'V': V, - 'E': E, - 'dE': dE, - 'ddE': ddE, - 'a': a, - 'b': b, - 'c': c - }) - - -@calcfunction -def bundle_step_data(step0, **kwargs): - """Bundle step data into Dict.""" - steps = [step.get_dict() for step in kwargs.values()] - print((step0, type(step0))) - print((steps, type(steps))) - return Dict(dict={'step0': step0.get_dict(), 'steps': steps}) - - -class PressureConvergence(WorkChain): - """Relax a structure using Newton's algorithm on the first derivative of the energy (minus the pressure).""" - - @classmethod - def define(cls, spec): - """Define spec of WorkChain.""" - # yapf: disable - # pylint: disable=bad-continuation - super(PressureConvergence, cls).define(spec) - spec.input('code', valid_type=Code, - help='Code setup to run `pw.x` to use for the calculations.') - spec.input('structure', valid_type=StructureData, - help='The structure to minimize.') - spec.input('pseudo_family', valid_type=Str, - help='Family of pseudopotentials to use for the calculations.') - spec.input('volume_tolerance', valid_type=Float, - help='Stop if the volume difference of two consecutive calculations is less than this threshold.') - spec.output('steps', valid_type=Dict, - help='The data of all the steps in the minimization process containing info about energy and volume') - spec.output('structure', valid_type=StructureData, - help='Final relaxed structure.') - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - - def setup(self): - """Launch the first calculation for the input structure, and a second calculation for a shifted volume.""" - scaled_structure = get_structure(self.inputs.structure) - self.ctx.last_structure = scaled_structure - - inputs0 = generate_scf_input_params(self.inputs.structure, - self.inputs.code, - self.inputs.pseudo_family) - inputs1 = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - - # Run two `PwCalculations` - future0 = self.submit(PwCalculation, **inputs0) - future1 = self.submit(PwCalculation, **inputs1) - - # Wait for them to complete before going to the next step - return ToContext(r0=future0, r1=future1) - - def put_step0_in_ctx(self): - """Store the outputs of the very first step in a specific dictionary.""" - self.ctx.step0 = get_step_data(self.ctx.r0.outputs.output_parameters) - - # Prepare the list containing the steps: step 1 will be stored here by move_next_step - self.ctx.steps = [] - - def move_next_step(self): - """Main part of the algorithm. - - Compare the results of two consecutive calculations and use Newton's algorithm on the pressure by fitting the - results with a parabola and setting the next volume to calculate to the parabola minimum. - - The oldest calculation gets replaced by the most recent and a new calculation is launched that will replace - the most recent. - """ - # Computer the new Volume using Newton's algorithm and create the new corresponding structure by scaling it - new_step_data = get_step_data(self.ctx.r0.outputs.output_parameters, - self.ctx.r1.outputs.output_parameters) - scaled_structure = get_structure(self.inputs.structure, new_step_data) - self.ctx.steps.append(new_step_data) - - # Replace the older step with the latest and set the current structure - self.ctx.r0 = self.ctx.r1 - self.ctx.last_structure = scaled_structure - - inputs = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - future = self.submit(PwCalculation, **inputs) - - return ToContext(r1=future) - - def not_converged(self): - """Return True if the worflow is not converged yet (i.e. the volume changed significantly).""" - r0_out = self.ctx.r0.outputs.output_parameters - r1_out = self.ctx.r1.outputs.output_parameters - - return abs(r1_out.dict.volume - - r0_out.dict.volume) > self.inputs.volume_tolerance - - def finish(self): - """Attach the result nodes as outputs.""" - steps = { - 'step{}'.format(index + 1): step - for index, step in enumerate(self.ctx.steps) - } - bundled_steps = bundle_step_data(step0=self.ctx.step0, **steps) - - self.out('steps', bundled_steps) - self.out('structure', self.ctx.last_structure) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/scripts/simple_sync_workflow.py b/docs/pages/2019_MARVEL_Psik_MaX/scripts/simple_sync_workflow.py deleted file mode 100644 index acf01b36..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/scripts/simple_sync_workflow.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -"""Simple workflow example""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import zip - -from aiida.engine import run, Process, calcfunction, workfunction -from aiida.orm import load_code, Dict, Float, Str -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -# Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' -PwCalculation = CalculationFactory('quantumespresso.pw') - - -@calcfunction -def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -@workfunction -def run_eos_wf(code, pseudo_family, element): - """Run an equation of state of a bulk crystal structure for the given element.""" - - # This will print the pk of the work function - print('Running run_eos_wf<{}>'.format(Process.current().pid)) - - scale_factors = (0.96, 0.98, 1.0, 1.02, 1.04) - labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - calculations = {} - - # Create an initial bulk crystal structure for the given element, using the calculation function defined earlier - initial_structure = create_diamond_fcc(element) - - # Loop over the label and scale_factor pairs - for label, factor in list(zip(labels, scale_factors)): - - # Generated the scaled structure from the initial structure - structure = rescale(initial_structure, Float(factor)) - - # Generate the inputs for the `PwCalculation` - inputs = generate_scf_input_params(structure, code, pseudo_family) - - # Launch a `PwCalculation` for each scaled structure - print('Running a scf for {} with scale factor {}'.format( - element, factor)) - calculations[label] = run(PwCalculation, **inputs) - - # Bundle the individual results from each `PwCalculation` in a single dictionary node. - # Note: since we are 'creating' new data from existing data, we *have* to go through a `calcfunction`, otherwise - # the provenance would be lost! - inputs = { - label: result['output_parameters'] - for label, result in calculations.items() - } - eos = create_eos_dictionary(**inputs) - - # Finally, return the results of this work function - result = {'initial_structure': initial_structure, 'eos': eos} - - return result - - -def run_eos(code=load_code('qe-6.3-pw@localhost'), - pseudo_family='SSSP', - element='Si'): - """Helper function to run EOS WorkChain.""" - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - -if __name__ == '__main__': - run_eos() diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_computer_code_setup.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_computer_code_setup.rst deleted file mode 100644 index 943504b5..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_computer_code_setup.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _appendix_computer_code_setup: - -Setting up computers and codes -============================== - -To setup a computer, please refer to the `online documentation `_. -Instructions to setup a code for a computer can be `found here `_. diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_input_validation.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_input_validation.rst deleted file mode 100644 index f41e6e0c..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_input_validation.rst +++ /dev/null @@ -1,94 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Calculation input validation -============================ - -This appendix shows additional ways to debug errors with Quantum ESPRESSO, namely -how to take advantage of a powerful tool to validate the input to Quantum ESPRESSO -and possibly suggest the correct name to misspelled keywords. - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. -Let's check for example this input dictionary, where we inserted two mistakes: - -.. code:: python - - parameters_dict = { - 'CTRL': { - 'calculation': 'scf', - 'restart_mode': 'from_scratch', - }, - 'SYSTEM': { - 'nat': 2, - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - -The two mistakes in the dictionary are the following: - -- We wrote a wrong namelist name (``CTRL`` instead of ``CONTROL``). -- We inserted the number of atoms explicitly: while that is indeed how the - number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system, - since this information is already contained within the StructureData. - -After we correct the dictionary ``parameters_dict``, we could run the calculation directly. -Alternatively, we can use a tool of the the `aiida-quantumespresso` plugin, -the 'input helper', which checks the inputs before submitting the calculation. - -In your script, after you define ``parameters_dict``, -you can validate it with the following command (note that you need to pass also an input crystal structure, -``structure``, to allow the validator to perform all the needed checks): - -.. code:: python - - PwCalculation = CalculationFactory('quantumespresso.pw') - validated_dict = PwCalculation.input_helper(parameters_dict, structure=structure) - parameters = Dict(dict=validated_dict) - - -The ``input_helper`` method will check for the correctness of the input parameters. -If misspelling or incorrect keys are detected, the method raises an exception, -which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, -without providing the namelists (useful if you don't remember where to put some variables in the input file). -Hence, you can provide to ``input_helper`` a dictionary like this one: - -.. code:: python - - parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, - } - - validated_dict = PwCalculation.input_helper( - parameters_dict, - structure=structure, - flat_mode=True - ) - -If you print ``validated_dict``, it will look like the following: - -.. code:: python - - { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_queries.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_queries.rst deleted file mode 100644 index ead97f3b..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_queries.rst +++ /dev/null @@ -1,89 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Queries in AiiDA - Examples and Exercises -========================================= - -Optional exercises on relationships (Task 3) --------------------------------------------- - -**Hint for the exercises:** - -- You can have projections on properties of more than one entity in your query. - You just have to add the ``project`` key (specifying the list of properties that you want to project) along with the corresponding entity when you append it. - -**Exercises:**   Try to write the following queries: - -- Find all descendants of a ``StructureData`` with a certain ``uuid`` (descendants indicate all nodes that can be reached by following outgoing links starting from a given node, with any number of hops). - For every match, print both the ``StructureData`` node and the descendant. - -- Find all the ``FolderData`` nodes created by a specific user. - -Optional exercises on attributes and extras (Task 4) ----------------------------------------------------- - -**Hint for the exercises:** - -- You can easily order or limit the number of the results by using the ``order_by()`` and ``limit()`` methods of the QueryBuilder. - For example, we can order all our ``CalcJobNode``s by their ``id`` and limit the result to the first 10 as follows: - - .. code:: python - - qb = QueryBuilder() - qb.append(CalcJobNode, tag='calc') - qb.order_by({'calc': 'id'}) - qb.limit(10) - qb.all() - -**Exercises:** - -- Write a code snippet to check how many pseudopotentials you have for each element. - -- Smearing contribution to the total energy for calculations: - - 1. Write a query that returns the smearing contribution to the energy stored in some instances of ``Dict`` output by a ``CalcJobNode`` generated by running a ``PwCalculation`` process. - 2. Extend the previous query to also get the input structures of the ``CalcJobNode``. - 3. Which structures have a smearing contribution to the energy smaller or equal to ``-0.02``? - -Summarizing what we learned by now - An example ------------------------------------------------ - -At this point you should be able to do queries with projections, joins and filters. -Moreover, you saw how to apply filters and projections even on attributes and extras. -Let's discover the full power of the ``QueryBuilder`` with a complex graph query that allows you to project various properties from different nodes and apply different filters and joins. - -Imagine that you would like to get the smearing energy for all the calculations that have a smearing energy below a given value and have a Sn\ :sub:`2`\ O\ :sub:`3` as input. -Moreover, besides the smearing energy, you would like to print the units of this energy and the formula of the structure that was given to the calculation. -The graphical representation of this query can be seen in :numref:`fig_qb_example_2_2019` and the actual query follows: - -.. _fig_qb_example_2_2019: -.. figure:: include/images/qb_example_2.png - :width: 50% - :align: center - - Complex graph query. - -.. code:: python - - qb = QueryBuilder() - qb.append( - StructureData, - project=['extras.formula'], - filters={'extras.formula': 'BaO3Ti'}, - tag='structure' - ) - qb.append( - CalcJobNode, - tag='calculation', - with_incoming='structure' - ) - qb.append( - Dict, - tag='results', - filters={'attributes.energy_smearing':{'<=':-1e-8}}, - project=[ - 'attributes.energy_smearing', - 'attributes.energy_smearing_units', - ], - with_incoming='calculation' - ) - qb.all() diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_restarting_calculations.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_restarting_calculations.rst deleted file mode 100644 index bb06ea24..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_restarting_calculations.rst +++ /dev/null @@ -1,90 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -Restarting calculations -======================= - -Up till now, we have presented several cases where, for example, the wrong input parameters were passed to a calculation, causing it to fail. -Often we had to retype a lot of code, to setup a new calculation with the correct inputs. -In addition, there are many real-life scenarios where one would need to restart calculations from completed calculations with largely the same inputs. -For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to relaunch a calculation from one that has already completed, while having the chance to add or change inputs before launching it. -As an example, let us first submit a total energy calculation using a parameters dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, - } - -In this case, we set a very low number of self consistent iterations (3), which is too small to be able to reach the desired accuracy of 10\ :sup:`-14`. -This means the calculation will not converge and will not be successful, despite there not being any actual mistake in the parameters dictionary. - -To easily restart from the previous calculation, instead of retyping all the inputs, you can simply use the ``get_builder_restart`` method of the ``CalcJobNode``. -Just like the ``get_builder`` method of the ``Process`` class, this will create an instance of the ``ProcessBuilder``, except this one will have all the inputs pre-populated based on the node. -To do so, simply load the node of a terminated calculation job in a ``verdi shell``, that you want to restart from, e.g.: - -.. code:: python - - failed_calculation = load_node('') - restart_builder = failed_calculation.get_builder_restart() - -If you simply type ``restart_builder``, you can see that all the inputs have already been set to those that were used for the original calculation. -The only thing that now remains to be done, is to replace those inputs that caused the failure in the first place. - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['ELECTRONS']['electron_maxstep'] = 80 - restart_builder.parameters = Dict(dict=parameters) - -Simply giving the calculation some more steps in the convergence cycle will probably already fix the problem. - -.. note:: - - We have to create a new ``Dict`` node as we are changing its contents, and the original input is immutable as it would break the provenance. - -However, in this Quantum ESPRESSO example even more can be done, by using the results of the previous calculation to speed up the restart. -To do so, we simply have to set the input ``parent_folder`` to the remote working directory of the old calculation, i.e.: - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['CONTROL']['restart_mode'] = 'restart' - restart_builder.parameters = Dict(dict=parameters) - restart_builder.parent_folder = failed_calculation.outputs.remote_folder - -Note that this particular step is specific to a ``PwCalculation``, but the restart builder concept works for any calculation class. -Any of the inputs can be changed or set. -For example, you may also want to record that this calculation is a restart, in addition to the provenance graph, by setting the label or description: - -.. code:: python - - restart_builder.metadata.label = 'Restart from PwCalculation<{}>'.format(failed_calculation.pk) - -Ultimately whatever needs to be changed for the restart is up to you, but the restart builder makes it a lot easier. -Finally, to submit the restart, since it is a process builder, it works exactly as any other builder: - -.. code:: python - - from aiida.engine import launch - results, node = launch.run.get_node(restart_builder) - -You can now inspect the restarted calculation to verify that this time it actually completed successfully. -Using the restart builder, the required code to setup a calculation is much shorter than the one needed to launch a new one from scratch. -There is no need to load or create many of the inputs such as the pseudopotentials, structures and k-points, -because they were reused from the first calculation. diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_workflow_logic.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_workflow_logic.rst deleted file mode 100644 index 4c2fe783..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/appendix_workflow_logic.rst +++ /dev/null @@ -1,118 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -.. _workflow_logic: - -More workflow logic: while loops and conditional statements -=========================================================== - -In the previous sections, you have been introduced to ``WorkChains``, and the reason for using them over 'standard' work functions. -However, in the :ref:`original example`, the ``spec.outline`` was quite simple, with a 'static' sequence of two steps. -Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). -To support this scenario, the ``spec.outline`` can support logic: `while` loops and `if/elif/else` blocks. -The simplest way to explain it is to show an example: - -.. code:: python - - from aiida.engine import if_, while_ - - spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), - ) - -that would *roughly* correspond, in a python syntax, to: - -.. code:: python - - s1() - if isA(): - s2() - elif isB(): - s3() - else: - s4() - s5() - while condition(): - s6() - -The only constraint is that condition functions (in the example above ``isA``, ``isB`` and ``condition``) must be class methods that returns ``True`` or ``False`` depending on whether the condition is met or not. - -A suggestion on how to write new workchains: use the outline to help you in designing the logic. -First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. -Then, define one by one the methods. - -.. _convpressure: - -Pressure convergence --------------------- - -For example, we have prepared a simple workflow (using work chains, work functions and calculation functions) to optimize the lattice parameter of silicon efficiently using Newton's algorithm on the energy derivative, i.e. the pressure :math:`p=-dE/dV`. -You can download this code :download:`here <../scripts/pressure_convergence.py>` -The outline looks like this: - -.. code:: python - - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - -This outline already roughly explains the algorithm: after an initialization (``setup``) and putting the first step (number zero) in the ctx (``put_step0_in_ctx``), a function to move to the next step is called (``move_next_step``). -This is iterated while a given convergence criterion is not met (``not_converged``). -Finally, some reporting is done, including returning some output nodes (``finish``). - -If you are interested in the details of the algorithm, you can inspect the file. -The main ideas are described here: - -``setup`` -Generate a ``pw.x`` calculation for the input structure (with volume -(V)), and one for a structure where the volume is :math:`V+4 \mbox{\normalfont\AA}^3` (just to get a closeby volume). -Store the results in the context as ``r0`` and ``r1`` - -``put_step0_in_ctx`` -Store in the context :math:`V`, :math:`E(V)` and :math:`dE/dV` for the first calculation ``r0`` - -``move_next_step`` -This is the most important function. Calculate :math:`V`, :math:`E(V)` and :math:`dE/dV` for ``r1``. -Also, estimate :math:`d^2E/dV^2` from the finite difference of the first derivative of ``r0`` and ``r1`` (helper functions to achieve this are provided). -Get the :math:`a`, :math:`b` and :math:`c` coefficients of a parabolic fit :math:`E = aV^2 + bV + c` and estimate the expected minimum of the EOS function as the minimum of the fit :math:`V_0 = -b / 2a`. -Finally, replace ``r0`` with ``r1`` in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume :math:`V_0`, that will be stored in the context replacing ``r1``. -In this way, at the next iteration ``r0`` and ``r1`` will contain the latest two simulations. -Finally, at each step some relevant information (coefficients :math:`a`, :math:`b` and :math:`c`, volumes, energies, energy derivatives, ...) are stored in a list called ``steps``. -This whole list is stored in the context because it provides quantities to be preserved between different work chain steps. - -``not_converged`` -Return ``True`` if convergence has not been achieved yet. -Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold ``volume_tolerance``. - -``finish`` -This is the final step. -Mainly, we return the output nodes: ``steps`` with the list of results at each step, and ``structure`` with the final converged structure. - -The results returned in ``steps`` can be used to represent the evolution of the minimisation algorithm. -A possible way to visualize it is presented in :numref:`fig_convpressure` obtained with an initial lattice constant of `alat = 5.2`. - -.. _fig_convpressure: -.. figure:: include/images/convergence_pressure.png - - Example of results of the convergence algorithm presented in this section. - The bottom plot is a zoom near the minimum. - The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. - The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step. diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/bands.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/bands.rst deleted file mode 100644 index 9422b19d..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/bands.rst +++ /dev/null @@ -1,52 +0,0 @@ -.. _bands: - -A real-world ``WorkChain``: Computing a band structure -====================================================== - -.. note:: *If you still have enough time, you might want to first check* - :numref:`Appendix %s ` *before continuing with this section.* - -As a final demonstration of the power of ``WorkChain``\ s in AiiDA, we want to -give a demonstration of a ``WorkChain``, which will take a structure as its -only input and compute its band structure. All of the steps that would -normally have to be done manually by the researcher -- choosing appropriate -pseudopotentials, energy cutoffs, k-point meshes, high-symmetry k-point paths, -and performing the various calculation steps -- are performed automatically by -the ``WorkChain``. - -The demonstration of the WorkChain will be performed in a Jupyter notebook, -that you can :download:`download from here <../notebooks/bandstructure.ipynb>`. -There you will find some example structures that are loaded from the -Crystallography Open Database (COD), using the COD-importer integrated in -AiiDA. - -Note that the required time to calculate the bandstructure for the given -structures ranges from ~5 minutes to more than an hour, given that the virtual -machine is running on two cores with CPU throttling. It is not necessary to -run all these examples as they may take too long to complete. For reference, -the expected output band structures are plotted in :numref:`fig_calc_bands_1` -to :numref:`fig_calc_bands_4`. - -.. _fig_calc_bands_1: -.. figure:: include/images/bandstructures/Al_bands.png - :width: 100% - - Electronic band structures of Al computed with AiiDA’s PwBandsWorkChain - -.. _fig_calc_bands_2: -.. figure:: include/images/bandstructures/GaAs_bands.png - :width: 100% - - Electronic band structures of GaAs computed with AiiDA’s PwBandsWorkChain - -.. _fig_calc_bands_3: -.. figure:: include/images/bandstructures/CaF2_bands.png - :width: 100% - - Electronic band structures of CaF\ :sub:`2` computed with AiiDA’s PwBandsWorkChain - -.. _fig_calc_bands_4: -.. figure:: include/images/bandstructures/hBN_bands.png - :width: 100% - - Electronic band structures of BN computed with AiiDA’s PwBandsWorkChain diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/calculations.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/calculations.rst deleted file mode 100644 index 4d9148da..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/calculations.rst +++ /dev/null @@ -1,625 +0,0 @@ -.. _calculations: - -Submit, monitor and debug calculations -====================================== - -In this section we'll be learning how to create new data in AiiDA. - -We will use the `Quantum Espresso `_ package to launch a simple `density functional theory `_ calculation of a silicon crystal using the :doi:`PBE exchange-correlation functional <10.1103/PhysRevB.54.16533>` and check its results. -While we're going to debug these issues 'manually' here, workflows (which you'll encounter later in this tutorial) can help you avoid these issues systematically. - -Note that besides the ``aiida-quantumespresso`` plugin, AiiDA comes with plugins for a range of other codes, all of which are listed in the `AiiDA plugin registry `_. - -Computer setup --------------- - -In a production environment, AiiDA would typically be running on your work station or laptop, while launching calculations -on remote high-performance compute resources that you have SSH access to. -For this reason AiiDA has the concept of a ``Computer`` to run calculations on. - -To keep things simple, Quantum ESPRESSO (together with several other *ab initio* codes) has been installed directly in the -``codes`` subfolder of your virtual machine, -and you are going to launch your first calculations on the same computer where AiiDA is installed. -Nevertheless, we're now going to set up this computer for launching calculations: - -.. code:: bash - - verdi computer setup --config computer.yml - -where ``computer.yml`` is a configuration file in the `YAML format `_) that you can :download:`download here `. This is its content: - -.. literalinclude:: include/configuration/computer.yml - -.. note:: - When used without the ``--config`` option, ``verdi computer setup`` will prompt you for the required information, just like you have seen when :ref:`setting up a profile`. - The configuration file should work for the virtual machine that comes with this tutorial - but may need to be adapted when you are running AiiDA in a different environment, - as explained in :ref:`this appendix`. - -Finally, you need to provide AiiDA with information on how to access the ``Computer``. -For remote computers with ``ssh`` transport, this would involve e.g. an SSH key. -For ``local`` computers, this is just a "formality" (press enter to confirm the default cooldown time): - -.. code:: bash - - verdi computer configure local localhost - -.. note:: - - For remote computers with ``ssh`` transport, use ``verdi computer configure ssh`` instead of ``verdi computer configure local``. - -Your ``localhost`` computer should now show up in - -.. code:: bash - - verdi computer list - -Before proceeding, test that it works: - -.. code:: bash - - verdi computer test localhost - - -Code setup ----------- - -Now that we have our localhost set up, let's configure the ``Code``, namely the ``pw.x`` executable. -As with the computer, we have prepared a configuration file for you to :download:`download `. -This is its content: - -.. literalinclude:: include/configuration/code.yml - :language: yaml - -Once you have the configuration file in your local working environment, set up the code: - -.. code:: bash - - verdi code setup --config code.yml - -.. warning:: - - The configuration should work for the virtual machine that comes with this tutorial. - If you are following this tutorial in a different environment, you will need to install Quantum ESPRESSO and adapt the configuration to your needs, - as explained in :ref:`this appendix`. - -Similar to the computers, you can list all the configured codes with: - -.. code:: bash - - verdi code list - -Verify that it now contains a code named ``qe-6.3-pw`` that we just configured. -You can always check the configuration details of an existing code using: - -.. code:: bash - - verdi code show qe-6.3-pw - -.. note:: - - The ``generic`` profile has already a number of other codes configured. - See ``verdi -p generic code list``. - - -The AiiDA daemon ----------------- - -First of all, check that the AiiDA daemon is actually running. -The AiiDA daemon is a program that - - * runs continuously in the background - * waits for new calculations to be submitted - * transfers the inputs of new calculations to your compute resource - * checks the status of your calculation at the compute resource, and - * retrieves the results from the compute resource - -Check the status of the daemon process by typing in the terminal: - -.. code:: bash - - verdi daemon status - -If the daemon is running, the output should look like - -.. code:: bash - - Profile: default - Daemon is running as PID 2050 since 2019-04-30 12:37:12 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 2055 2.147 0 2019-04-30 12:37:12 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers - -If this is not the case, type in the terminal - -.. code:: bash - - verdi daemon start - -to start the daemon. - -Creating a new calculation --------------------------- - -In the following, we'll be working in the ``verdi shell``. -As you go along, feel free to keep track of your commands by -copying them into a python script ``test_pw.py``. - -.. note:: - - The ``verdi shell`` imports a number of AiiDA internals so that you as the user don't have to. - You can also make those available to a python script, by running it using - - .. code:: bash - - verdi run - - -Every calculation sent to a cluster is linked to a *code*, which describes -the executable file to be used as well as some metadata. -Let's have a look at the codes already installed on your machine: - -.. code:: bash - - verdi code list - -There should be a number of them. Here, we're interested in the "PWscf" executable of Quantum Espresso, i.e. in codes for the ``quantumespresso.pw`` plugin: - -.. code:: bash - - verdi code list -P quantumespresso.pw - -Pick the correct codename, that might look like, e.g. -``qe-6.3-pw@localhost`` and load it in the verdi shell. - -.. code:: python - - code = load_code("") - -.. note:: - - ``load_code`` returns an object of type ``Code``, which is the general AiiDA class for describing simulation codes. - -Let's build the inputs for a new ``PwCalculation`` (defined by the ``quantumespresso.pw`` plugin, the default plugin for the code you chose before) - -.. code:: python - - builder = code.get_builder() - -As the first step, assign a (short) label or a (long) description to your -calculation, that you might find convenient in the future. - -.. code:: python - - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calc with Quantum ESPRESSO on Si" - -This information will be saved in the database for later queries or -inspection. Note that you can press TAB after writing ``builder.`` to -see all inputs available for this calculation. -In order to figure out which data type is expected for a particular input, such as ``builder.structure``, -and whether the input is optional or required, use ``builder.structure??``. - -Now, specify the number of machines (a.k.a. cluster nodes) -you are going to run on and the maximum time allowed for the -calculation. -The general options grouped under ``builder.metadata.options`` are independent of -the code or plugin, and will be passed to the scheduler that handles the -queue on your compute resource . - -.. code:: python - - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - -Again, to see the list of available options, type -``builder.metadata.options.`` and hit the TAB button. - -Preparation of inputs -~~~~~~~~~~~~~~~~~~~~~ - -A Quantum Espresso calculation needs a number of inputs: - -1. `Pseudopotentials `_ -2. a structure -3. a mesh in reciprocal space (k-points) -4. a number of input parameters - -These are mirrored in the inputs of the ``aiida-quantumespresso`` plugin -(see `documentation `_). -We'll start with the structure, k-points, and pseudopotentials -and leave the input parameters as the last thing to setup. - -.. admonition:: Exercise - - Use what you learned in the previous section to load the ``structure`` and ``kpoints`` inputs for your calculation: - - * Use a silicon crystal structure - * Define a ``2x2x2`` mesh of k-points. - - Note: If you just copy and paste code that you executed previously, - this may result in duplication of information on your database. - In fact, you can re-use an existing structure stored in your database [#f1]_. Use a combination of the bash command - ``verdi data structure list`` and of the shell command ``load_node()`` - to get an object representing the structure created earlier. - -Attaching the input information to the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once you've created a ``structure`` node and a ``kpoints`` node, -attach it to the calculation: - -.. code:: python - - builder.structure = structure - builder.kpoints = kpoints - -.. note:: - - The builder accepts both *stored* and *unstored* data nodes. - AiiDA will take care of storing the unstored nodes upon submission. - If you decide not to submit, nothing will be stored in the database. - -PWscf also needs information on the pseudopotentials, -in the form of a dictionary, where keys are the names of the elements and the values are the corresponding ``UpfData`` objects containing the information on the pseudopotential. -However, instead of creating the dictionary by hand, we can use a helper function that picks the right pseudopotentials for our structure from a pseudopotential *family*. -You can list the preconfigured families from the command line: - -.. code:: bash - - verdi data upf listfamilies - -Pick the one you configured earlier (or one of the ``SSSP`` families that -we provide) and link it to the calculation using the command: - -.. code:: python - - from aiida.orm.nodes.data.upf import get_pseudos_from_structure - builder.pseudos = get_pseudos_from_structure(structure, '') - -Print the content of the `pseudos` namespace (`print(builder.pseudos)`) to see what the helper function created. - -Preparing and debugging input parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Finally, we need to specify a number of input parameters (i.e. plane wave cutoffs, convergence thresholds, etc.). -to launch the Quantum ESPRESSO calculation. -The structure of the parameter dictionary closely follows the structure of the `PWscf input file `_. - -Since these are often the parameters to tune in a calculation, -let's **introduce a few mistakes intentionally** -and use this part of the tutorial to learn how to debug problems. - -Define a set of input parameters for Quantum ESPRESSO, preparing a -dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, - } - -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, -except for an invalid key ``mickeymouse``. When Quantum ESPRESSO -receives an unrecognized key, it will stop. -By default, the AiiDA plugin will *not* validate your input and simply pass -it on to the code. - -Let's wrap the ``parameters_dict`` python dictionary in an AiiDA ``Dict`` node and see what happens. - -.. code:: python - - builder.parameters = Dict(dict=parameters_dict) - -Simulate submission -~~~~~~~~~~~~~~~~~~~ - -At this stage, you have created in memory (it's not yet stored in the -database) the input of the graph shown below. The outputs will -be created by the daemon later on. - -.. figure:: include/images/verdi_graph/si/graph-full.png - :alt: - -In order to check which input files AiiDA creates, -we can perform a *dry run* of the submission process. -Let's tell the builder that we want a dry run and that -we don't want to store the provenance of the dry run: - -.. code:: python - - builder.metadata.dry_run = True - builder.metadata.store_provenance = False - -It's time to run: - -.. code:: python - - from aiida.engine import run - run(builder) - -.. note:: - - Instead of using the builder, you can also simply pass the calculation class - as the first argument, followed by the inputs as keyword arguments, e.g.: - - .. code:: python - - run(PwCalculation, structure=structure, pseudos={'Si': pseudo_node}, ....) - - The builder is simply a convenience wrapper providing tab-completion in the shell and automatic help strings. - -This creates a folder of the form ``submit_test/[date]-0000[x]`` in the -current directory. In your second terminal: - - * open the input file ``aiida.in`` within this folder - * compare it to input data nodes you created earlier - * verify that the `pseudo` folder contains the needed pseudopotentials - * have a look at the submission script ``_aiidasubmit.sh`` - -.. note:: - - The files created by a dry run are only intended for inspection - and cannot be used to correct the inputs of your calculation. - -Submitting the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Up to now we've just been playing around and our calculation has been kept -in memory and not in the database. -Now that we have inspected the input files and convinced ourselves that -Quantum ESPRESSO will have all the information it needs to perform the -calculation, we will submit the calculation properly. -Doing so will make sure that all inputs are stored in the database, -will run and store the calculation and link the outputs to it. - -Let's revert the following values in our builder to their defaults: - -.. code:: python - - builder.metadata.dry_run = False - builder.metadata.store_provenance = True - -And then rely on the submit machinery of AiiDA, - -.. code:: python - - from aiida.engine import submit - calculation = submit(builder) - -As soon as you have executed these lines, the ``calculation`` variable contains a ``PwCalculation`` instance, already submitted to the daemon. - -.. note:: - - You may have noticed that we used ``submit`` here instead of ``run``. - The difference is that ``submit`` will hands over the calculation to the daemon running in the background, - while ``run`` will execute all tasks in the current shell. - - All processes in AiiDA (you will soon get to know more) can be "launched" using one of available functions: - - * run - * run_get_node - * run_get_pk - * submit - - which are explained in more detail in the `online documentation `_. - - -The calculation is now stored in the database and was assigned a "database primary key" or ``pk`` (``calculation.pk``) as well as a UUID (``calculation.uuid``). -See the :ref:`previous section <2019-aiida-identifiers>` for more details on these identifiers. - -Note that while AiiDA will prevent you from changing the content of stored nodes, -the concept of "extras" allows you to set extra attributes, e.g. as a way of -labelling nodes and providing information for querying. - -For example, let's add an extra attribute called ``element``, with value ``Si``: - -.. code:: python - - calculation.set_extra("element", "Si") - -In the mean time, after you submitted your calculation, the daemon -picked it up and started to: generate the input files, submit the calculation -to the queue, wait for it to run and finish, retrieve the output files, -parse them, store them in the database and set the state of the -calculation to ``Finished``. - -.. note:: - - If the daemon is not running, the calculation will remain in the - ``NEW`` state until you start the daemon. - -Checking the status of the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can check the calculation status from the command line: - -.. code:: bash - - verdi process list - -.. note:: - - Since you are running your DFT calculation directly on the VM, - ``verdi`` commands can be a bit slow until the calculation finishes. - -If you don't see any calculation in the -output, the calculation you submitted has already finished. - -By default, the command only prints calculations that are still active [#f2]_. -Let's also list your finished calculations (and limit those only -to the one created in the past day): - -.. code:: bash - - verdi process list -a -p1 - -as explained in the first section. - -Similar to the dry run, we can also inspect the input files of the *actual* -calculation: - -.. code:: bash - - verdi calcjob inputls -c - -for the ``pk_number`` of your calculation. This will show the -contents of the input directory (``-c`` prints directories in color). -Check the content of input files with - -.. code:: bash - - verdi calcjob inputcat | less - -Troubleshooting ---------------- - -Your calculation should end up in a FAILED state -(last column of ``verdi process list -a -p1``), and correspondingly the -error code near the "Finished" status of the State should be non-zero, - -.. code:: bash - - $ verdi process list -a -p1 - PK Created State Process label Process status - ---- --------- ---------------- --------------- ---------------- - 98 16h ago Finished [115] PwCalculation - ... - $ # Anything but [0] after the Finished state signals a failure - -This was expected, since we used an invalid key in the input parameters. -Situations like this happen in real life, so AiiDA provides -tools to trace back to the source of the problem and correct it. - -A first way to proceed is to inspect the output file of -PWscf. - -.. code:: bash - - verdi calcjob outputcat | less - -This might be enough to understand the reason why the calculation -failed. - -AiiDA provides further tools for troubleshooting in a more compact way. -For any calculation, both successful and failed, you can get a summary by: - -.. code:: bash - - $ verdi process show - Property Value - ------------- --------------------------------------------------- - type CalcJobNode - pk 98 - uuid 4c444afd-f6e2-4896-b9ae-8cb8a5ec75c5 - label PW test - description My first AiiDA calc with Quantum ESPRESSO on Si - ctime 2019-05-01 15:59:39.180018+00:00 - mtime 2019-05-01 16:01:44.870902+00:00 - process state Finished - exit status 115 - computer [1] localhost - - Inputs PK Type - ---------- ---- ------------- - pseudos - Si 50 UpfData - code 2 Code - kpoints 10 KpointsData - parameters 96 Dict - settings 97 Dict - structure 9 StructureData - - Outputs PK Type - ------------- ---- ---------- - remote_folder 99 RemoteData - retrieved 100 FolderData - - Log messages - --------------------------------------------- - There are 2 log messages for this calculation - Run 'verdi process report 98' to see them - -The last part of the output alerts you to the fact that there -are some log messages waiting for you, if you run -``verdi process report ``. - -Let's now correct our input parameters dictionary by leaving out the invalid -key and see if our calculation succeeds: - -.. code:: python - - parameters_dict = { - "CONTROL": { - "calculation": "scf", - }, - "SYSTEM": { - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } - } - builder.parameters = Dict(dict=parameters_dict) - calculation = submit(builder) - -If you have been using the separate script approach, modify -the script to remove the faulty input and run it again with: - -.. code:: bash - - verdi run test_pw.py - -Use ``verdi process list -a -p1`` to verify that -the calculation reaches the finished status, with exit code zero. - -Using the calculation results ------------------------------ - -Now you can access the results as you have seen earlier. For example, -note down the pk of the calculation so that you can load it in the -``verdi shell`` and check the total energy with the commands: - -.. code:: python - - calculation = load_node() - calculation.res.energy - -Besides writing input files, running the software for you, storing the output -files, and connecting it all together in your provenance graph, -many AiiDA plugins will parse the output of your code and make output values -of interest available through an output dictionary node (as depicted in the -graph above). -In the case of the ``aiida-quantumespresso`` plugin this output node -is available at ``calculation.outputs.output_parameters`` and you can access -all the available attributes (not only the energy) using: - -.. code:: python - - calculation.outputs.output_parameters.attributes - -While the name of this output dictionary node can be chosen by the plugin, -AiiDA provides the "results" shortcut ``calculation.res`` -that plugin developers can use to provide what they consider the result of the -calculation. - - -.. rubric:: Footnotes - -.. [#f1] In order to avoid duplication of KpointsData, you would first need to learn how to query the database, therefore we will ignore this issue for now. -.. [#f2] A process is considered active if it is either ``Created``, ``Running`` or ``Waiting``. If a process is no longer active, but terminated, it will have a state ``Finished``, ``Killed`` or ``Excepted``. diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/configuration/code.yml b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/configuration/code.yml deleted file mode 100644 index bd0a52c4..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/configuration/code.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.3-pw" -description: "quantum_espresso v6.3" -input_plugin: "quantumespresso.pw" -on_computer: true -remote_abs_path: "/home/max/codes/q-e-90a2aa88298f5005b8ab8f5fcc354c5870481d68/bin/pw.x" -computer: "localhost" -prepend_text: "ulimit -s unlimited" -append_text: " " diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/configuration/computer.yml b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/configuration/computer.yml deleted file mode 100644 index 01938930..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/configuration/computer.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -description: "localhost" -label: "localhost" -hostname: "localhost" -transport: local -scheduler: "direct" -work_dir: "/home/max/.aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/Al_bands.png b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/Al_bands.png deleted file mode 100644 index 83a18e02..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/Al_bands.png and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/CaF2_bands.png b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/CaF2_bands.png deleted file mode 100644 index 91ec05ea..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/CaF2_bands.png and 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mode 100644 index fa89c991..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/process_functions.svg +++ /dev/null @@ -1,1996 +0,0 @@ - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - - - Float(0.98) - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - - CALL CALL - - - - RETURN - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - Float(0.98) - - - - - - - - - - - W1 - - - - - - CREATE RESCALED - - - - a) b) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/qb_example_2.png b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/qb_example_2.png deleted file mode 100644 index 6a0c9117..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/qb_example_2.png and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/batio3/graph-full.png b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/batio3/graph-full.png deleted file mode 100644 index f2c49b0e..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/batio3/graph-full.png and /dev/null differ diff --git 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a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/graph.svg +++ /dev/null @@ -1,2678 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/si/graph-full.png b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/si/graph-full.png deleted file mode 100644 index 5cfe8472..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/si/graph-full.png and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/si/graph-input.png b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/si/graph-input.png deleted file mode 100644 index 306d45d9..00000000 Binary files a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/images/verdi_graph/si/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/__init__.py b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_create_diamond_fcc.py b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_create_diamond_fcc.py deleted file mode 100644 index 0ad3d483..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_create_diamond_fcc.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import absolute_import -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - try: - symbol = element.value - except AttributeError: - symbol = str(element) - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_create_rescaled.py b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_create_rescaled.py deleted file mode 100644 index c7d0a617..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_create_rescaled.py +++ /dev/null @@ -1,11 +0,0 @@ -from __future__ import absolute_import -from aiida.engine import workfunction - - -@workfunction -def create_rescaled(element, scale): - """Workfunction to create and immediately rescale a crystal structure of a given element.""" - structure = create_diamond_fcc(element) - rescaled = rescale(structure, scale) - - return rescaled diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_rescale.py b/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_rescale.py deleted file mode 100644 index 99761a77..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/include/snippets/workflows_rescale.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import absolute_import -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/querybuilder.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/querybuilder.rst deleted file mode 100644 index 7d631ad4..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/querybuilder.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _querybuilder: - -Queries in AiiDA: The Querybuilder -================================== - -This part of the tutorial is provided only in interactive mode through a -Jupyter notebook, which you will be able to run in your browser. -For instructions on starting the Jupyter server, please refer to the -:ref:`setup section`. Once the server is running, -:download:`download this tutorial notebook <../notebooks/querybuilder-tutorial.ipynb>` -and open it in Jupyter. For instructions on downloading these files on -a machine through which you are connected through SSH, refer to -:ref:`this section`. - -The notebook will show you how the ``QueryBuilder`` can be used to query your -database for specific data. It will demonstrate certain concepts and then ask -you to use those to perform certain queries on your own database. Some of these -question cells will have partial solutions that you will have to complete. - -Once you have finished the notebook, you can download a -:download:`notebook with the solutions <../notebooks/querybuilder-solutions.ipynb>` -but try not to use them at first! - -See below for a rendered version of the notebook: - -.. toctree:: - - QueryBuilder Notebook <../notebooks/querybuilder-tutorial.ipynb> diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/setup.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/setup.rst deleted file mode 100644 index d33dd044..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/setup.rst +++ /dev/null @@ -1,208 +0,0 @@ -Getting set up -============== - -.. _2019_epfl_connect: - -Connect to your virtual machine -------------------------------- - -You should each have received from the instructors: - -- an IP address -- a private SSH key ``aiida_tutorial_NUM`` -- a public SSH key ``aiida_tutorial_NUM.pub`` - -The steps below explain how to use these in order to connect to your -personal virtual machine (VM) on Amazon Elastic Cloud 2 -using the `Secure Shell `_ protocol. -The software on this VM is based on `Quantum Mobile -`_ and already includes a -pre-configured AiiDA installation as well as some test data for the tutorial. - -.. note:: - - If you decide to work in pairs, one of you should forward their credentials - to the other and you should both use the same IP address and ssh key. - Since you will be sharing the VM and user account, be careful not to delete - the work of your colleague. - -Linux and MacOS -~~~~~~~~~~~~~~~ - -It's recommended for you to place the ssh key you received in a folder -dedicated to your ssh configuration, to do so: - -- If not already present, create a ``.ssh`` directory in your home - (``mkdir ~/.ssh``), and set its permissions: ``chmod 700 ~/.ssh`` -- Copy the two keys ``aiida_tutorial_NUM`` and ``aiida_tutorial_NUM.pub`` - in the ``~/.ssh`` directory -- Set the correct permissions on the private key: - ``chmod 600 ~/.ssh/aiida_tutorial_NUM``. You can check check with ``ls -l`` - that the permissions of this file are now ``-rw-------``. - -After that ssh key is in place, you can add the following block your -``~/.ssh/config`` file: - - .. code:: bash - - Host aiidatutorial - Hostname IP_ADDRESS - User max - IdentityFile ~/.ssh/aiida_tutorial_NUM - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - -replacing the IP address (``IP_ADDRESS``) and the ``NUM`` by -the one you received. - -Afterwards you can connect to the server using this simple command: - -.. code:: console - - ssh aiidatutorial - -.. note:: - - Here's a copy-paste ready command for you to use directly with zero - configuration: - - .. code:: console - - ssh \ - -i ~/.ssh/aiida-tutorial-max.pem \ - -L 8888:localhost:8888 \ - -L 5000:localhost:5000 \ - -X -C \ - max@IP_ADDRESS - -.. note:: - - On MacOS you need to install `XQuartz `_ - in order to use X-forwarding. - -Windows -~~~~~~~ - -If you're running Windows 10, you may want to consider `installing the Windows Subsystem for Linux `_ (and then follow the instructions above). Alternatively: - -- Install the `PuTTY SSH client `_. - -- Run PuTTYGen - - - Load the ``aiida_tutorial_NN`` private key (button - "Load"). You may need to choose to show "All files (*.*)", - and select the file without any extension (Type: File). - - In the same window, click on "Save private Key", and save the key - with the name ``aiida_tutorial_NN.ppk`` (don't specify a password). - -- Run Pageant - - - It will add a new icon near the clock, in the bottom right of your screen. - - Right click on this Pageant icon, and click on “View Keys”. - - Click on "Add key" and select the ``aiida_tutorial_NN.ppk`` you saved a few steps above. - -- Run PuTTY - - - Put the given IP address as hostname, type ``aiidatutorial`` in "Saved Sessions" - and click "Save". - - Go to Connection > Data and put ``max`` as autologin username. - - Go to Connection > SSH > Tunnels, type ``8888`` in the - "Source Port" box, type ``localhost:8888`` in "Destination" and click "Add". - - Repeat the previous step for port ``5000`` instead of ``8888``. - - Go back to the "Session" screen, select "aiidatutorial" and click "Save" - - Finally, click "Open" (and click "Yes" on the putty security alert - to add the VM to your known hosts). - You should be redirected to a bash terminal on the virtual machine. - -.. note:: - Next time you open PuTTY, select ``aiidatutorial`` and click "Load" - before clicking "Open". - - -In order to enable X-forwarding: - -- Install the `Xming X Server for Windows `_. - -- Configure PuTTy as described in the `Xming wiki `_. - -.. _setup_jupyter: - -Start jupyter -------------- - -Once connected to your virtual machine, type in the remote terminal - -.. code:: bash - - workon aiida - -This will enable the virtual environment in which AiiDA is installed, -allowing you to use AiiDA. Now type in the same shell: - -.. code:: bash - - jupyter notebook --no-browser - -This will run a server with a web application called ``jupyter``, which -is used to create interactive python notebooks. -In order to connect to the jupyter notebook server: - - - copy the URL that has been printed to the terminal (similar to ``http://localhost:8888/?token=2a3ba37cd1...``) - - open a web browser **on your laptop** and paste the URL - - You will see a list of folders on your personal VM. - -While keeping the first ``ssh`` connection running, open another ``ssh`` -connection in a second terminal and type ``workon aiida`` here too. This -terminal is the one we will actually use in this tutorial. - -.. note:: - - Our SSH configuration assumes that ``jupyter`` will serve the notebooks on port 8888. - If you want to serve notebooks on different ports, you'll also need to adjust - the SSH configuration. - - -.. _setup_downloading_files: - -Downloading files ------------------ - -Throughout this tutorial, you will encounter links to download python scripts, jupyter notebooks and more. -These files should be downloaded to the environment/working directory you use to run the tutorial. -In particular, when running the tutorial on a linux virtual machine, copy the link address and download the files to the machine using the ``wget`` utility on the terminal: - - wget - -where you replace ```` with the actual HTTPS link that you copied from the tutorial text in your browser. -This will download that file in your current directory. - - -Troubleshooting ---------------- - -- If you get errors ``ImportError: No module named aiida`` or - ``No command ’verdi’ found``, double check that you have loaded the - virtual environment with ``workon aiida`` before launching ``python``, - ``ipython`` or the ``jupyter`` notebook server. - -- If your browser cannot connect to the jupyter notebook server, check that - you have correctly configured SSH tunneling/forwarding as described - above. Keep in mind that you need to start the jupyter server from the - terminal connected to the VM, while the web browser should be opened locally - on your laptop. - -- See the `jupyter notebook documentation `_ for compatibility of jupyter with various web browsers. - -Getting help ------------- - -There are a number of helpful resources available to you for getting more information about AiiDA. -Please consider: - - * consulting the extensive `AiiDA documentation `_ - * asking in the `Slack channel of the tutorial `_ - * opening a new issue on the `tutorial issue tracker `_ - * asking your neighbor - * asking a tutor diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_cmdline.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_cmdline.rst deleted file mode 100644 index 91cb9868..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_cmdline.rst +++ /dev/null @@ -1,545 +0,0 @@ -Verdi command line -================== - -This part of the tutorial will familiarize you with the ``verdi`` command-line interface (CLI), -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``--help/-h`` flag, e.g.: - - .. code:: bash - - verdi quicksetup -h - -More details on ``verdi`` can be found in the `online documentation `_. - - -.. _setup_verdi_quicksetup: - -Setting up a profile --------------------- - -After installing AiiDA, the first step is to create a "profile". -Typically, you would be using one profile per independent research project. - -The easiest way of setting up a new profile is through ``verdi quicksetup``. -Let's set up a new profile that we will use throughout this tutorial: - -.. code:: bash - - verdi quicksetup - -This will prompt you for some information, such as the name of the profile, your name, etc. -The information about you as a user will be associated with all the data that you create in AiiDA -and is important for attribution, when you start sharing your data with others. -After you have answered all the questions, a new profile will be created, along -with the required database and repository. - -.. note:: - - ``verdi quicksetup`` is a user-friendly wrapper around the ``verdi setup`` command that provides more control over the profile setup. - As explained in `the documentation `_, ``verdi setup`` expects certain external resources, such as the database to already have been pre-configured. - ``verdi quicksetup`` will try to do this for you, but may not be successful in certain environments. - -To see this profile, and any others that may have been configured, run: - -.. code:: bash - - verdi profile list - - Info: configuration folder: /home/max/.aiida - * generic - quicksetup - -Each line, ``generic`` and ``quicksetup`` in this example, corresponds to a profile. -The one marked with an asterisk is the "default" profile, meaning that any ``verdi`` command that you execute will be applied to that profile. - -.. note:: - - The output you get may differ. - The ``generic`` profile is pre-configured on the virtual machine built for the tutorial (but we are not going to use it here). - -Let's change the default profile to the newly created ``quicksetup`` for the rest of the tutorial: - -.. code:: bash - - verdi profile setdefault quicksetup - -From now on, all ``verdi`` commands will apply to the ``quicksetup`` profile. - -.. note:: - - To quickly perform a single command on a profile that is not the default, use the ``-p/--profile`` option: - For example, ``verdi -p generic profile show`` will display the configuration of the ``generic`` profile, despite it not being the current default profile. - - -Importing data --------------- - -Before we start running calculations ourselves, we are going to look at an AiiDA database already created by someone else. -Let's import one from the web: - -.. code:: bash - - verdi import https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2019_05_perovskites_v0.3.aiida - -Contrary to most databases, AiiDA databases contain not only *results* of calculations but also their inputs and information on how a particular result was obtained. -This information, the *data provenance*, is stored in the form of a *directed acyclic graph* (DAG). -In the following, we are going to introduce you to different ways of browsing this graph and will ask you to find out some information regarding the database you just imported. - -.. _aiidagraph: - -Your first AiiDA graph ----------------------- - -:numref:`fig_graph_input_only` shows a typcial example of a calculation represented in an AiiDA graph. -Have a look to the figure and its caption before moving on. - -.. _fig_graph_input_only: -.. figure:: include/images/verdi_graph/batio3/graph-input.png - :width: 100% - - Graph with all inputs (data, circles; and code, diamond) to the Quantum ESPRESSO calculation (square) that you will create in the :ref:`calculations` section of this tutorial. - -.. _fig_graph: -.. figure:: include/images/verdi_graph/batio3/graph-full.png - :width: 100% - - Same as :numref:`fig_graph_input_only`, but also with the outputs that the engine will create and connect automatically. - The ``RemoteData`` node is created during submission and can be thought as a symbolic link to the remote folder in which the calculation runs on the cluster. - The other nodes are created when the calculation has finished, after retrieval and parsing. - The node with linkname 'retrieved' contains the raw output files stored in the AiiDA repository; all other nodes are added by the parser. - Additional nodes (symbolized in gray) can be added by the parser (e.g. an output ``StructureData`` if you performed a relaxation calculation, a ``TrajectoryData`` for molecular dynamics etc.). - -:numref:`fig_graph_input_only` was drawn by hand but you can generate a similar graph automatically by passing the **identifier** of a calculation node to ``verdi graph generate ``. -Identifiers in AiiDA come in three forms: - - * "Primary Key" (PK): An integer, e.g. ``723``, that identifies your entity within your database (automatically assigned) - * `Universally Unique Identifier `_ (UUID): A string, e.g. ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` that identifies your entity globally (automatically assigned) - * Label: A human-readable string, e.g. ``test_qe_calculation`` (manually assigned) - -Any ``verdi`` command that expects an identifier will accept a PK, a UUID or a label (although not all entities have a label by default). -While PKs are often shorter than UUIDs and can be easier to remember, they are only unique within your database. -**Whenever you intend to share your data with others, use UUIDs to refer to nodes.** - -.. note:: - For UUIDs, it is sufficient to specify a subset (starting at the beginning) as long as it can already be uniquely resolved. - For more information on identifiers in ``verdi`` and AiiDA in general, see the `documentation `_. - -For the remainder of this section, fields enclosed in angular brackets, such as ````, are placeholders that you should replace before executing the command. -With that in mind, let's generate a graph for the calculation node with UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``: - -.. code:: bash - - verdi graph generate - -This command will create the file ``.dot`` that can be rendered by means of the utility ``dot`` as follows: - -.. code:: bash - - dot -Tpdf -o .pdf .dot - -which will create a pdf file ``.pdf``. - - -You can open this file on the Amazon machine by using ``evince`` or, if the ssh connection is too slow, copy it via ``scp`` to your local machine. -To do so, if you are using Linux/Mac OS X, you can type in your *local* machine: - -.. code:: bash - - scp aiidatutorial: - -and then open the file. -Alternatively, you can use graphical software to achieve the same, for instance: WinSCP on Windows, Cyberduck on the Mac, or the 'Connect to server' option in the main menu after clicking on the desktop for Ubuntu. - - -The provenance browser ----------------------- - -While the ``verdi`` CLI provides full access to the data underlying the provenance graph (and we will return to it in :numref:`inspecting_nodes`), -a more intuitive tool for browsing AiiDA graphs is the interactive -provenance browser available on `Materials -Cloud `__. - -In order to use it, we first need to start the `AiiDA REST API `_: - -.. code:: bash - - verdi restapi - * Serving Flask app "aiida.restapi.run_api" (lazy loading) - * Environment: production - WARNING: Do not use the development server in a production environment. - Use a production WSGI server instead. - * Debug mode: off - * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) - -Now you can connect the provenance browser to your local REST API: - -- Open the |provenance_browser| on your laptop -- In the form, paste the (local) URL ``http://127.0.0.1:5000/api/v3`` - of our REST API -- Click "GO!" - -.. |provenance_browser| raw:: html - - provenance explorer - -Once the provenance browser javascript application has been loaded by your browser, it is communicating directly with the REST API and your data never leaves your computer. - -.. note:: - In order for this to work on your laptop, while the REST API is running on the virtual machine, we've enabled SSH tunneling for port ``5000`` in :ref:`2019_epfl_connect`. - -Start by clicking on the Details of a ``CalcJobNode`` and use the graph explorer to complete the exercise below. -If you ever get lost, just go to the "Details" tab, enter ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` and click on the "GO" button. - -.. admonition:: Exercise - - Use the provenance browser in order to figure out: - - - When was the calculation run and who run it? - - Was it a serial or a parallel calculation? How many MPI processes were used? - - What inputs did the calculation take? - - What code was used and what was the name of the executable? - - How many calculations were performed using this code? - - -.. _inspecting_nodes: - -Inspecting the nodes of a graph -------------------------------- - - -Processes ---------- - -Anything that 'runs' in AiiDA, be it calculations or workflows, is considered a ``Process``. -To get a list of currently running processes, use: - -.. code:: bash - - verdi process list - -.. note:: - - The first time you run this command, it might take a few seconds. - Subsequent calls will be faster. - -which should be empty: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - - Total results: 0 - - Info: last time an entry changed state: never - -Let's see whether there are any *finished* processes in the database by passing the ``-S/--process-state`` flag: - -.. code:: bash - - verdi process list -S finished - -This command will list all the processes that have a process state ``Finished`` and should look something like: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - 1178 1653D ago PwCalculaton ⏹ Finished [0] - 1953 1653D ago PwCalculaton ⏹ Finished [0] - 1734 1653D ago PwCalculaton ⏹ Finished [0] - 336 1653D ago PwCalculaton ⏹ Finished [0] - 1056 1653D ago PwCalculaton ⏹ Finished [0] - 1369 1653D ago PwCalculaton ⏹ Finished [0] - - Total results: 6 - - Info: last time an entry changed state: never - -Processes can be in any of the following states: - - * ``Created`` - * ``Waiting`` - * ``Running`` - * ``Finished`` - * ``Excepted`` - * ``Killed`` - -The first three states are 'active' states, meaning the process is not done yet, and the last three are 'terminal' states. -Once a process is in a terminal state, it will never become active again. -The `official documentation `_ contains more details on process states. - -In order to list processes of *all* states, use the ``-a/--all`` flag: - -.. code:: bash - - verdi process list -a - -This command will list all the processes that have *ever* been launched. -As your database will grow, so will the output of this command. -To limit the number of results, you can use the ``-p/--past-days `` option, that will only show processes that were created ``NUM`` days ago. -For example, this lists all processes launched since yesterday: - -.. code:: bash - - verdi process list -a -p1 - -.. _2019-aiida-identifiers: - -Each row of the output identifies a process with some basic information about its status. -For a more detailed list of properties, you can use ``verdi process show``, but to address any specific process, you need an identifier for it. - -Let's revisit the process with the UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``, this time using the CLI: - -.. code:: bash - - verdi process show ce81c420-7751-48f6-af8e-eb7c6a30cec - -Producing the output: - -.. code:: bash - - Property Value - ------------- ------------------------------------ - type CalcJobNode - pk 828 - uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 - label - description - ctime 2014-10-27 17:51:21.781045+00:00 - mtime 2019-05-09 14:10:09.307986+00:00 - process state Finished - exit status 0 - computer [1] daint - - Inputs PK Type - ---------- ---- ------------- - pseudos - Ba 611 UpfData - O 661 UpfData - Ti 989 UpfData - code 825 Code - kpoints 811 KpointsData - parameters 829 Dict - settings 813 Dict - structure 27 StructureData - - Outputs PK Type - ----------------------- ---- ------------- - output_kpoints 1894 KpointsData - output_parameters 62 Dict - output_structure 61 StructureData - output_trajectory_array 63 ArrayData - remote_folder 357 RemoteData - retrieved 60 FolderData - -You can use the PKs shown for the inputs and outputs to get more information about those nodes. - -.. warning:: - - Since the inputs and outputs are ``Data`` nodes, not ``Process`` nodes, use ``verdi node show`` instead. - - -Dict and CalcJobNode -~~~~~~~~~~~~~~~~~~~~ - -Let's investigate some of the nodes appearing in the graph. -From the inputs of the process, let's choose the node of type ``Dict`` with input link name ``parameters`` and type in the terminal: - -.. code:: bash - - verdi data dict show - -where ```` is the PK of the node. - -A ``Dict`` contains a dictionary (i.e. key–value pairs), stored in the database in a format ready to be queried. -We will learn how to run queries later on in this tutorial. -The command above will print the content dictionary, containing the parameters used to define the input file for the calculation. -You can compare the dictionary with the content of the raw input file to Quantum ESPRESSO (that was generated by AiiDA) via the command: - -.. code:: bash - - verdi calcjob inputcat - -where you provide the identifier of the calculation node. -Check the consistency of the parameters written in the input file and those stored in the ``Dict`` node. -Even if you don't know the meaning of the input flags of a Quantum ESPRESSO calculation, you should be able to see how the input dictionary has been converted to Fortran namelists. - -The previous command just printed the content of the 'default' input file ``aiida.in``. -To see a list of all the files used to run a calculation (input file, submission script, etc.) instead type: - -.. code:: bash - - verdi calcjob inputls - -Adding a ``--color`` flag allows you to easily distinguish files from folders by a different coloring. -Once you know the name of the file you want to visualize, you can call the ``verdi calcjob inputcat [PATH]`` command specifying the path. -For instance, to see the submission script, you can do: - -.. code:: bash - - verdi calcjob inputcat _aiidasubmit.sh - -StructureData -~~~~~~~~~~~~~ - -Now let us focus on ``StructureData`` objects, which represent a crystal structure. -We can consider for instance the input structure to the calculation we were considering before (it should have the UUID ``3a4b1270``). -Such objects can be inspected interactively by means of an atomic viewer such as the one provided by ``ase``. -AiiDA however supports several other viewers such as ``xcrysden``, ``jmol``, and ``vmd``. -Type in the terminal: - -.. code:: bash - - verdi data structure show --format ase - -to show the selected structure, although it will take a few seconds to appear -You should be able to rotate the view with the right mouse button. - -.. note:: - - If you receive some errors, make sure you started your SSH connection with the ``-X`` or ``-Y`` flag. - -Alternatively, especially if showing them interactively is too slow over SSH, you can export the content of a structure node in various popular formats such as ``xyz`` or ``xsf``. -This is achieved by typing in the terminal: - -.. code:: bash - - # verdi data structure export --format xsf > .xsf - verdi data structure export --format xsf 254e5a86 > 254e5a86.xsf - -You can open the generated ``xsf`` file and observe the cell and the coordinates. -Then, you can then copy ``.xsf`` from the Amazon machine to your local one and then visualize it, e.g. with ``xcrysden`` (if you have it installed): - -.. code:: bash - - xcrysden --xsf .xsf - -Codes and computers -~~~~~~~~~~~~~~~~~~~ - -Let us focus now on the nodes of type ``Code``. -A code represents (in the database) the actual executable used to run the calculation. -Find the identifier of such a node in the graph and type: - -.. code:: bash - - verdi code show - -The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc. -To show a list of all available codes type: - -.. code:: bash - - verdi code list - -If you want to show all codes, including hidden ones and those created by other users, use ``verdi code list -a -A``. -Now, among the entries of the output you should also find the code just shown. - -Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command: - -.. code:: bash - - verdi computer list -a - -The ``-a`` flag shows all computers, also the one imported in your database but that you did not configure, i.e. to which you don't have access. -Details about each computer can be obtained by the command: - -.. code:: bash - - verdi computer show - -Now you have the tools to answer the question: what is the scheduler installed on the computer where the calculations of the graph have run? - -Calculation results -~~~~~~~~~~~~~~~~~~~ - -The results of a calculation can be accessed directly from the calculation node. -Type in the terminal: - -.. code:: bash - - verdi calcjob res - -which will print the output dictionary of the 'scalar' results parsed by AiiDA at the end of the calculation. -Note that this is actually a shortcut for: - -.. code:: bash - - verdi data dict show - -where ``IDENTIFIER`` refers to the ``Dict`` node attached as an output of the calculation node, with link name ``output_parameters``. -By looking at the output of the command, what is the Fermi energy of the calculation with UUID ``ce81c420``? - -Similarly to what you did for the calculation inputs, you can access the output files via the commands: - -.. code:: bash - - verdi calcjob outputls - -and - -.. code:: bash - - verdi calcjob outputcat - -Use the latter to verify that the Fermi energy that you have found in the last step has been extracted correctly from the output file - -.. note:: - - Hint: filter the lines containing the string 'Fermi', e.g. using ``grep``, to isolate the relevant lines - -The results of calculations are stored in two ways: ``Dict`` objects are stored in the database, which makes querying them very convenient, whereas ``ArrayData`` objects are stored on the disk. -Once more, use the command ``verdi data array show `` to determine the Fermi energy obtained from calculation with the UUID ``ce81c420``. -This time you will need to use the identifier of the output ``ArrayData`` of the calculation, with link name ``output_trajectory_array``. -As you might have realized the difference now is that the whole series of values of the Fermi energy calculated after each relax/vc-relax step are stored. -The choice of what to store in ``Dict`` and ``ArrayData`` nodes is made by the parser of ``pw.x`` implemented in the ``aiida-quantumespresso`` plugin. - -(Optional section) Comments -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -AiiDA offers the possibility to attach comments to a any node, in order to be able to remember more easily its details. -Node with UUID prefix ``ce81c420`` should have no comments, but you can add a very instructive one by typing in the terminal: - -.. code:: bash - - verdi comment add "vc-relax of a BaTiO3 done with QE pw.x" -N - -Now, if you ask for a list of all comments associated to that calculation by typing: - -.. code:: bash - - verdi comment show - -the comment that you just added will appear together with some useful information such as its creator and creation date. -We let you play with the other options of ``verdi comment`` command to learn how to update or remove comments. - -AiiDA groups of calculations ----------------------------- - -In AiiDA, calculations (and more generally nodes) can be organized in groups, which are particularly useful to assign a set of calculations or data to a common project. -This allows you to have quick access to a whole set of calculations with no need for tedious browsing of the database or writing complex scripts for retrieving the desired nodes. -Type in the terminal: - -.. code:: bash - - verdi group list -a -A - -to show a list of **all** groups that exist in the database. -Choose the PK of the group named ``tutorial_pbesol`` and look at the calculations that it contains by typing: - -.. code:: bash - - verdi group show - -In this case, we have used the name of the group to organize calculations according to the pseudopotential that has been used to perform them. -Among the rows printed by the last command you will be able to find the calculation we have been inspecting until now. - -If, instead, you want to know all the groups to which a specific node belongs, you can run: - -.. code:: bash - - verdi group list -N/--node diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_shell.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_shell.rst deleted file mode 100644 index 6933f1cf..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_shell.rst +++ /dev/null @@ -1,416 +0,0 @@ -Verdi shell and AiiDA objects -============================= - -In this section we will use an interactive IPython environment with all the -basic AiiDA classes already loaded. We propose two realizations of such a -tool. The first consists of a special IPython shell where all the AiiDA -classes, methods and functions are accessible. Type in the terminal - -.. code:: bash - - verdi shell - -For all the everyday AiiDA-based operations, i.e. creating, querying, and -using AiiDA objects, the ``verdi shell`` is probably the best tool. In this -case, we suggest that you use two terminals, one for the ``verdi shell`` and -one to execute bash commands. - -The second option is based on Jupyter notebooks and is probably most suitable -to the purposes of our tutorial. Go to the browser where you have opened -``jupyter`` and click ``New`` → ``Python 3`` (top right corner). This will -open an IPython-based Jupyter notebook, made of cells in which you can type -portions of python code. The code will not be executed until you press -``Shift+Enter`` from within a cell. Type in the first cell - -.. code:: ipython - - %aiida - -and execute it. This will set exactly the same environment as the -``verdi shell``. The notebook will be automatically saved upon any -modification and when you think you are done, you can export your notebook in -many formats by going to ``File`` → ``Download as``. We suggest you to have a -look at the drop-down menus ``Insert`` and ``Cell`` where you will find the -main commands to manage the cells of your notebook. - -.. note:: - - The ``verdi shell`` and Jupyter - notebook are completely equivalent. Use either according to your - personal preference. - -You will still sometimes need to type command-line instructions in ``bash`` in -the first terminal you opened. To differentiate these from the commands to be -typed in the ``verdi shell``, the latter will be marked in this document by a -green background, like: - -.. code:: python - - some verdi shell command - -while command-line instructions in ``bash`` to be typed into a terminal will -be written with a blue background: - -.. code:: bash - - some bash command - -Alternatively, to avoid changing terminal, you can execute ``bash`` commands -within the ``verdi shell`` or the notebook by adding an exclamation mark before -the command itself: - -.. code:: ipython - - !some bash command - -.. _2019_EPFL_loadnode: - -Loading a node --------------- - -Most AiiDA objects are represented by nodes, identified in the database by its -``PK`` (an integer). You can access a node using the following command -in the shell: - -.. code:: python - - node = load_node(PK) - -Load a node using one of the calculation ``PK`` s visible in the graph you -displayed in the previous section of the tutorial. Then get the energy of the -calculation with the command - -.. code:: python - - node.res.energy - -You can also type - -.. code:: python - - node.res. - -and then press ``TAB`` to see all the available output results of the -calculation. - -Loading specific kinds of nodes -------------------------------- - -Pseudopotentials -~~~~~~~~~~~~~~~~ - -From the graph you generated in section :ref:`aiidagraph`, -find the ``PK`` of the pseudopotential file (LDA). Load it and -show what elements it corresponds to by typing: - -.. code:: python - - upf = load_node(PK) - upf.element - -All methods of ``UpfData`` are accessible by typing ``upf.`` and then pressing ``TAB``. - -k-points -~~~~~~~~ - -A set of k-points in the Brillouin zone is represented by an instance of the -``KpointsData`` class. Choose one from the graph of -produced in section :ref:`aiidagraph`, -load it as ``kpoints`` and inspect its content: - -.. code:: python - - kpoints.get_kpoints_mesh() - -Then get the full (explicit) list of k-points belonging to this mesh using - -.. code:: python - - kpoints.get_kpoints_mesh(print_list=True) - -If this throws an ``AttributeError``, it means that the kpoints instance does not represent a regular mesh but rather a list of k-points defined by their crystal coordinates (typically used when plotting a band structure). -In this case, get the list of k-points coordinates using - -.. code:: python - - kpoints.get_kpoints() - -Conversely, if the `KpointsData` node `does` actually represent a mesh, this method is the one, that when called, will throw an ``AttributeError``. - -If you prefer Cartesian (rather than crystal) coordinates, type - -.. code:: python - - kpoints.get_kpoints(cartesian=True) - -For later use in this tutorial, let us try now to create a kpoints instance, -to describe a regular (2 x 2 x 2) mesh of k-points, centered at the Gamma -point (i.e. without offset). This can be done with the following commands: - -.. code:: python - - KpointsData = DataFactory('array.kpoints') - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh] * 3) - kpoints.store() - -This function loads the appropriate class defined in a string (here -``array.kpoints``). Therefore, ``KpointsData`` is not a class instance, but -the kpoints class itself! - -While it is also possible to import ``KpointsData`` directly, it is recommended -to use the ``DataFactory`` function instead, as this is more future-proof: -even if the import path of the class changes in the future, its entry point -string (``array.kpoints``) will remain stable. - -Parameters -~~~~~~~~~~ - -Dictionaries with various parameters are represented in AiiDA by ``Dict`` nodes. -Get the PK and load the input parameters of a calculation in the graph produced in section :ref:`aiidagraph`. -Then display its content by typing - -.. code:: python - - params = load_node('') - YOUR_DICT = params.get_dict() - YOUR_DICT - -Modify the python dictionary ``YOUR_DICT`` so that the wave-function cutoff is now set to 20 -Ry. Note that you cannot modify an object already stored in the database. To -write the modified dictionary to the database, create a new object of class ``Dict``: - -.. code:: python - - Dict = DataFactory('dict') - new_params = Dict(dict=YOUR_DICT) - -where ``YOUR_DICT`` is the modified python dictionary. -Note that ``new_params`` is not yet stored in the database. -In fact, typing ``new_params`` in the verdi shell will print a string notifying you of its 'unstored' status. -Let's finish by storing the ``new_params`` dictionary node in the datbase: - -.. code:: python - - new_params.store() - -Structures -~~~~~~~~~~ - -Find a structure in the graph you generated in section :ref:`aiidagraph` and load it. -Display its chemical formula, atomic positions and species using - -.. code:: python - - structure.get_formula() - structure.sites - -where ``structure`` is the structure you loaded. If you are familiar with ASE -and PYMATGEN, you can convert this structure to those formats by typing - -.. code:: python - - structure.get_ase() - structure.get_pymatgen() - -Let’s try now to define a new structure to study, specifically a silicon -crystal. In the ``verdi shell``, define a cubic unit cell as a 3 x 3 matrix, -with lattice parameter `a`\ :sub:`lat`\ `= 5.4` Å: - -.. code:: python - - alat = 5.4 - the_cell = [[alat/2, alat/2, 0.], [alat/2, 0., alat/2], [0., alat/2, alat/2]] - -.. note:: - - Default units for crystal structure cell and coordinates in AiiDA are Å (Ångström). - -Structures in AiiDA are instances of the class ``StructureData``: load it in the -verdi shell - -.. code:: python - - StructureData = DataFactory('structure') - -Now, initialize the class instance (i.e. the actual structure we want to study) by -the command - -.. code:: python - - structure = StructureData(cell=the_cell) - -which sets the cubic cell defined before. From now on, you can access the cell -with the command - -.. code:: python - - structure.cell - -Finally, append each of the 2 atoms of the cell command. You can do it using -commands like - -.. code:: python - - structure.append_atom(position=(alat/4., alat/4., alat/4.), symbols="Si") - -for the first ‘Si’ atom. Repeat it for the other atomic site (0, 0, 0). You -can access and inspect the structure sites with the command - -.. code:: python - - structure.sites - -If you make a mistake, start over from -``structure = StructureData(cell=the_cell)``, or equivalently use -``structure.clear_kinds()`` to remove all kinds (atomic species) and sites. -Alternatively, AiiDA structures can also be converted directly from ASE -structures [#f1]_ using - -.. code:: python - - from ase.spacegroup import crystal - ase_structure = crystal('Si', [(0, 0, 0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90], primitive_cell=True) - structure = StructureData(ase=ase_structure) - -Now you can store the new structure object in the database with the command: - -.. code:: python - - structure.store() - -Finally, we can also import the silicon structure from an external (online) -repository such as the Crystallography Open Database (COD): - -.. code:: python - - from aiida.tools.dbimporters.plugins.cod import CodDbImporter - importer = CodDbImporter() - for entry in importer.query(formula='Si', spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print("Formula", structure.get_formula()) - print("Unit cell volume: ", structure.get_cell_volume()) - -In that case two duplicate structures are found for 'Si'. - -Accessing inputs and outputs ----------------------------- - -Load again the calculation node used in Section :ref:`2019_EPFL_loadnode`: - -.. code:: python - - calc = load_node(PK) - -Then type - -.. code:: python - - calc.inputs. - -and press ``TAB``: you will see all the link names between the calculation and -its input nodes. You can use a specific linkname to access the corresponding -input node, e.g.: - -.. code:: python - - calc.inputs.structure - -Similarly, if you type: - -.. code:: python - - calc.outputs. - -and then ``TAB``, you will list all output link names of the calculation. One -of them leads to the structure that was the input of ``calc`` we loaded -previously: - -.. code:: python - - calc.outputs.output_structure - -Note that links have a single name, that was assigned by the calculation that -used the corresponding input or produced the corresponding output, as -illustrated in section :ref:`aiidagraph`. - -For a more programmatic approach, you can get a represenation of the inputs and outputs of a node, say ``calc``, through the following methods: - -.. code:: python - - calc_incoming = calc.get_incoming() - calc_outgoing = calc.get_outgoing() - -These methods will return an instance of the ``LinkManager`` class. -You can iterate over the neighboring nodes by calling the ``.all()`` method: - -.. code:: python - - for entry in calc.get_outgoing(): - print(entry.link_label, entry.link_type, entry.node) - -each entry returned by ``.all()`` is a ``LinkTriple``, a named tuple, from which you can get the link label and type and the neighboring node itself. -If you print one, you will see something like: - -.. code:: python - - LinkTriple(node=, link_type=, link_label=u'output_parameters') - -There are many other convenience methods on the ``LinkManager``. -For example if you are only interested in the link labels you can use: - -.. code:: python - - calc.get_outgoing().all_link_labels() - -which will return a list of all the labels of the outgoing links. -Likewise, ``.all_nodes()`` will give you a list of all the nodes to which links are going out from the ``calc`` node. -If you are looking for the node with a specific label, you can use: - -.. code:: python - - calc.get_outgoing().get_node_by_label('output_parameters') - -The ``get_outgoing`` and ``get_incoming`` methods also support filtering on various properties, such as the link label. -For example, if you only want to get the outgoing links whose label starts with ``output``, you can do the following: - -.. code:: python - - calc.get_outgoing(link_label_filter='output%').all_nodes() - - -Pseudopotential families ------------------------- - -Pseudopotentials in AiiDA are grouped in 'families' that contain one single -pseudo per element. We will see how to work with UPF pseudopotentials (the -format used by Quantum ESPRESSO and some other codes). Download and untar the -SSSP pseudopotentials via the commands: - -.. code:: bash - - wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz - -Then you can upload the whole set of pseudopotentials to AiiDA by using the -following ``verdi`` command: - -.. code:: bash - - verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' - -In the command above, ``SSSP_efficiency_pseudos`` is the folder containing the -pseudopotentials, ``'SSSP'`` is the name given to the family, and the last argument -is its description. Finally, you can list all the pseudo families present in -the database with - -.. code:: bash - - verdi data upf listfamilies - - -.. rubric:: Footnotes - -.. [#f1] We purposefully do not provide advanced commands for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks already exist (such as ASE or pymatgen). diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/workflows.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/workflows.rst deleted file mode 100644 index 0cea404e..00000000 --- a/docs/pages/2019_MARVEL_Psik_MaX/sections/workflows.rst +++ /dev/null @@ -1,504 +0,0 @@ -Workflows -========= - -The aim of the last part of this tutorial is to introduce the concept of workflows in AiiDA. - -In this section, we will ask you to: - -1. Understand how to keep the provenance when running small python scripts to convert one data object into another (postprocessing, preparation of inputs, etc.) -2. Understand how to represent simple python functions in the AiiDA database -3. Learn how to write a simple workflow in AiiDA (without and with remote calculation submission) -4. Learn how to write a workflow with checkpoints: this means that, even if your workflow requires external calculations to start, they and their dependencies are managed through the daemon. - While you are waiting for the calculations to complete, you can stop and even shutdown the computer in which AiiDA is running. - When you restart, the workflow will continue from where it was. -5. (optional) Go a bit deeper in the syntax of workflows with checkpoints (``WorkChain``), e.g. implementing a convergence workflow using ``while`` loops. - -.. note:: - - This is probably the most 'complex' part of the tutorial. - We suggest that you try to understand the underlying logic behind the scripts, without focusing too much on the details of the workflows implementation or the syntax. - If you want, you can then focus more on the technicalities in a second reading. - -Introduction ------------- - -The ultimate aim of this section is to create a workflow to calculate the equation of state of silicon. -This is a very common task for an *ab initio* researcher. -An equation of state consists in calculating the total energy (E) as a function of the unit cell volume (V). -The minimal energy is reached at the equilibrium volume. -Equivalently, the equilibrium is defined by a vanishing pressure (p=-dE/dV). -In the vicinity of the minimum, the functional form of the equation of state can be approximated by a parabola. -Such an approximation greatly simplifies the calculation of the bulk modulus, that is proportional to the second derivative of the energy (a more advanced treatment requires fitting the curve with, e.g., the Birch–Murnaghan expression). - -The process of calculating an equation of state puts together several operations. -First, we need to define and store in the AiiDA database the basic structure of, e.g., bulk Si. -Next, one has to define several structures with different lattice parameters. -Those structures must be connected between them in the database, in order to ensure that their provenance is recorded. -In other words, we want to be sure that in the future we will know that if we find a bunch of rescaled structures in the database, they all descend from the same one. -How to link two nodes in the database in an easy way is the subject of :ref:`provenancewf`. - -In the following sections, the newly created structures will then serve as an input for total energy calculations performed, in this tutorial, with Quantum ESPRESSO. -This task is very similar to what you have done in the previous part of the tutorial. -Finally, you will fit the resulting energies as a function of volume to get the bulk modulus. -As the EOS task is very common, we will show how to automate its computation with workflows, and how to deal with both serial and parallel (i.e., independent) execution of multiple tasks. -Finally, we will show how to introduce more complex logic in your workflows such as loops and conditional statements (:ref:`see this section`), with an example on a convergence loop to find iteratively the minimum of an EOS. - -.. _provenancewf: - -Process functions: a way to generalize provenance in AiiDA ----------------------------------------------------------- - -Imagine having a function that takes as input a string of the name of a chemical element and generates the corresponding bulk structure as a ``StructureData`` object. -The function might look like the following snippet: - -.. literalinclude:: include/snippets/workflows_create_diamond_fcc.py - :language: python - -For the equation of state you need another function that takes as input a ``StructureData`` object and a rescaling factor, and returns a ``StructureData`` object with the rescaled lattice parameter: - -.. literalinclude:: include/snippets/workflows_rescale.py - :language: python - -In order to generate the rescaled starting structures, say for five different lattice parameters you would combine the two functions. -Open a ``verdi shell``, define the two functions from the previous snippets and enter the following commands: - -.. code:: python - - initial_structure = create_diamond_fcc('Si') - rescaled_structures = [rescale(initial_structure, factor) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and store them in the database: - -.. code:: python - - initial_structure.store() - for structure in rescaled_structures: - structure.store() - -As expected, all the structures that you have created are not linked in any manner as you can verify via the ``get_incoming()``/``get_outgoing()`` methods of the ``StuctureData`` class. -Instead, you would like these objects to be connected as sketched in :numref:`fig_provenance_process_functions`. - -.. _fig_provenance_process_functions: -.. figure:: include/images/process_functions.png - - Typical graphs created by using calculation and work functions. - (a) The calculation function ``create_structure`` takes a ``Str`` object as input and returns a single ``StructureData`` object which is used as input for the calculation function ``rescale`` together with a ``Float`` object. - This latter calculation function returns another ``StructureData`` object, defining a crystal with a rescaled lattice constant. - (b) Graph generated by a work function that calls two calculation functions. - A wrapper work function ``create_rescaled`` calls serially the calculation functions ``create_structure`` and ``rescale``. - This relationship is stored via ``CALL`` links. - - -Now that you are familiar with AiiDA, you know that the way to connect two data nodes is through a calculation. -In order to 'wrap' python functions and automate the generation of the needed links, in AiiDA we provide you with what we call 'process functions'. -There are two variants of process functions: - - * calculation functions - * work functions - -These operate mostly the same, but they should be used for different purposes, which will become clear later. -A normal function can be converted to a calculation function by using the ``@calcfunction`` decorator [#f1]_ that takes care of storing the execution as a calculation and adding the links between the input and output data nodes. - -To turn the original functions ``create_diamond_fcc`` and ``rescale`` into calculation functions, simply change the definition as follows: - -.. code:: python - - # Add this import - from aiida.engine import calcfunction - - # Add decorators - @calcfunction - def create_diamond_fcc(element): - ... (same as above) - - @calcfunction - def rescale(structure, scale): - ... (same as above) - -Note that the only change is that the function definitions were 'decorated' with the ``@calcfunction`` line. -This is the only thing that is necessary to transform the plain python functions magically into fully-fledged AiiDA process functions. - -.. note:: - - The only additional change necessary, is that process function input and outputs need to be ``Data`` nodes, so that they can be stored in the database. - AiiDA objects such as ``StructureData``, ``Dict``, etc. carry around information about their provenance as stored in the database. - This is why we must use the special database-storable types ``Float``, ``Str``, etc. as shown in the snippet below. - -Try now to run the following script: - -.. code:: python - - from aiida.orm import Float, Str - initial_structure = create_diamond_fcc(Str('Si')) - rescaled_structures = [rescale(initial_structure, Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and check now that the output of ``initial_structure`` as well as the input of the rescaled structures point to an intermediate node, representing the execution of the calculation function, see :numref:`fig_provenance_process_functions`. -For instance, you can check that the output links of ``initial_structure`` are the five ``rescale`` calculations: - -.. code:: python - - initial_structure.get_outgoing().all_nodes() - -which outputs - -.. code:: bash - - [, - , - , - , - ] - -and the inputs of each ``CalcFunctionNode`` 'rescale' are obtained with: - -.. code:: python - - for structure in initial_structure.get_outgoing().all_nodes(): - print(structure.get_incoming().all_nodes()) - -that will return - -.. code:: bash - - [, ] - [, ] - [, ] - [, ] - [, ] - -Function nesting -~~~~~~~~~~~~~~~~ -Calculation functions can be 'chained' together by wrapping them together in a work function. -The work function works almost exactly the same as a calculation function, except that it cannot 'create' data, but rather is used to 'call' calculation functions that do the calculations for it. -The calculation functions that it calls will be automatically linked in the provenance graph through 'call' links. -As an example, let us combine the two previously defined calculation functions by means of a wrapper work function called 'create_rescaled' that takes as input the element and the rescale factor. - -Type in your shell (or modify the functions defined in ``create_rescale.py`` and then run): - -.. include:: include/snippets/workflows_create_rescaled.py - :code: python - -and create an already rescaled structure by typing - -.. code:: python - - rescaled = create_rescaled(element=Str('Si'), scale=Float(0.98)) - -Now inspect the input links of ``rescaled``: - -.. code:: ipython - - In [6]: rescaled.get_incoming().all_nodes() - Out[6]: - [, - ] - -The object ``rescaled`` has two incoming links, corresponding to *two* different calculations as input. -These correspond to the calculations 'create_rescaled' and 'rescale' as shown in :numref:`fig_provenance_process_functions`. -To see the 'call' link, inspect now the outputs of the ``WorkFunctionNode`` which corresponds to the ``create_rescaled`` work function. -Write down its ```` (in general, it will be different from 441), then in the shell load the corresponding node and inspect the outputs: - -.. code:: ipython - - In [12]: node = load_node() - In [13]: node.get_outgoing().all() - -You should be able to identify the two children calculations as well as the final structure (you will see the process nodes linked via CALL links: these are process-to-process links representing the fact that ``create_rescaled`` called two calculation functions). -The graphical representation of what you have in the database should match :numref:`fig_provenance_process_functions`. - -.. _sync: - -Run a simple workflow ---------------------- -Let us now use the work and calculation functions that we have just created to build a simple workflow to calculate the equation of state of silicon. -We will consider five different values of the lattice parameter obtained rescaling the experimental minimum, ``a=5.431``, by a factor in ``[0.96, 0.98, 1.0, 1.02, 1.04]``. -We will write a simple script that runs a series of five calculations and at the end returns the volume and the total energy corresponding to each value of the lattice parameter. -For your convenience, besides the functions that you have written so far in the file ``create_rescale.py``, we provide you with some other utilities to get the correct pseudopotential and to generate a pw input file, in :download:`common_wf.py <../scripts/common_wf.py>`. - -We have already created the following script, which you can :download:`download <../scripts/simple_sync_workflow.py>`, but please go through the lines carefully and make sure you understand them. -We suggest that you first have a careful look at it before running it: - -.. include:: ../scripts/simple_sync_workflow.py - :code: python - -If you look into the previous piece of code, you will notice that the way we submit a QE calculation is slightly different from what you have seen in the first part of the tutorial. -The following: - -.. code:: python - - calculations[label] = run(PwCalculation, **inputs) - -runs in the current python session (without the daemon), waits for its completion and returns the output in the user-defined variable ``result``. -The latter is a dictionary whose values are the output nodes generated by the work function, with the link labels as keys. -For example once the function is finished, in order to access the total energy, we need to access the ``Dict`` node which is linked via the 'output_parameters' link (see again Fig. 1 of Day 1 Tutorial, to see inputs and outputs of a Quantum ESPRESSO calculation). -Once the right node is retrieved as ``result['output_parameters']``, we need to get the ``energy`` attribute. -The global operation is achieved by the command - -.. code:: python - - result['output_parameters'].dict.energy - -To collect these results from the various calculations into a single data node, we need one final calculation function to do so. -Since the ``run_eos_wf`` is a 'workflow'-like processes, which cannot create data, this operation **cannot** simply be done in the work function body itself. -To keep the provenance **we have to** do this through a calculation function. -This is done by the ``create_eos_dictionary`` calculation function, that receives as inputs, the output parameters nodes from the completed calculations: - -.. code:: python - - @calcfunction - def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - -It constructs a new ``Dict`` node that contains a single value ``eos`` which is a list of tuples with the relevant data for each calculation. -If you were to inspect the returned data node, with for example ``verdi data dict show`` you would see something like: - -.. code:: bash - - { - "eos": [ - [ - 40.04786949775, - -308.193208771881, - "eV" - ], - [ - 37.6927343883263, - -307.990673492459, - "eV" - ], - [ - 35.4317918679613, - -307.666113525946, - "eV" - ], - [ - 45.048406674717, - -308.308206255253, - "eV" - ], - [ - 42.4991194939683, - -308.293522312459, - "eV" - ] - ] - } - -As you see, the function ``run_eos_wf`` has been decorated as a work function to keep track of the provenance. -To run the workflow it suffices to call the function ``run_eos_wf`` in a python script providing the required input parameters. -For simplicity, we have included few lines at the end of the script that invoke the function with a static choice of parameters: - -.. code:: python - - def run_eos(code=load_code('qe-6.3-pw@localhost'), pseudo_family='SSSP', element='Si'): - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - if __name__ == '__main__': - run_eos() - -To get a reference to the node that represents the function execution, we can ask the ``run`` function to return the node, in addition to the results. -Instead of calling the work function to run it, we can use the method ``run_get_node``: - -.. code:: python - - results, node = run_eos_wf.run_get_node(code, Str(pseudo_family), Str(element)) - print('run_eos_wf<{}> completed'.format(node.pk)) - -Run the workflow: - -.. code:: bash - - verdi run simple_sync_workflow.py - -The command above locks the shell until the full workflow has completed (we will see in a moment how to avoid this). -While the function is running, you can use (in a different shell) the command ``verdi process list`` to show ongoing and finished processes. -You can 'grep' for the ```` you are interested in. -Additionally, you can use the command ``verdi process status `` to show the tree of the calculatino functions called by the root work function with a given ````. - -Wait for the work function to finish, then call the function ``plot_eos()`` that we provided in the file ``common_wf.py`` to plot the equation of state and fit it with a Birch–Murnaghan equation. - -.. _wf_multiple_calcs: - -Run multiple calculations -------------------------- - -You should have noticed that the calculations for different lattice parameters are executed serially, although they might perfectly be executed in parallel because their inputs and outputs are not connected in any way. -In the language of workflows, these calculations are executed in a synchronous (or blocking) way, whereas we would like to have them running *asynchronously* (i.e., in a non-blocking way, to run them in parallel). -One way to achieve this, is to submit the calculation to the daemon using the ``submit`` function. -Make a copy of the script ``simple_sync_workflow.py`` that we worked on in the previous section and name it ``simple_submit_workflow.py``. -To make the new script work asynchronously, simply change the following subset of lines: - -.. code:: python - - from aiida.engine import run - - [...] - - for label, factor in zip(labels, scale_factors): - - [...] - - calculations[label] = run(PwCalculation, **inputs) - - [...] - - inputs = {label: result['output_parameters'] for label, result in calculations.items()} - eos = create_eos_dictionary(**inputs) - - [...] - -replacing them with - -.. code:: python - - from aiida.engine import submit - from time import sleep - - [...] - - for label, factor in zip(labels, scale_factors): - [...] - - # Replace `run` with `submit` - calculations[label] = submit(PwCalculation, **inputs) - - [...] - - # Wait for the calculations to finish - for calculation in calculations.values(): - while not calculation.is_finished: - sleep(1) - - inputs = {label: node.get_outgoing().get_node_by_label('output_parameters') for label, node in calculations.items()} - eos = create_eos_dictionary(**inputs) - -The main differences are: - -- ``run`` is replaced by ``submit`` -- The return value of ``submit`` is not a dictionary describing the outputs of the calculation, but it is the node that represents the execution of that calculation. -- Each calculation starts in the background and calculation nodes are added to the ``calculations`` dictionary. -- At the end of the loop, when all calculations have been launched with ``submit``, another loop is used to wait for all calculations to finish before gathering the results as the final step. - -In the next section we will show you another way to achieve this, which has the added bonus that it introduces checkpoints in the work function, from which the process can be resumed should it be interrupted. -After applying the modifications, run the script. -You will see that all calculations start at the same time, without waiting for the previous ones to finish. - -If in the meantime you run ``verdi process status ``, all five calculations are already shown as output. -Also, if you run ``verdi process list``, you will see how the calculations are submitted to the scheduler. - -.. _workchainsimple: - -Workchains, or how not to get lost if your computer shuts down or crashes -------------------------------------------------------------------------- - -The simple workflows that we have used so far have been launched by a python script that needs to be running for the whole time of the execution, namely the time in which the calculations are submitted, and the actual time needed by Quantum ESPRESSO to perform the calculation and the time taken to retrieve the results. -If you had killed the main python process during this time, the workflow would not have terminated correctly. -Perhaps you killed the calculation and you experienced the unpleasant consequences: intermediate calculation results are potentially lost and it is extremely difficult to restart a workflow from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way to insert checkpoints, where the main code defining a workflow can be stopped (you can even shut down the machine on which AiiDA is running!). -We call these work functions with checkpoints 'work chains' because, as you will see, they basically amount to splitting a work function in a chain of steps. -Each step is then ran by the daemon, in a way similar to the remote calculations. - -Here below you can find the basic rules that allow you to convert your workfunction-based script to a workchain-based one and a snippet example focusing on the code used to perform the calculation of an equation of state. - -.. include:: ../scripts/equation_of_state.py - :code: python - -- Instead of using decorated functions you need to define a class, inheriting from a prototype class called ``WorkChain`` that is provided by AiiDA in the ``aiida.engine`` module. - -- Within your class you need to implement a ``define`` classmethod that always takes ``cls`` and ``spec`` as inputs. - Here you specify the main information on the workchain, in particular: - - - The *inputs* that the workchain expects. - This is obtained by means of the ``spec.input()`` method, which provides as the key feature the automatic validation of the input types via the ``valid_type`` argument. - The same holds true for outputs, as you can use the ``spec.output()`` method to state what output types are expected to be returned by the workchain. - - - The ``outline`` consisting in a list of 'steps' that you want to run, put in the right sequence. - This is obtained by means of the method ``spec.outline()`` which takes as input the steps. - *Note*: in this example we just split the main execution in two sequential steps, that is, first ``run_eos`` then ``results``. - However, more complex logic is allowed, as will be explained in :ref:`another section`. - -- You need to split your main code into methods, with the names you specified before into the outline (``run_eos`` and ``results`` in this example). - Where exactly should you split the code? - Well, the splitting points should be put where you would normally block the execution of the script for collecting results in a standard work function, namely whenever you call the method ``.result()``. - Each method should accept only one parameter, ``self``, e.g. ``def step_name(self)``. - -- You will notice that the methods reference the attribute ``ctx`` through ``self.ctx``, which is called the *context* and is inherited from the base class ``WorkChain``. - A python function or process function normally just stores variables in the local scope of the function. - For instance, in the example of :ref:`this subsection`, you stored the completed calculations in the ``calculations`` dictionary, that was a local variable. - - In work chains, instead, to preserve variables between different steps, you need to store them in a special dictionary called *context*. - As explained above, the context variable ``ctx`` is inherited from the base class ``WorkChain``, and at each step method you just need to update its content. - AiiDA will take care of saving the context somewhere between workflow steps (on disk, in the database, depending on how AiiDA was configured). - For your convenience, you can also access the value of a context variable as ``self.ctx.varname`` instead of ``self.ctx['varname']``. - -- Any submission within the workflow should not call the normal ``run`` or ``submit`` functions, but ``self.submit`` to which you have to pass the process class, and a dictionary of inputs. - -- The submission in ``run_eos`` returns a future and not the actual calculation, because at that point in time we have only just launched the calculation to the daemon and it is not yet completed. - Therefore it literally is a 'future' result. - Yet we still need to add these futures to the context, so that in the next step of the workchain, when the calculations are in fact completed, we can access them and continue the work. - To do this, we can use the ``ToContext`` class. - This class takes a dictionary, where the values are the futures and the keys will be the names under which the corresponding calculations will be made available in the context when they are done. - See how the ``ToContext`` object is created and returned in ``run_eos``. - By doing this, the workchain will implicitly wait for the results of all the futures you have specified, and then call the next step *only when all futures have completed*. - -- *Return values*: While in normal process functions you attach output nodes to the node by invoking the *return* statement, in a workchain you need to call ``self.out(link_name, node)`` for each node you want to return. - Of course, if you have already prepared a dictionary of outputs, you can just use the following syntax: - - .. code:: python - - self.out_many(retdict) # Keys are link names, value the nodes - - The advantage of this different syntax is that you can start emitting output nodes already in the middle of the execution, and not necessarily at the very end as it happens for normal functions (*return* is always the last instruction executed in a function or method). - Also, note that once you have called ``self.out(link_name, node)`` on a given ``link_name``, you can no longer call ``self.out()`` on the same ``link_name``: this will raise an exception. - -Finally, the workflow has to be run. -For this you have to use the function ``run`` passing as arguments the ``EquationOfState`` class and the inputs as key-value arguments. -For example, you can execute: - -.. code:: python - - from aiida.engine import run - run(EquationOfState, element=Str('Si'), code=load_code('qe-6.3-pw@localhost'), pseudo_family=Str('SSSP')) - -While the workflow is running, you can check (in a different terminal) what is happening to the calculations using ``verdi process list``. -You will see that after a few seconds the calculations are all submitted to the scheduler and can potentially run at the same time. - -.. warning:: - - The calculations launched by the work chain will now be submitted to the daemon, instead of run in the same interpreter - However, by using ``run`` on the work chain itself, it will still not be able to restart. - To make the work chain save its checkpoints, you should use the ``submit`` launcher instead. - -As an additional exercise (optional), instead of running the main workflow ``EquationOfState``, try to submit it. -Note that the file where the work chains is defined will need to be globally importable (so the daemon knows how to load it) and you need to launch it (with ``submit``) from a different python file. -The easiest way to achieve this is typically to embed the workflow inside a python package. - -.. note:: - - As good practice, you should try to keep the steps as short as possible in term of execution time. - The reason is that the daemon can be stopped and restarted only between execution steps and not if a step is in the middle of a long execution. - -Finally, as an optional exercise if you have time, you can jump to :ref:`this appendix`, which shows how to introduce more complex logic into your work chains (if conditionals, while loops etc.). -The exercise will show how to realize a convergence loop to obtain the minimum-volume structure in a EOS using the Newton's algorithm. - -.. note:: - - You might see warnings that say ``Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint``. - These are generated by the ``PwCalculation`` which is unable to save a checkpoint because it is not in a so called 'importable path'. - Simply put, this means that if AiiDA were to try and reload the class it wouldn't know which file to find it in. - To get around this you could simply put the workchain in a different file that is in the 'PYTHONPATH' and then launch it by importing it in your launch file. - In this way AiiDA knows where to find it next time it loads the checkpoint. - - -.. rubric:: Footnotes - -.. [#f1] In simple words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form ``@decorating_function_name`` on the line just before the ``def`` line of the decorated function. 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - -     Main attributes and methods -   - - - - - - - - - - - - - - - The - cheat sheet - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Note: each derived class inherits all the methods of the parent class -   - - - - Node - pklabeluuidctimemtimeget_incoming()get_outgoing()inputsoutputsattributesget_attribute(k)extrasget_extra(<k>)set_extra(<k>,<v>) get_comments()add_comment(<c>) store() Node IDShort labelUnique IDCreation timeModification timeGet inputGet outputAll inputs generatorAll outputs generatorQueryable attributesAttribute 'k'Queryable extrasExtra 'k'Set extra k = vAll commentsAdd comment with content <c>Save node in DB - - - - Code - load_code(<id>) get_builder()  Load code using pk, UUID, or labelReturn new builder using this code  - - - - Data - export()_exportcontent()importfile()importstring()  Export to fileExport to stringImport from fileImport from string - - - - - - Dict - dict.<k>keys()get_dict()set_dict(<dict>)  Get value for key 'k'Get all keys generatorGet all key/valuesReplace all key/values - - - - ArrayData - get_arraynames()get_array(<n>)set_array(<n>,<a>)   Names of all arraysGet array named 'n'Set/store array 'a' with name 'n' - - - - StructureData - cellsiteskinds pbc get_formula()get_cell_volume()convert(<fmt>) set_cell(<c>)set_ase(<a>)set_pymatgen(<p>) append_atom(symbols=<symb>,position=<p>) Lattice vectorsAtomic sitesSpecies with masses, symbols, ...Periodic bound. cond. along each axisChemical formulaCompute cell volumeConvert to ASE, pymatgen, ...Set lattice vectorsCreate cell from ASECreate cell from pymatgenAdd atom of type 'symb' at position 'p'   - - - - - - KpointsData - set_kpoints(<k>)  get_kpoints() set_kpoints_mesh( <m>)get_kpoints_mesh()   Set an explicit list of kpoints 'k' (optionally with weights)Get explicit list of kpts(if stored explicitly)Set an implicit mesh (e.g. 'm'=3x2x5)Get the implicit mesh(if stored implicitly)    - - - CalcJobNode - process_stateexit_statusis_finishedis_failedcomputer inputs.codeget_job_id()get_options() res.<k> Calc. process stateExit status or int codeHas calc. finished?Has calc. failed?Computer where it is runningCode used to runScheduler job IDGet # machines, MPI procs per machine, ...Value of parsed output 'k' v19.05 - - - WorkChainNode - get_state()computer inputs.codeget_job_id()get_options() res.<k> Calculation stateComputer where it is runningCode used to runScheduler job IDGet # machines, MPI procs per machine, ...Value of parsed output 'k' aiida-core v1.0.0b3 - - To load an existing node: load_node(<id>)Where <id> may be either the pk, UUID, or label To load a class, either import it from aiida.orm or use the DataFactory (returning Data subclasses) or the CalculationFactory (returning CalcJobNode subclasses) - - - - - Import aiida-core Node classes from aiida.orm using their class name: from aiida.orm import CalcJobNodefrom aiida.orm import Dict Import Data classes via the DataFactory using <label>s:: KpointsData = DataFactory("array.kpoints")MyData = DataFactory("plugin.my") Other <label>s for Data:"upf", "array", "array.bands", "dict", ... Import CalcJob classes via the CalculationFactory: PwCalculation = CalculationFactory("quantumespresso.pw") Other <label>s for Calculations:"quantumespresso.ph", "vasp.scf", ... Import WorkChain classes via the WorkflowFactory. ORM and the Factories - - - The main AiiDA Node subclasses - - Node - - - CalculationNode - - - Data - - - - - - - WorkFunctionNode - - - - CalcFunctionNode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... - ArrayData - ... - StructureData - UpfData - KpointsData - BandsData - TrajectoryData - WorkflowNode - - - - CalcJobNode - - - - - WorkChainNode - - - - - - - - - - - Code - - - - Dict, Float, Int, ... - - - ProcessNode - - - - - - - Importing classes - Useful links: Tutorial website:aiida-tutorials.readthedocs.io AiiDA documentation:aiida-core.readthedocs.io/en/latest - - - - - - - - Test1 - Test1 - Test1 - Test1 - Test1 - - diff --git a/docs/pages/2019_SINTEF/cheatsheet/required_fonts/HelveticaNeue-otf/HelveticaNeue-Black.otf b/docs/pages/2019_SINTEF/cheatsheet/required_fonts/HelveticaNeue-otf/HelveticaNeue-Black.otf deleted file mode 100644 index 981ea5a1..00000000 Binary files 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-+-----------------+--------------------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.4.1`_ | -+-----------------+--------------------------------------------------------------------------------+ - -.. _Quantum Mobile 19.09.0: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.09.0 -.. _aiida-core 1.0.0b6: https://pypi.org/project/aiida-core/1.0.0b6 -.. _aiida-quantumespresso 3.0.0a5: https://github.com/aiidateam/aiida-quantumespresso/releases/tag/v3.0.0a5 -.. _aiidalab 19.08.0a1: https://pypi.org/project/aiidalab/19.8.0a1 -.. _Quantum Espresso 6.4.1: https://github.com/QEF/q-e/releases/tag/qe-6.4.1 - -These are the hands-on materials for the AiiDA tutorial held at the `VASP and AiiDA workshop at SINTEF in Oslo, Norway `__ from September 23-27, 2019. - -While participants of the tutorial used virtual machines on a cloud service, you can follow the tutorial on your own computer using the Quantum Mobile VirtualBox image linked above. The image already contains all the required software. - -AiiDA hands-on materials -^^^^^^^^^^^^^^^^^^^^^^^^ - -Demo ----- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/setup - ./sections/first_taste - -In-depth tutorial ------------------ - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/verdi_cmdline - ./sections/verdi_shell - ./sections/calculations - ./sections/querybuilder - ./sections/workflows - -aiida-vasp tutorial -------------------- - -The materials for the aiida-vasp tutorial held on September 26-27 can be found in the `aiida-vasp documentation `_. - -Appendices ----------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/appendix_computer_code_setup - ./sections/appendix_structure_data - ./sections/appendix_upf_data - ./sections/appendix_input_validation - ./sections/appendix_restarting_calculations - ./sections/appendix_queries - ./sections/appendix_workflow_logic diff --git a/docs/pages/2019_SINTEF/notebooks/bandstructure.ipynb b/docs/pages/2019_SINTEF/notebooks/bandstructure.ipynb deleted file mode 100644 index c1e2dc00..00000000 --- a/docs/pages/2019_SINTEF/notebooks/bandstructure.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A real world workchain example: electronic band structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%aiida\n", - "\n", - "from datetime import datetime, timedelta\n", - "\n", - "from aiida.tools.dbimporters.plugins.cod import CodDbImporter\n", - "from aiida.engine import launch\n", - "\n", - "PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating the electronic band structure with an AiiDA workchain\n", - "This tutorial will show how useful a workchain can be in performing a well defined task, such as computing and visualizing the electronic band structure for a simple crystal structure. The goal of this tutorial is not to show you the intricacies of the actual workchain itself, but rather to serve as an example that workchains can simplify standard workflows in computational materials science. The workchain that we will use here will employ Quantum Espresso's pw.x code to calculate the charge densities for several crystal structures and compute a band structure from those. Many choices that normally face the researcher before being able to perform this calculation, such as the selection of suitable pseudo potentials, energy cutoff values, k-point grids and k-point paths along high symmetry points, are now performed automatically by the workchain. All that remains for the user to do is to simply define a structure, pass it to the workchain and sit back!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we import the crystal structure of Al as an example, and run the PwBandStructureWorkChain for that structure. The estimated run time is noted in a comment in the calculation cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading the COD importer so we can directly import structure from COD id's\n", - "importer = CodDbImporter()\n", - "# Make sure here to define the correct codename that corresponds to the pw.x code installed on your machine of choice\n", - "codename = 'qe-6.4.1-pw@localhost'\n", - "code = Code.get_from_string(codename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Importing example crystal structures from COD to AiiDA structure objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Al COD ID='9008460'\n", - "structure_Al = importer.query(id='9008460')[0].get_aiida_structure()\n", - "\n", - "structure_Al.get_formula()\n", - "\n", - "# The following structure can be used instead of Al, but will take much longer on the AWS machine.\n", - "# CaF2 COD ID='1000043' -- approximately 1/2 hour to run\n", - "# h-BN COD ID='9008997' -- approximately 45 mins to run\n", - "# GaAs COD ID='9008845' -- approximately 2 hours to run " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now we run the bandstructure workchain for the selected structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bandstructure workchain follows the following protocol:\n", - "* Determine the primitive cell of the input structure\n", - "* Run a vc-relax to relax the structure\n", - "* Refine the symmetry of the relaxed structure to ensure the primitive cell is used and run a self-consistent field calculation on it\n", - "* Run a non self-consistent field band structure calculation along a path of high symmetry k-points determined by [seekpath](http://materialscloud.org/tools/seekpath)\n", - "\n", - "Numerical parameters for the default 'theos-ht-1.0' protocol are determined as follows: \n", - "* Suitable pseudopotentials and energy cutoffs are automatically searched from the [SSSP library](http://materialscloud.org/sssp) installed on your machine (it uses the efficiency version 1.1).\n", - "* K-point mesh is selected to have a minimum k-point density of 0.2 Å-1\n", - "* A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations\n", - "\n", - "In case the pseudopotentials are not installed, they can be downloaded in a terminal as:\n", - "\n", - " wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz\n", - " tar -zxvf SSSP_efficiency_pseudos.tar.gz\n", - " \n", - "The protocol looks for a UPF file with a specific hash code, that is unique for each different file. \n", - "You can check that you have the right\n", - "one by performing a search in the database:\n", - "\n", - " qb=QueryBuilder()\n", - " qb.append(UpfData, filters={'attributes.md5':{'==':'cfc449ca30b5f3223ec38ddd88ac046d'}})\n", - " len(qb.all())\n", - "\n", - "'md5' is a searchable attribute of the pseudopotential data object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fermi_energy = results['scf_parameters'].dict.fermi_energy\n", - "results['band_structure'].show_mpl(y_origin=fermi_energy, plot_zero_axis=True)\n", - "\n", - "print(\"\"\"Final crystal symmetry: {spacegroup_international} (number {spacegroup_number})\n", - "Extended Bravais lattice symbol: {bravais_lattice_extended}\n", - "The system has inversion symmetry: {has_inversion_symmetry}\"\"\".format(\n", - " **results['seekpath_parameters'].get_dict()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to use a different pseudopotential family (or version of the family) (for instance [SSSP v1.0](https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz) instead of the default SSSP v1.1) you can pass an additional parameter when calling the WorkChain, as follows:\n", - " \n", - " protocol = Dict(dict={\n", - " 'name':'theos-ht-1.0', \n", - " 'modifiers': {\n", - " 'pseudo' : 'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - "\n", - "(note that only some values are accepted for pseudo, that you can find [here](https://github.com/aiidateam/aiida-quantumespresso/blob/b02250146576eb573ccb45d05047075f54853f9d/aiida_quantumespresso/utils/protocols/pw.py#L24))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al,\n", - " protocol=Dict(dict={\n", - " 'name':'theos-ht-1.0',\n", - " 'modifiers': {\n", - " 'pseudo':'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/pages/2019_SINTEF/notebooks/querybuilder-template.ipynb b/docs/pages/2019_SINTEF/notebooks/querybuilder-template.ipynb deleted file mode 100644 index 65dd70ae..00000000 --- a/docs/pages/2019_SINTEF/notebooks/querybuilder-template.ipynb +++ /dev/null @@ -1,973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AiiDA's QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from datetime import datetime, timedelta\n", - "import numpy as np\n", - "from aiida import load_profile\n", - "from matplotlib import gridspec, pyplot as plt\n", - "load_profile()\n", - "from aiida.orm import load_node, Node, Group, Computer, User, CalcJobNode, Code\n", - "from aiida.plugins import CalculationFactory, DataFactory\n", - "\n", - "PwCalculation = CalculationFactory('quantumespresso.pw')\n", - "StructureData = DataFactory('structure')\n", - "KpointsData = DataFactory('array.kpoints')\n", - "Dict = DataFactory('dict')\n", - "UpfData = DataFactory('upf')\n", - "\n", - "def plot_results(query_res):\n", - " \"\"\"\n", - " :param query_res: The result of an instance of the QueryBuilder\n", - " \"\"\"\n", - " smearing_unit_set,magnetization_unit_set,pseudo_family_set = set(), set(), set()\n", - " # Storing results:\n", - " results_dict = {}\n", - " for pseudo_family, formula, smearing, smearing_units, mag, mag_units in query_res:\n", - " if formula not in results_dict:\n", - " results_dict[formula] = {}\n", - " # Storing the results:\n", - " results_dict[formula][pseudo_family] = (smearing, mag)\n", - " # Adding to the unit set:\n", - " smearing_unit_set.add(smearing_units)\n", - " magnetization_unit_set.add(mag_units)\n", - " pseudo_family_set.add(pseudo_family)\n", - "\n", - " # Sorting by formula:\n", - " sorted_results = sorted(results_dict.items())\n", - " formula_list = next(zip(*sorted_results))\n", - " nr_of_results = len(formula_list)\n", - "\n", - " # Checks that I have not more than 3 pseudo families.\n", - " # If more are needed, define more colors\n", - " #pseudo_list = list(pseudo_family_set)\n", - " if len(pseudo_family_set) > 3:\n", - " raise Exception('I was expecting 3 or less pseudo families')\n", - "\n", - " colors = ['b', 'r', 'g']\n", - "\n", - " # Plotting:\n", - " plt.clf()\n", - " fig=plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None)\n", - " gs = gridspec.GridSpec(2,1, hspace=0.01, left=0.1, right=0.94)\n", - "\n", - " # Defining barwidth\n", - " barwidth = 1. / (len(pseudo_family_set)+1)\n", - " offset = [-0.5+(0.5+n)*barwidth for n in range(len(pseudo_family_set))]\n", - " # Axing labels with units:\n", - " yaxis = (\"Smearing energy [{}]\".format(smearing_unit_set.pop()),\n", - " \"Total magnetization [{}]\".format(magnetization_unit_set.pop()))\n", - " # If more than one unit was specified, I will exit:\n", - " if smearing_unit_set:\n", - " raise ValueError('Found different units for smearing')\n", - " if magnetization_unit_set:\n", - " raise ValueError('Found different units for magnetization')\n", - " \n", - " # Making two plots, the top one for the smearing, the bottom one for the magnetization\n", - " for index in range(2):\n", - " ax=fig.add_subplot(gs[index])\n", - " for i,pseudo_family in enumerate(pseudo_family_set):\n", - " X = np.arange(nr_of_results)+offset[i]\n", - " Y = np.array([thisres[1][pseudo_family][index] for thisres in sorted_results])\n", - " ax.bar(X, Y, width=0.2, facecolor=colors[i], edgecolor=colors[i], label=pseudo_family)\n", - " ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15*index+5)\n", - " ax.set_xlim(-0.5, nr_of_results-0.5)\n", - " ax.set_xticks(np.arange(nr_of_results))\n", - " if index == 0:\n", - " plt.setp(ax.get_yticklabels()[0], visible=False)\n", - " ax.xaxis.tick_top()\n", - " ax.legend(loc=3, prop={'size': 18})\n", - " else:\n", - " plt.setp(ax.get_yticklabels()[-1], visible=False)\n", - " for i in range(0, nr_of_results, 2):\n", - " ax.axvspan(i-0.5, i+0.5, facecolor='y', alpha=0.2)\n", - " ax.set_xticklabels(list(formula_list),rotation=90, size=14, ha='center')\n", - " plt.show()\n", - "\n", - "def generate_query_graph(qh, out_file_name):\n", - "\n", - " def draw_vertice_settings(idx, vertice, **kwargs):\n", - " \"\"\"\n", - " Returns a string with all infos needed in a .dot file to define a node of a graph.\n", - " :param node:\n", - " :param kwargs: Additional key-value pairs to be added to the returned string\n", - " :return: a string\n", - " \"\"\"\n", - " if vertice['entity_type'].startswith('process'):\n", - " shape = \"shape=polygon,sides=4\"\n", - " elif vertice['entity_type'].startswith('data.code'):\n", - " shape = \"shape=diamond\"\n", - " else:\n", - " shape = \"shape=ellipse\"\n", - " filters = kwargs.pop('filters', None)\n", - " additional_string = \"\"\n", - " if filters:\n", - " additional_string += '\\nFilters:'\n", - " for k,v in filters.items():\n", - " additional_string += \"\\n {} : {}\".format(k,v)\n", - "\n", - "\n", - " label_string = \" ('{}')\".format(vertice['tag'])\n", - "\n", - " labelstring = 'label=\"{} {}{}\"'.format(\n", - " vertice['entity_type'], #.split('.')[-2] or 'Node',\n", - " label_string,\n", - " additional_string)\n", - " #~ return \"N{} [{},{}{}];\".format(idx, shape, labelstring,\n", - " return \"{} [{},{}];\".format(vertice['tag'], shape, labelstring)\n", - " nodes = {v['tag']:draw_vertice_settings(idx, v, filters=qh['filters'][v['tag']]) for idx, v in enumerate(qh['path'])}\n", - " links = [(v['tag'], v['joining_value'], v['joining_keyword']) for v in qh['path'][1:]]\n", - "\n", - " with open('temp.dot','w') as fout:\n", - " fout.write(\"digraph G {\\n\")\n", - " for l in links:\n", - " fout.write(' {} -> {} [label=\" {}\"];\\n'.format(*l))\n", - " for _, n_values in nodes.items():\n", - " fout.write(\" {}\\n\".format(n_values))\n", - "\n", - " fout.write(\"}\\n\")\n", - " import os\n", - " os.system('dot temp.dot -Tpng -o {}'.format(out_file_name))\n", - "\n", - "def store_formula_in_extra():\n", - " from aiida.orm import QueryBuilder\n", - " qb = QueryBuilder()\n", - " qb.append(StructureData, filters={'extras':{'!has_key':'formula'}})\n", - " for structure, in qb.all():\n", - " structure.set_extra('formula', structure.get_formula(mode='count'))\n", - "\n", - "store_formula_in_extra()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction to the QueryBuilder\n", - "As you will use `AiiDA` to run your calculations, the database that stores all the data and the provenance will quickly grow to be very large. To help you find the needle that you might be looking for in this big haystack, we need an efficient search tool. `AiiDA` provides a tool to do exactly this: the `QueryBuilder`. The `QueryBuilder` acts as the gatekeeper to your database, to whom you can ask questions about the contents of your database (also referred to as queries), by specifying what are looking for. In this part of the tutorial, we will focus on how to use the `QueryBuilder` to make these queries and understand/use the results.\n", - "\n", - "In order to use the `QueryBuilder`, we first need to import it. We can accomplish this by executing the `import` statement in the following cell. Go ahead and select the next cell, and press `Shift+Enter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida.orm import QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we can ask the `QueryBuilder` questions about our database, we first need to create an instance of it (i.e., we create a new query):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an instance of our `QueryBuilder` which we named `qb`, we are ready to start asking about the content of our database. For example, we may want to know exactly how many nodes there are in our database. To tell `qb` that we are interested in all the occurrences of the `Node` class in our database, we `append` it to the list of objects it should find. \n", - "\n", - "*Note*: The method is called `append` because, as we will see later, you can append multiple nodes to a `QueryBuilder` instance consecutively to search in the graph, as if you had a list: what we are doing is querying a graph, and for every vertice of the graph in our subquery, we will use one `append` call. But we'll see this use better in a few steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(Node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now narrowed down the scope of `qb` to just the nodes that are present in our database. To learn how many nodes there are exactly, all we have to do is to ask `qb` to count them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now as you may have learned in previous sections of the tutorial, nodes come in different kinds and flavors. For example, all our crystal structures that we have stored in the database are saved in nodes that are of the type `StructureData`. If instead of all the nodes, we would rather like to count only the crystal structure nodes, we simply tell our `QueryBuilder` to narrow its scope only to objects of type `StructureData`. Since we want to create a new independent query, we must create a new instance of the `QueryBuilder`. In the next cell, we have typed part of the code to count all the structure nodes. See if you can finish the line with the comment, to tell the `QueryBuilder` that you are only interested in `StructureData` nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "qb.append() # How do we finish this line to tell the query builder to count only the structure nodes?\n", - "qb.count()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.count()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of just counting how many structure nodes we have, we may also actually want to see some of them. This is as easy as telling our `QueryBuilder` that we are not interested in the `count` but rather that we want to retrieve `all` the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this command is very literal and does in fact retrieve **all** the structure nodes that are stored in your database, which may be very slow if your database becomes very large. One solution is to simply tell the `QueryBuilder` that we are, for example, only interested in 5 structure nodes. This can be done with the `limit` method as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to simply use the concept of array slicing, native to python, to specify a subset of the total return set to be returned. Notice that this example can be very slow in big databases. When you want performance, use the functionality native to the QueryBuilder like `limit`, that limit the number of results directly at the database level!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(None)\n", - "qb.all()[:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to know a little bit more about the retrieved structure nodes, we can loop through our results. This allows you, for instance, to print the formula of the structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "for structure, in qb.all():\n", - " print(structure.get_formula())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just a simple example how we can employ the `QueryBuilder` to get details about the contents of our database. We have now seen simple queries for the `Node` and `StructureData` classes, but the same rules apply to all the `AiiDA` node classes. For example we may want to count the number of entries for each of the node classes in the following list:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class_list = [Node, StructureData, KpointsData, Dict, UpfData, Code]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the tools we have learned so far, we can build a table of the number of occurrences of each of these node classes that are stored in our database. We simply loop over the `class_list` and create a `QueryBuilder` for each and count the entries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for class_name in class_list:\n", - " qb = QueryBuilder()\n", - " qb.append(class_name)\n", - "#TUT_USER_START\n", - " print() # Finish this line to print the results!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - " print('{:>15} | {:6}'.format(class_name.__name__, qb.count()))\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, you should see something like the following, where of course the numbers may differ for your database\n", - "\n", - "Class name | Entries\n", - "---------------|--------\n", - " Node | 10273 \n", - " StructureData | 271 \n", - " KpointsData | 953 \n", - " Dict | 2922 \n", - " UpfData | 85 \n", - " Code | 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Projection and filters\n", - "Up until now we have always asked the `QueryBuilder` to return the node class. However, we might not necessarily be interested in all the node's properties, but rather just a selected set or even just a single property. We can tell the `QueryBuilder` which properties we would like to be returned, by asking it to **project** those properties in the result. For example, we may only want to get the `uuid`s of a set of nodes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['uuid'])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By using the `project` keyword in the `append` call, we are informing the `QueryBuilder` that we are only interested in the `uuid` property of the `Node` class. Note that the value that we assign to `project` is a list, since we may want to specify more than one property. See if you can get the `QueryBuilder` to return *both* the `id` and the `uuid` of the first 5 `Node`s in the following cell.\n", - "\n", - "**Note**: in the context of the QueryBuilder, the `id` is the `pk` of the node." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_USER_START\n", - "qb.append(Node, project=)#? What should the value be for the project key\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Node, project=['id', 'uuid'])\n", - "#TUT_SOLUTION_END\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To give you an idea of the various properties that you can project for some of the `AiiDA` node classes you can consult the following table.\n", - "Note that this is by no means an exhaustive list:\n", - "\n", - "Class | Properties\n", - "---------|-----------\n", - "Node | `id`, `uuid`, `node_type`, `label`, `description`, `ctime`, `mtime`\n", - "Computer | `id`, `uuid`, `name`, `hostname`, `description`, `transport_type`, `scheduler_type`\n", - "User | `id`, `email`, `first_name`, `last_name`, `institution`\n", - "Group | `id`, `uuid`, `label`, `type_string`, `time`, `description`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same properties can also be used to *filter* for specific nodes in your database. Indeed, up until now, we only asked the `QueryBuilder` to return *all* the instances of a certain type of node, or at best a limited number of those (without specifying which ones). But in general we might be interested in a very specific node. For example, we may have the `id` of a certain node and we would like to know when it was created and last modified. We can tell the `QueryBuilder` to select nodes that only match that criterion, by telling it to **filter** based on that property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['ctime', 'mtime'], filters={'id': {'==': 1}})\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the syntax of the `filters` keyword. The value is a dictionary, where the keys indicate the node property that it is supposed to operate on, in this case the `id` property. The value of that key is again itself a dictionary, where the key indicates the logical operator `==` and the value corresponds to the value of the property.\n", - "\n", - "You may have multiple criteria that you want to filter for, in which case you can use the logical `or` and `and` operators. Let's say, for example, that you want the `QueryBuilder` to retrieve all the `StructureData` nodes that have a certain `label` **and** were created no longer than 12 days ago. You can translate this criterion by making use of the `and` operator which allows you to specify multiple filters that all have to be satisfied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(\n", - " Node, \n", - " filters={\n", - " 'and': [\n", - " {'ctime': {'>': datetime.now() - timedelta(days=12)}},\n", - " {'label': {'==':'graphene'}}\n", - " ]\n", - " }\n", - ")\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will have noticed that the `>` operator, and its related operators, can work with python `datetime` objects. These are just a few of the operators that `QueryBuilder` understands. Below you find a table with some of the logical operators that you can use:\n", - "\n", - "Operator | Data type | Example | Description\n", - "---------------------|-----------------------|------------------------------------|------------------\n", - "`==` | all | `{'==': '12'}` | equality operator\n", - "`in` | all | `{'in':['FINISHED', 'PARSING']}` | member of a set\n", - "`<`, `>`, `<=`, `>=` | float, int, datetime | `{'>': 5.2}` | size comparison operator\n", - "`like` | char, str | `{'like': 'calculation%'}` | string comparison, `%` is wildcard\n", - "`ilike` | char, str | `{'ilike': 'caLCulAtion%'}` | string comparison, capital insensitive\n", - "`or` | | `{'or': [{'<': 5.3}, {'>': 6.3}]}` | logical or operator\n", - "`and` | | `{'and': [{'>=': 2}, {'<=': 6}]}` | logical and operator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, try to write a query below that will retrieve all `Group` nodes whose `label` property starts with the string `tutorial`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your query here\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'tutorial%'}})\n", - "qb.limit(5)\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining relationships between query clauses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how we can use the `QueryBuilder` to search the database for entries of a specific node type, potentially projecting only specific properties and filtering for certain property values. However, our nodes do not live in a vacuum, but they are part of a graph and thus are linked one another. Therefore, we typically want to be able to search for nodes based on a certain relationship that they might have with other nodes. Consider for example that you have a `StructureData` node that was produced by some calculation. How would we be able to retrieve that calculation?\n", - "\n", - "To accomplish this, we need to be able to tell the `QueryBuilder` what the relationship is between the nodes that we are interested in. With the `QueryBuilder`, we can do the following to find all the structure nodes that have been created as an output by a `PwCalculation` process.\n", - "\n", - "**IMPORTANT NOTE**: In the graph, we are not looking for a `PwCalculation` process (since processes do not live in the graph, as you have learnt). We are actually looking for a `CalcJobNode` whose `process_type` column indicates that it was run by a `PwCalculation` process. Since this is a very common pattern, the `QueryBuilder` allows to directly append the `PwCalculation` process class as a shortcut, but it internally unwraps this into a query for a `CalcJobNode` with the appropriate filter on the `process_type`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are looking for pairs of nodes, we need to `append` the second node as well to the `QueryBuilder` instance. In the next line, to specify the relationship between the nodes, we need to be able to reference back to the `CalcJobNode` that is matched by the previous clause: therefore, we give it a `tag` with the `tag` keyword." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(StructureData, with_incoming='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal was to find `StructureData` nodes, so we `append` that to the `qb`. However, we didn't want to find just any `StructureData` nodes; they had to be an output of `PwCalculation`. \n", - "\n", - "Note how we expressed this relation by the `with_incoming` keyword, because we want a `StructureData` node having an *incoming* link from the `CalcJobNode` referenced by the `calculation` tag (i.e., the `StructureData` must be an *output* of the calculation).\n", - "\n", - "Now all we have to do is execute the query to retrieve our structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we did can be visualized schematically, thanks to a little tool we have written for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query1.png')\n", - "Image(filename='query1.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `with_incoming` keyword is only one of many potential relationships that exist between the various `AiiDA` nodes and that are implemented in the `QueryBuilder`. The table below gives an overview of the implemented relationships, which nodes they are defined for and what relation it implicates. The full documentation can be found [on this page of the AiiDA documentation](https://aiida-core.readthedocs.io/en/latest/querying/querybuilder/append.html#joining-entities)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entity from\t| Entity to\t| Relationship | Explanation\n", - "------------|-----------|------------------|------------\n", - "Node | Node | with_outgoing | One node as input of another node\n", - "Node | Node | with_incoming | One node as output of another node\n", - "Node | Node | with_descendants | One node as the ancestor of another node\n", - "Node | Node | with_ancestors | One node as descendant of another node\n", - "Group | Node | with_node | The group of a node\n", - "Node | Group | with_group | The node is a member of a group\n", - "Computer | Node | with_node | The computer of a node\n", - "Node | Computer | with_computer | The node of a computer\n", - "User | Node | with_node | The creator of a node is a user\n", - "Node | User | with_user | The node was created by a user" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, see if you can write a query that will return all the `UpfData` nodes that are a member of a `Group` whose name starts with the string `SSSP`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'SSSP%'}}, tag='group')\n", - "qb.append(UpfData, with_group='group')\n", - "qb.all()\n", - "#TUT_SOLUTION_END\n", - "# Here I also visualize what's going on:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query2.png')\n", - "Image(filename='query2.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Attributes and extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In section 2, we showed you how you can `project` specific properties of a `Node` and gave a list of properties that a `Node` instance possesses. Since then, we have come across a lot of different `AiiDA` data nodes, such as `StructureData` and `UpfData`, that were secretly `Node`'s in disguise. Or to put it correctly, as `AiiDA` employs the object-oriented programming paradigm, both `StructureData` and `UpfData` are examples of subclasses of the `Node` class and therefore inherit its properties. That means that whatever property a `Node` has, both `StructureData` and `UpfData` will have too. However, there is a semantic difference between a `StructureData` node and a `UpfData`, and so we may want to add a property to one that would not make sense for the other. To solve this, `AiiDA` introduces the concept of `attributes`. These are similar to properties, except that they are specific to the `Node` type that they are attached to. This allows you to add an `attribute` to a certain node, without having to change the implementation of all the others.\n", - "\n", - "For example, the `Dict` nodes that are generated as output of `PwCalculation`'s may have an attribute named `wfc_cutoff`. To project for this particular `attribute`, one can use exactly the same syntax as shown in section 2 for the regular `Node` properties, and one has to only prepend `attributes.` to the attribute name. Demonstration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='pw')\n", - "qb.append(Dict, with_incoming='pw', project=[\"attributes.wfc_cutoff\"])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not every `Dict` node has to have this attribute, in which case the `QueryBuilder` will return the python keyword `None`. Similar to the `attributes`, nodes also can have `extras`, which work in the same way, except that `extras` are mutable, which means that their value can be changed even after a node instance has been stored.\n", - "\n", - "If you are not sure which attributes a given node has, you can use the `.attributes` property to simply retrieve them all. It will return a dictionary with all the attributes the node has.\n", - "\n", - "Note that a node also has a number of additional methods. For instance, you can do `list(node.attributes_keys())` to get only the attribute keys, or `node.get_attribute('wfc_cutoff')` to get the value of a single attribute (these two variants are more efficient if the node has a lot of attributes and you don't need all data). Similarly, for extras, you have `node.extras`, `list(node.extras_keys())`, and `node.get_attribute('SOME_EXTRA_KEY')`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation)\n", - "node, = qb.first()\n", - "node.attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chemical-element symbol of a pseudopotential, that is represented by a `UpfData` node, is stored in the `element` attribute. Using the knowledge that filtering on attributes works exactly as for normal node properties, see if you can write a query that will search your database for pseudopotentials for silicon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_SOLUTION_START\n", - "qb.append(UpfData, filters={'attributes.element': {'==': 'Si'}})\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**For more exercises on relationships and attributes/extras, have a look at the appendix section on queries.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 5. A small high-throughput study" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The following section assumes that a specific dataset is present in your current AiiDA profile. If you are not running this script on the Virtual Machine of the AiiDA tutorial, this script will not produce the desired output.\n", - "You can download the Virtual Machine image from [https://aiida-tutorials.readthedocs.io](https://aiida-tutorials.readthedocs.io) along with the tutorial text (choose the correct version of the tutorial, depending on which version of AiiDA you want to try).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part of the tutorial, we will focus on how to systematically retrieve, parse and analyze the results of\n", - "multiple calculations using AiiDA. We know you’re able to do this yourself, but to save time, a set of calculations\n", - "have already been done with AiiDA for you on 57 perovskites, using three different pseudopotential families (LDA,\n", - "PBE and PBESOL, all from GBRV 1.2).\n", - "\n", - "**Note**: if you are not following the tutorial on the official virtual machine that comes with this data, but you are working with your own database, you first have to import this archive (download it from: https://drive.google.com/open?id=1eqJaJr7q1VsYYvGxqHxmDN7gBMs534rz) using `verdi import`\n", - "\n", - "These calculations are spin-polarized (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. The idea of this part of the tutorial is to “screen” for magnetic and metallic perovskites in a “high-throughput” way. As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. Once more check the list of groups in your database by typing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!verdi group list -A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The calculations needed for this task were put in three different groups whose names start with \"tutorial\" (one for each pseudopotential family). The main task is to make a plot showing, for all perovskites and for each pseudopotential family, the total magnetization and the $-TS$ contribution from the smearing to the total energy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start building the query\n", - "So we first of all need to instantiate a QueryBuilder instance. We `append` the groups of interest, which means that we select only groups that start with the string `tutorial_`. We can execute the query after this append (this will not affect the final results) and check whether we have retrieved 3 groups." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiating QB:\n", - "qb = QueryBuilder()\n", - "# Appending the groups I care about:\n", - "qb.append(Group, filters={'label':{'like':'tutorial_%'}}, project='label', tag='group')\n", - "# Visualize:\n", - "print(\"Groups:\", ', '.join([g for g, in qb.all()]))\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query3.png')\n", - "Image(filename='query3.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the calculations that are members of each group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# I want every PwCalculation that is a member of the specified groups:\n", - "#TUT_USER_START\n", - "qb.append(PwCalculation, tag='calculation', with_group=) # Complete the function call with the correct relationship-tag!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(PwCalculation, tag='calculation', with_group='group')\n", - "#TUT_SOLUTION_END\n", - "#Visualize\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query4.png') \n", - "Image(filename='query4.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the structures that are input of the calculation. \n", - "We extend the query to include the structures that are input of the calculations that match the query so far.\n", - "This means that we `append` StructureData, and defining the relationship with the calculation with corresponding keyword.\n", - "\n", - "For simplicity the formulas have been added in the extras of each structure node under the key `formula`. \n", - "(The function that does this is called `store_formula_in_extra` and can be found in the top cell of this notebook. It also uses the QueryBuilder!)\n", - "\n", - "Project the formula, stored in the extras under the key `formula`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(StructureData, project=, tag='structure', with_outgoing=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(StructureData, project=['extras.formula'], tag='structure', with_outgoing='calculation')\n", - "#TUT_SOLUTION_END\n", - "# Visualize:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query5.png')\n", - "Image(filename='query5.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the output of the calculation\n", - "Every successful PwCalculation outputs a `Dict` node that stores the parsed results as key-value pairs. You can find these pairs among the attributes of the `Dict` node. To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. For convenience, the units are also added as key/value pairs (with the same key name, but with `_units` appended). \n", - "Extend the query so that also the output `Dict` of each calculation is returned. Project only\n", - "the attributes relevant to your analysis.\n", - "\n", - "In particular, project (in this order):\n", - "\n", - "* The smearing contribution \n", - "* The units of the smearing contribution\n", - "* The magnetization \n", - "* The units of the magnetization\n", - "\n", - "(to know the projection keys, you can try to load one `CalcJobNode` from one of the groups, get its output `Dict` and inspect its `attributes` as discussed before, to see the key-value pairs that have been parsed)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(Dict, tag='results', project=['attributes.energy_smearing', ...], with_incoming=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Dict, tag='results',\n", - " project=['attributes.energy_smearing', 'attributes.energy_smearing_units',\n", - " 'attributes.total_magnetization', 'attributes.total_magnetization_units',\n", - " ], with_incoming='calculation'\n", - " )\n", - "#TUT_SOLUTION_END\n", - "\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query6.png') \n", - "Image(filename='query6.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print the query results\n", - "We can print the results to see if everything worked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = qb.all()\n", - "for item in results:\n", - " print(', '.join(map(str, item)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results\n", - "Getting a long list is not always helpful, and a graph can be much more clear and useful. To help you, we have already prepared a function that visualizes the results of the query. Run the following cell and, if you did everything correctly, you should get a graph with the results of your queries!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_results(results)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/docs/pages/2019_SINTEF/scripts/__init__.py b/docs/pages/2019_SINTEF/scripts/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_SINTEF/scripts/common_wf.py b/docs/pages/2019_SINTEF/scripts/common_wf.py deleted file mode 100644 index 4a75d0ee..00000000 --- a/docs/pages/2019_SINTEF/scripts/common_wf.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper functions.""" -from __future__ import absolute_import -import numpy as np -from aiida.plugins import CalculationFactory, DataFactory -from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def generate_scf_input_params(structure, code, pseudo_family): - """Construct a builder for the `PwCalculation` class and populate its inputs. - - :return: `ProcessBuilder` instance for `PwCalculation` with preset inputs - """ - parameters = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, # Important that this stays to get stress - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - - kpoints = KpointsData() - kpoints.set_kpoints_mesh([2, 2, 2]) - - builder = PwCalculation.get_builder() - builder.code = code - builder.structure = structure - builder.kpoints = kpoints - builder.parameters = Dict(dict=parameters) - builder.pseudos = validate_and_prepare_pseudos_inputs( - structure, pseudo_family=pseudo_family) - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calculation of Silicon with Quantum ESPRESSO" - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - - return builder - - -def birch_murnaghan(V, E0, V0, B0, B01): - """Compute energy by Birch Murnaghan formula.""" - r = (V0 / V)**(2. / 3.) - return E0 + 9. / 16. * B0 * V0 * (r - 1.)**2 * (2. + (B01 - 4.) * (r - 1.)) - - -def fit_birch_murnaghan_params(volumes_, energies_): - """Fit Birch Murnaghan parameters.""" - from scipy.optimize import curve_fit - - volumes = np.array(volumes_) - energies = np.array(energies_) - params, covariance = curve_fit( - birch_murnaghan, - xdata=volumes, - ydata=energies, - p0=( - energies.min(), # E0 - volumes.mean(), # V0 - 0.1, # B0 - 3., # B01 - ), - sigma=None) - return params, covariance - - -def plot_eos(eos_pk): - """ - Plots equation of state taking as input the pk of the ProcessCalculation - printed at the beginning of the execution of run_eos_wf - """ - import pylab as pl - from aiida.orm import load_node - eos_calc = load_node(eos_pk) - - data = [] - for V, E, units in eos_calc.outputs.eos.dict.eos: - data.append((V, E)) - - data = np.array(data) - params, _covariance = fit_birch_murnaghan_params(data[:, 0], data[:, 1]) - - vmin = data[:, 0].min() - vmax = data[:, 0].max() - vrange = np.linspace(vmin, vmax, 300) - - pl.plot(data[:, 0], data[:, 1], 'o') - pl.plot(vrange, birch_murnaghan(vrange, *params)) - - pl.xlabel("Volume (ang^3)") - # I take the last value in the list of units assuming units do not change - pl.ylabel("Energy ({})".format(units)) # pylint: disable=undefined-loop-variable - pl.show() diff --git a/docs/pages/2019_SINTEF/scripts/create_rescale.py b/docs/pages/2019_SINTEF/scripts/create_rescale.py deleted file mode 100644 index 508df377..00000000 --- a/docs/pages/2019_SINTEF/scripts/create_rescale.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper CalcFunctions for equation of state.""" -from __future__ import absolute_import -from aiida.engine import calcfunction - - -@calcfunction -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - symbol = element.value - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure - - -@calcfunction -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_SINTEF/scripts/equation_of_state.py b/docs/pages/2019_SINTEF/scripts/equation_of_state.py deleted file mode 100644 index 703dc7e2..00000000 --- a/docs/pages/2019_SINTEF/scripts/equation_of_state.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -"""Equation of State WorkChain.""" -from __future__ import absolute_import -from six.moves import zip - -from aiida.engine import WorkChain, ToContext, calcfunction -from aiida.orm import Code, Dict, Float, Str, StructureData -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -PwCalculation = CalculationFactory('quantumespresso.pw') -scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) -labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - -@calcfunction -def get_eos_data(**kwargs): - """Store EOS data in Dict node.""" - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -class EquationOfState(WorkChain): - """WorkChain to compute Equation of State using Quantum Espresso.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super(EquationOfState, cls).define(spec) - spec.input('code', valid_type=Code) - spec.input('element', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.output('initial_structure', valid_type=StructureData) - spec.output('eos', valid_type=Dict) - spec.outline( - cls.run_eos, - cls.results, - ) - - def run_eos(self): - """Run calculations for equation of state.""" - # Create basic structure and attach it as an output - initial_structure = create_diamond_fcc(self.inputs.element) - self.out('initial_structure', initial_structure) - - calculations = {} - - for label, factor in zip(labels, scale_facs): - - structure = rescale(initial_structure, Float(factor)) - inputs = generate_scf_input_params(structure, self.inputs.code, - self.inputs.pseudo_family) - - self.report( - 'Running an SCF calculation for {} with scale factor {}'. - format(self.inputs.element, factor)) - future = self.submit(PwCalculation, **inputs) - calculations[label] = future - - # Ask the workflow to continue when the results are ready and store them in the context - return ToContext(**calculations) - - def results(self): - """Process results.""" - inputs = { - label: self.ctx[label].get_outgoing().get_node_by_label( - 'output_parameters') - for label in labels - } - eos = get_eos_data(**inputs) - - # Attach Equation of State results as output node to be able to plot the EOS later - self.out('eos', eos) diff --git a/docs/pages/2019_SINTEF/scripts/plot_calculation_results.py b/docs/pages/2019_SINTEF/scripts/plot_calculation_results.py deleted file mode 100644 index 628f8a7f..00000000 --- a/docs/pages/2019_SINTEF/scripts/plot_calculation_results.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot results of your calculations.""" -from __future__ import absolute_import -from __future__ import print_function -import sys -from argparse import ArgumentParser -from six.moves import map, range, zip - -import numpy as np -from matplotlib import gridspec, pyplot as plt - - -def plot_results(inputfname, outputfname=None, plotformat=None): # pylint: disable=too-many-locals,too-many-statements,too-many-branches - """ - :param inputfile: the filename of the inputfile - :param (opt) outputfile: - The (optional) name for the outputfile. - :param (opt) format: - The (optional) file format, if not clear from the input file name - - Reads the input file provided by the user. - The format should be comma separated values: - formula, pseudo_family, smearing, smearing_units, magnetization, magnetization_units - - - This order has to be respected! - - If an outputfile is provided, use matplotlib.pyplot.savefig to save the figure - Else, calls matplotlib.pyplot.show to show in screen - """ - - # Reading the input file given to me: - with open(inputfname) as f: - lines = f.readlines() - - # Definining the variables I need to read the units and pseudo families - smearing_unit_set = set() - magnetization_unit_set = set() - pseudo_family_set = set() - - # Storing results: - results_dict = {} - - # Looping through results: - for line in lines: - try: - (formula, pseudo_family, smearing, smearing_units, magnetization, - magnetization_units) = [i.strip() for i in line.split(',')] - smearing, magnetization = list( - map(float, (smearing, magnetization))) - except Exception as e: # pylint: disable=broad-except - print("There was an {} reading line:\n" - "{}" - "Exception: {}\n" - "Skipping this line\n".format(type(e), line, e)) - continue - - # If this is a new formula, not present in results, - # Creating the key-value pair: - if formula not in results_dict: - results_dict[formula] = {} - - # Storing the results: - results_dict[formula][pseudo_family] = (smearing, magnetization) - - # Adding to the unit set: - smearing_unit_set.add(smearing_units) - magnetization_unit_set.add(magnetization_units) - pseudo_family_set.add(pseudo_family) - - # Sorting by formula: - sorted_results = sorted(results_dict.items()) - formula_list = list(zip(*sorted_results))[0] - nr_of_results = len(formula_list) - - # Checks that I have not more than 3 pseudo families. - # If more are needed, define more colors - - if len(pseudo_family_set) > 3: - raise Exception('I was expecting 3 or less pseudo families') - - colors = ['b', 'r', 'g'] - - # Plotting: - gs = gridspec.GridSpec(2, 1, hspace=0.01, left=0.1, right=0.94) - fig = plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None) - - # Defining barwidth - barwidth = 1. / (len(pseudo_family_set) + 1) - offset = [ - -0.5 + (0.5 + n) * barwidth for n in range(len(pseudo_family_set)) - ] - - # Axing labels with units: - yaxis = ("Smearing energy [{}]".format(smearing_unit_set.pop()), - "Total magnetization [{}]".format(magnetization_unit_set.pop())) - # If more than one unit was specified, I will exit: - if smearing_unit_set: - raise Exception('Found different units for smearing') - if magnetization_unit_set: - raise Exception('Found different units for magnetization') - - # Making two plots, one for the smearing, the lower for magnetization - for index in range(2): - ax = fig.add_subplot(gs[index]) - for i, pseudo_family in enumerate(pseudo_family_set): - X = np.arange(nr_of_results) + offset[i] - Y = np.array([ - thisres[1][pseudo_family][index] for thisres in sorted_results - ]) - - ax.bar(X, - Y, - width=0.2, - facecolor=colors[i], - edgecolor=colors[i], - label=pseudo_family) - ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15 * index + 5) - ax.set_xlim(-0.5, nr_of_results - 0.5) - ax.set_xticks(np.arange(nr_of_results)) - - if index: - plt.setp(ax.get_yticklabels()[-1], visible=False) - else: - plt.setp(ax.get_yticklabels()[0], visible=False) - ax.xaxis.tick_top() - ax.legend(loc=3, prop={'size': 18}) - - for i in range(0, nr_of_results, 2): - ax.axvspan(i - 0.5, i + 0.5, facecolor='y', alpha=0.2) - ax.set_xticklabels(list(formula_list), - rotation=90, - size=14, - ha='center') - - if outputfname is not None: - plt.savefig(outputfname, format=plotformat) - else: - plt.show() - - -if __name__ == '__main__': - parser = ArgumentParser() - parser.add_argument('inputfile', - help='The input file with the data to plot') - parser.add_argument('-o', - '--outputfile', - help='The outputfile to plot results to', - default=None) - parser.add_argument( - '-f', - '--format', - default=None, - help= - 'The format to plot, if outputfile is specified and no valid extension is provided' - ) - parsed_args = parser.parse_args(sys.argv[1:]) - plot_results(inputfname=parsed_args.inputfile, - outputfname=parsed_args.outputfile, - plotformat=parsed_args.format) diff --git a/docs/pages/2019_SINTEF/scripts/plot_convergence_pressure.py b/docs/pages/2019_SINTEF/scripts/plot_convergence_pressure.py deleted file mode 100644 index 725133d3..00000000 --- a/docs/pages/2019_SINTEF/scripts/plot_convergence_pressure.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot parabola using matplotlib.""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import range -from aiida.orm import load_node - - -def parabola(x, a, b, c): - """Parabola.""" - return a * x**2 + b * x + c - - -def show_parabola_results(pk): # pylint: disable=too-many-locals - """Plot (and show) parabola using matplotlib.""" - out_p = load_node(pk).outputs.steps.get_dict() - - step0 = out_p['step0'] - steps = out_p['steps'] - - import numpy as np - import pylab as pl - - data = [] - data.append((step0['V'], step0['E'])) - for step in steps: - data.append((step['V'], step['E'])) - - data = np.array(data) - min_V = data[:, 0].min() - max_V = data[:, 0].max() - - min_E = data[:, 1].min() - max_E = data[:, 1].max() - - min_V -= 10. - max_V += 10. - min_E -= 0.1 - max_E += 0.5 - - V_range = np.linspace(min_V, max_V, 500) - - for subplot in range(2): - pl.subplot(1, 2, 1 + subplot) - - pl.plot(data[:1, 0], data[:1, 1], 'o', color='red') - pl.annotate('0', xy=(data[0, 0], data[0, 1])) - - pl.plot(data[1:, 0], data[1:, 1], 'o', color='blue') - - color = 0.8 - for idx, step in enumerate(steps, start=1): - pl.plot(V_range, - parabola(V_range, step['a'], step['b'], step['c']), - color=(color, color, color)) - parabola_Vmin = -step['b'] / 2. / step['a'] - parabola_Emin = parabola(parabola_Vmin, step['a'], step['b'], - step['c']) - pl.plot([parabola_Vmin], [parabola_Emin], - 'x', - color=(color, color, color)) - pl.annotate(str(idx), xy=(step['V'], step['E'])) - pl.axvline(step['V'], color=(1., 0.7, 1.), linestyle='--') - color -= 0.25 - if color <= 0.: - color = 0. - - pl.axis([min_V, max_V, min_E, max_E]) - if subplot == 1: - # some zoom - pl.xlim((41.5, 44.)) - pl.ylim((-259.47, -259.45)) - pl.xlabel('Volume (ang^3)') - pl.ylabel('Energy (eV)') - - pl.show() - - -if __name__ == '__main__': - import sys - try: - pk_value = int(sys.argv[1]) - except (IndexError, ValueError): - print( - 'Pass as parameter the PK of the WorkChain calculating the pressure', - file=sys.stderr) - print('convergence to generate the plot.', file=sys.stderr) - sys.exit(1) - show_parabola_results(pk_value) diff --git a/docs/pages/2019_SINTEF/scripts/pressure_convergence.py b/docs/pages/2019_SINTEF/scripts/pressure_convergence.py deleted file mode 100644 index 89f42195..00000000 --- a/docs/pages/2019_SINTEF/scripts/pressure_convergence.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding: utf-8 -*- -"""Pressure convergence WorkChain""" -from __future__ import absolute_import -from __future__ import print_function -from aiida.engine import WorkChain, ToContext, while_, calcfunction, workfunction -from aiida.orm import Code, Float, Str, StructureData -from aiida.plugins import CalculationFactory, DataFactory - -from common_wf import generate_scf_input_params -from create_rescale import rescale - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def get_energy_first_derivative(stress): - """Return the energy first derivative from the stress. - - :param stress: the stress tensor in GPa - :return: first derivative of the energy in eV/Å^3 - """ - from numpy import trace - - GPa_to_eV_over_ang3 = 1. / 160.21766208 - - # Get the pressure (GPa) - pressure = trace(stress) / 3. - - # Pressure is -dE/dV; moreover p in kbar, we need to convert it to eV/Å^3 to be consistent - dE = -pressure * GPa_to_eV_over_ang3 - - return dE - - -def get_volume_energy_and_derivative(output_parameters): - """Return the volume, energy and energy derivative for given `PwCalculation` output parameters. - - Volume is in Å^3, energy in eV and energy derivative eV/Å^3 - - :param output_parameters: the `output_parameters` `Dict` result node of a `PwCalculation` - :return: tuple with volume, energy and energy derivative - """ - V = output_parameters.dict.volume - E = output_parameters.dict.energy - dE = get_energy_first_derivative(output_parameters.dict.stress) - - return V, E, dE - - -def get_energy_second_derivative(output_parameters_one, output_parameters_two): - """Return second derivative of the energy with respect to the volume given the results of two `PwCalculations`. - - The derivate is computed using the finite differences method. - - :param output_parameters_one: the `output_parameters` `Dict` result node of the first `PwCalculation` - :param output_parameters_two: the `output_parameters` `Dict` result node of the second `PwCalculation` - :return: the second derivative of the energy with respect to the volume - """ - dE1 = get_energy_first_derivative(output_parameters_one.dict.stress) - dE2 = get_energy_first_derivative(output_parameters_two.dict.stress) - V1 = output_parameters_one.dict.volume - V2 = output_parameters_two.dict.volume - - return (dE2 - dE1) / (V2 - V1) - - -def get_parabola_coefficients(V, E, dE, ddE): - """Return coefficients of a parabola fit to E = a*V^2 + b*V + c for the given volume and energy. - - :param V: volume in Å^3 - :param E: energy in eV - :param dE: the first derivative of the energy with respect to the volume - :param ddE: the second derivative of the energy with respect to the volume - :return: coefficients a, b and c - """ - a = ddE / 2. - b = dE - ddE * V - c = E - V * dE + V**2 * ddE / 2. - - return a, b, c - - -@workfunction -def get_structure(structure, step_data=None): - """Return a scaled version of the given structure, where the new volume is determined by the given step data.""" - initial_volume = structure.get_cell_volume() - - if step_data is None: - new_volume = initial_volume + 4. # In Å^3 - else: - # Minimum of a parabola - new_volume = -step_data.dict.b / 2. / step_data.dict.a - - scale_factor = (new_volume / initial_volume)**(1. / 3.) - scaled_structure = rescale(structure, Float(scale_factor)) - return scaled_structure - - -@calcfunction -def get_step_data(parameters_first, parameters_second=None): - """Generate a dictionary with the step parameters from the output parameters of a completed `PwCalculation`.""" - - # If the parameters of the second calculation are not passed, this is for the first step and we only return - # a dictionary with the volume, energy and derivative - if parameters_second is None: - V, E, dE = get_volume_energy_and_derivative(parameters_first) - return Dict(dict={'V': V, 'E': E, 'dE': dE}) - - # Otherwise, this is an iteration step and we return the difference between the steps - V, E, dE = get_volume_energy_and_derivative(parameters_first) - ddE = get_energy_second_derivative(parameters_first, parameters_second) - a, b, c = get_parabola_coefficients(V, E, dE, ddE) - - return Dict(dict={ - 'V': V, - 'E': E, - 'dE': dE, - 'ddE': ddE, - 'a': a, - 'b': b, - 'c': c - }) - - -@calcfunction -def bundle_step_data(step0, **kwargs): - """Bundle step data into Dict.""" - steps = [step.get_dict() for step in kwargs.values()] - print((step0, type(step0))) - print((steps, type(steps))) - return Dict(dict={'step0': step0.get_dict(), 'steps': steps}) - - -class PressureConvergence(WorkChain): - """Relax a structure using Newton's algorithm on the first derivative of the energy (minus the pressure).""" - - @classmethod - def define(cls, spec): - """Define spec of WorkChain.""" - # yapf: disable - # pylint: disable=bad-continuation - super(PressureConvergence, cls).define(spec) - spec.input('code', valid_type=Code, - help='Code setup to run `pw.x` to use for the calculations.') - spec.input('structure', valid_type=StructureData, - help='The structure to minimize.') - spec.input('pseudo_family', valid_type=Str, - help='Family of pseudopotentials to use for the calculations.') - spec.input('volume_tolerance', valid_type=Float, - help='Stop if the volume difference of two consecutive calculations is less than this threshold.') - spec.output('steps', valid_type=Dict, - help='The data of all the steps in the minimization process containing info about energy and volume') - spec.output('structure', valid_type=StructureData, - help='Final relaxed structure.') - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - - def setup(self): - """Launch the first calculation for the input structure, and a second calculation for a shifted volume.""" - scaled_structure = get_structure(self.inputs.structure) - self.ctx.last_structure = scaled_structure - - inputs0 = generate_scf_input_params(self.inputs.structure, - self.inputs.code, - self.inputs.pseudo_family) - inputs1 = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - - # Run two `PwCalculations` - future0 = self.submit(PwCalculation, **inputs0) - future1 = self.submit(PwCalculation, **inputs1) - - # Wait for them to complete before going to the next step - return ToContext(r0=future0, r1=future1) - - def put_step0_in_ctx(self): - """Store the outputs of the very first step in a specific dictionary.""" - self.ctx.step0 = get_step_data(self.ctx.r0.outputs.output_parameters) - - # Prepare the list containing the steps: step 1 will be stored here by move_next_step - self.ctx.steps = [] - - def move_next_step(self): - """Main part of the algorithm. - - Compare the results of two consecutive calculations and use Newton's algorithm on the pressure by fitting the - results with a parabola and setting the next volume to calculate to the parabola minimum. - - The oldest calculation gets replaced by the most recent and a new calculation is launched that will replace - the most recent. - """ - # Computer the new Volume using Newton's algorithm and create the new corresponding structure by scaling it - new_step_data = get_step_data(self.ctx.r0.outputs.output_parameters, - self.ctx.r1.outputs.output_parameters) - scaled_structure = get_structure(self.inputs.structure, new_step_data) - self.ctx.steps.append(new_step_data) - - # Replace the older step with the latest and set the current structure - self.ctx.r0 = self.ctx.r1 - self.ctx.last_structure = scaled_structure - - inputs = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - future = self.submit(PwCalculation, **inputs) - - return ToContext(r1=future) - - def not_converged(self): - """Return True if the worflow is not converged yet (i.e. the volume changed significantly).""" - r0_out = self.ctx.r0.outputs.output_parameters - r1_out = self.ctx.r1.outputs.output_parameters - - return abs(r1_out.dict.volume - - r0_out.dict.volume) > self.inputs.volume_tolerance - - def finish(self): - """Attach the result nodes as outputs.""" - steps = { - 'step{}'.format(index + 1): step - for index, step in enumerate(self.ctx.steps) - } - bundled_steps = bundle_step_data(step0=self.ctx.step0, **steps) - - self.out('steps', bundled_steps) - self.out('structure', self.ctx.last_structure) diff --git a/docs/pages/2019_SINTEF/scripts/simple_sync_workflow.py b/docs/pages/2019_SINTEF/scripts/simple_sync_workflow.py deleted file mode 100644 index 1a89206e..00000000 --- a/docs/pages/2019_SINTEF/scripts/simple_sync_workflow.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -"""Simple workflow example""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import zip - -from aiida.engine import run, Process, calcfunction, workfunction -from aiida.orm import load_code, Dict, Float, Str -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -# Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' -PwCalculation = CalculationFactory('quantumespresso.pw') - - -@calcfunction -def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -@workfunction -def run_eos_wf(code, pseudo_family, element): - """Run an equation of state of a bulk crystal structure for the given element.""" - - # This will print the pk of the work function - print('Running run_eos_wf<{}>'.format(Process.current().pid)) - - scale_factors = (0.96, 0.98, 1.0, 1.02, 1.04) - labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - calculations = {} - - # Create an initial bulk crystal structure for the given element, using the calculation function defined earlier - initial_structure = create_diamond_fcc(element) - - # Loop over the label and scale_factor pairs - for label, factor in list(zip(labels, scale_factors)): - - # Generated the scaled structure from the initial structure - structure = rescale(initial_structure, Float(factor)) - - # Generate the inputs for the `PwCalculation` - inputs = generate_scf_input_params(structure, code, pseudo_family) - - # Launch a `PwCalculation` for each scaled structure - print('Running a scf for {} with scale factor {}'.format( - element, factor)) - calculations[label] = run(PwCalculation, **inputs) - - # Bundle the individual results from each `PwCalculation` in a single dictionary node. - # Note: since we are 'creating' new data from existing data, we *have* to go through a `calcfunction`, otherwise - # the provenance would be lost! - inputs = { - label: result['output_parameters'] - for label, result in calculations.items() - } - eos = create_eos_dictionary(**inputs) - - # Finally, return the results of this work function - result = {'initial_structure': initial_structure, 'eos': eos} - - return result - - -def run_eos(code=load_code('qe-6.4.1-pw@localhost'), - pseudo_family='SSSP', - element='Si'): - """Helper function to run EOS WorkChain.""" - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - -if __name__ == '__main__': - run_eos() diff --git a/docs/pages/2019_SINTEF/sections/appendix_computer_code_setup.rst b/docs/pages/2019_SINTEF/sections/appendix_computer_code_setup.rst deleted file mode 100644 index f4dab4c3..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_computer_code_setup.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _2019_sintef_appendix_computer_code_setup: - -Setting up computers and codes -============================== - -To setup a computer, please refer to the `online documentation `_. -Instructions to setup a code for a computer can be `found here `_. diff --git a/docs/pages/2019_SINTEF/sections/appendix_input_validation.rst b/docs/pages/2019_SINTEF/sections/appendix_input_validation.rst deleted file mode 100644 index f41e6e0c..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_input_validation.rst +++ /dev/null @@ -1,94 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Calculation input validation -============================ - -This appendix shows additional ways to debug errors with Quantum ESPRESSO, namely -how to take advantage of a powerful tool to validate the input to Quantum ESPRESSO -and possibly suggest the correct name to misspelled keywords. - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. -Let's check for example this input dictionary, where we inserted two mistakes: - -.. code:: python - - parameters_dict = { - 'CTRL': { - 'calculation': 'scf', - 'restart_mode': 'from_scratch', - }, - 'SYSTEM': { - 'nat': 2, - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - -The two mistakes in the dictionary are the following: - -- We wrote a wrong namelist name (``CTRL`` instead of ``CONTROL``). -- We inserted the number of atoms explicitly: while that is indeed how the - number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system, - since this information is already contained within the StructureData. - -After we correct the dictionary ``parameters_dict``, we could run the calculation directly. -Alternatively, we can use a tool of the the `aiida-quantumespresso` plugin, -the 'input helper', which checks the inputs before submitting the calculation. - -In your script, after you define ``parameters_dict``, -you can validate it with the following command (note that you need to pass also an input crystal structure, -``structure``, to allow the validator to perform all the needed checks): - -.. code:: python - - PwCalculation = CalculationFactory('quantumespresso.pw') - validated_dict = PwCalculation.input_helper(parameters_dict, structure=structure) - parameters = Dict(dict=validated_dict) - - -The ``input_helper`` method will check for the correctness of the input parameters. -If misspelling or incorrect keys are detected, the method raises an exception, -which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, -without providing the namelists (useful if you don't remember where to put some variables in the input file). -Hence, you can provide to ``input_helper`` a dictionary like this one: - -.. code:: python - - parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, - } - - validated_dict = PwCalculation.input_helper( - parameters_dict, - structure=structure, - flat_mode=True - ) - -If you print ``validated_dict``, it will look like the following: - -.. code:: python - - { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } diff --git a/docs/pages/2019_SINTEF/sections/appendix_queries.rst b/docs/pages/2019_SINTEF/sections/appendix_queries.rst deleted file mode 100644 index 3737dc51..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_queries.rst +++ /dev/null @@ -1,89 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Queries in AiiDA - Examples and Exercises -========================================= - -Optional exercises on relationships (Task 3) --------------------------------------------- - -**Hint for the exercises:** - -- You can have projections on properties of more than one entity in your query. - You just have to add the ``project`` key (specifying the list of properties that you want to project) along with the corresponding entity when you append it. - -**Exercises:**   Try to write the following queries: - -- Find all descendants of a ``StructureData`` with a certain ``uuid`` (descendants indicate all nodes that can be reached by following outgoing links starting from a given node, with any number of hops). - For every match, print both the ``StructureData`` node and the descendant. - -- Find all the ``FolderData`` nodes created by a specific user. - -Optional exercises on attributes and extras (Task 4) ----------------------------------------------------- - -**Hint for the exercises:** - -- You can easily order or limit the number of the results by using the ``order_by()`` and ``limit()`` methods of the QueryBuilder. - For example, we can order all our ``CalcJobNode``s by their ``id`` and limit the result to the first 10 as follows: - - .. code:: python - - qb = QueryBuilder() - qb.append(CalcJobNode, tag='calc') - qb.order_by({'calc': 'id'}) - qb.limit(10) - qb.all() - -**Exercises:** - -- Write a code snippet to check how many pseudopotentials you have for each element. - -- Smearing contribution to the total energy for calculations: - - 1. Write a query that returns the smearing contribution to the energy stored in some instances of ``Dict`` output by a ``CalcJobNode`` generated by running a ``PwCalculation`` process. - 2. Extend the previous query to also get the input structures of the ``CalcJobNode``. - 3. Which structures have a smearing contribution to the energy smaller or equal to ``-0.02``? - -Summarizing what we learned by now - An example ------------------------------------------------ - -At this point you should be able to do queries with projections, joins and filters. -Moreover, you saw how to apply filters and projections even on attributes and extras. -Let's discover the full power of the ``QueryBuilder`` with a complex graph query that allows you to project various properties from different nodes and apply different filters and joins. - -Imagine that you would like to get the smearing energy for all the calculations that have a smearing energy below a given value and have a Sn\ :sub:`2`\ O\ :sub:`3` as input. -Moreover, besides the smearing energy, you would like to print the units of this energy and the formula of the structure that was given to the calculation. -The graphical representation of this query can be seen in :numref:`fig_qb_example_2_2019` and the actual query follows: - -.. _2019_sintef_fig_qb_example_2_2019: -.. figure:: include/images/qb_example_2.png - :width: 50% - :align: center - - Complex graph query. - -.. code:: python - - qb = QueryBuilder() - qb.append( - StructureData, - project=['extras.formula'], - filters={'extras.formula': 'BaO3Ti'}, - tag='structure' - ) - qb.append( - CalcJobNode, - tag='calculation', - with_incoming='structure' - ) - qb.append( - Dict, - tag='results', - filters={'attributes.energy_smearing':{'<=':-1e-8}}, - project=[ - 'attributes.energy_smearing', - 'attributes.energy_smearing_units', - ], - with_incoming='calculation' - ) - qb.all() diff --git a/docs/pages/2019_SINTEF/sections/appendix_restarting_calculations.rst b/docs/pages/2019_SINTEF/sections/appendix_restarting_calculations.rst deleted file mode 100644 index bb06ea24..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_restarting_calculations.rst +++ /dev/null @@ -1,90 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -Restarting calculations -======================= - -Up till now, we have presented several cases where, for example, the wrong input parameters were passed to a calculation, causing it to fail. -Often we had to retype a lot of code, to setup a new calculation with the correct inputs. -In addition, there are many real-life scenarios where one would need to restart calculations from completed calculations with largely the same inputs. -For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to relaunch a calculation from one that has already completed, while having the chance to add or change inputs before launching it. -As an example, let us first submit a total energy calculation using a parameters dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, - } - -In this case, we set a very low number of self consistent iterations (3), which is too small to be able to reach the desired accuracy of 10\ :sup:`-14`. -This means the calculation will not converge and will not be successful, despite there not being any actual mistake in the parameters dictionary. - -To easily restart from the previous calculation, instead of retyping all the inputs, you can simply use the ``get_builder_restart`` method of the ``CalcJobNode``. -Just like the ``get_builder`` method of the ``Process`` class, this will create an instance of the ``ProcessBuilder``, except this one will have all the inputs pre-populated based on the node. -To do so, simply load the node of a terminated calculation job in a ``verdi shell``, that you want to restart from, e.g.: - -.. code:: python - - failed_calculation = load_node('') - restart_builder = failed_calculation.get_builder_restart() - -If you simply type ``restart_builder``, you can see that all the inputs have already been set to those that were used for the original calculation. -The only thing that now remains to be done, is to replace those inputs that caused the failure in the first place. - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['ELECTRONS']['electron_maxstep'] = 80 - restart_builder.parameters = Dict(dict=parameters) - -Simply giving the calculation some more steps in the convergence cycle will probably already fix the problem. - -.. note:: - - We have to create a new ``Dict`` node as we are changing its contents, and the original input is immutable as it would break the provenance. - -However, in this Quantum ESPRESSO example even more can be done, by using the results of the previous calculation to speed up the restart. -To do so, we simply have to set the input ``parent_folder`` to the remote working directory of the old calculation, i.e.: - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['CONTROL']['restart_mode'] = 'restart' - restart_builder.parameters = Dict(dict=parameters) - restart_builder.parent_folder = failed_calculation.outputs.remote_folder - -Note that this particular step is specific to a ``PwCalculation``, but the restart builder concept works for any calculation class. -Any of the inputs can be changed or set. -For example, you may also want to record that this calculation is a restart, in addition to the provenance graph, by setting the label or description: - -.. code:: python - - restart_builder.metadata.label = 'Restart from PwCalculation<{}>'.format(failed_calculation.pk) - -Ultimately whatever needs to be changed for the restart is up to you, but the restart builder makes it a lot easier. -Finally, to submit the restart, since it is a process builder, it works exactly as any other builder: - -.. code:: python - - from aiida.engine import launch - results, node = launch.run.get_node(restart_builder) - -You can now inspect the restarted calculation to verify that this time it actually completed successfully. -Using the restart builder, the required code to setup a calculation is much shorter than the one needed to launch a new one from scratch. -There is no need to load or create many of the inputs such as the pseudopotentials, structures and k-points, -because they were reused from the first calculation. diff --git a/docs/pages/2019_SINTEF/sections/appendix_structure_data.rst b/docs/pages/2019_SINTEF/sections/appendix_structure_data.rst deleted file mode 100644 index cd1f7b3b..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_structure_data.rst +++ /dev/null @@ -1,280 +0,0 @@ -.. _2019_sintef_appendix_structure_data: - -General comments ----------------- - -This section contains an example of how you can use the -:class:`~aiida:aiida.orm.nodes.data.structure.StructureData` object -to create complex crystals. - -With the :class:`~aiida:aiida.orm.nodes.data.structure.StructureData` class we did not -try to have a full set of features to manipulate crystal structures. -Indeed, other libraries such as `ASE `_ exist, -and we simply provide easy -ways to convert between the ASE and the AiiDA formats. On the other hand, -we tried to define a "standard" format for structures in AiiDA, that can be -used across different codes. - - -How to use ``StructureData`` -------------------------------- - -Take a look at the following example:: - - alat = 4. # angstrom - cell = [[alat, 0., 0.,], - [0., alat, 0.,], - [0., 0., alat,], - ] - s = StructureData(cell=cell) - s.append_atom(position=(0.,0.,0.), symbols='Fe') - s.append_atom(position=(alat/2.,alat/2.,alat/2.), symbols='O') - -With the commands above, we have created a crystal structure ``s`` with -a cubic unit cell and lattice parameter of 4 angstrom, and two atoms in the -cell: one iron (Fe) atom in the origin, and one oxygen (O) at the center of -the cube (this cell has been just chosen as an example and most probably does -not exist). - -.. note:: As you can see in the example above, both the cell coordinates and - the atom coordinates are expressed in angstrom, and the position of - the atoms are given in a global absolute reference frame. - -In this way, any periodic structure can be defined. If you want to import -from ASE in order to specify the coordinates, e.g., in terms of the crystal -lattice vectors, see the guide on the conversion to/from ASE below. - -When using the :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` -method, further parameters can be passed. In particular, one can specify -the mass of the atom, particularly important if you want e.g. to run a -phonon calculation. If no mass is specified, the mass provided by -`NIST `_ (retrieved in October 2014) -is going to be used. The list of -masses is stored in the module :py:mod:`aiida.common.constants`, in the -``elements`` dictionary. - -Moreover, in the :class:`~aiida:aiida.orm.nodes.data.structure.StructureData` class -of AiiDA we also support the storage of crystal structures with alloys, -vacancies or partial occupancies. -In this case, the argument of the parameter ``symbols`` -should be a list of symbols, if you want to consider an alloy; -moreover, you must pass a ``weights`` list, with the same length as ``symbols``, -and with values between 0. (no occupancy) and 1. (full occupancy), to specify -the fractional occupancy of that site for each of the symbols specified -in the ``symbols`` list. The sum of -all occupancies must be lower or equal to one; if the sum is lower than one, -it means that there is a given probability of having a vacancy at that -specific site position. - -As an example, you could use:: - - s.append_atom(position=(0.,0.,0.),symbols=['Ba','Ca'],weights=[0.9,0.1]) - -to add a site at the origin of a structure ``s`` consisting of an alloy of -90% of Barium and 10% of Calcium (again, just an example). - -The following line instead:: - - s.append_atom(position=(0.,0.,0.),symbols='Ca',weights=0.9) - -would create a site with 90% probability of being occupied by Calcium, and -10% of being a vacancy. - -Utility properties ``s.is_alloy`` and ``s.has_vacancies`` can be used to -verify, respectively, if more than one element if given in the symbols list, -and if the sum of all weights is smaller than one. - -.. note:: if you pass more than one symbol, the property ``s.is_alloy`` will - always be ``True``, even if only one symbol has occupancy 1 and - all others have occupancy zero:: - - >>> s = StructureData(cell=[[4,0,0],[0,4,0],[0,0,4]]) - >>> s.append_atom(position=(0.,0.,0.), symbols=['Fe', 'O'], weights=[1.,0.]) - >>> s.is_alloy - True - - -Internals: Kinds and Sites --------------------------- -Internally, the :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` -method works by manipulating the kinds and sites of the current structure. -Kinds are instances of the :class:`~aiida:aiida.orm.nodes.data.structure.Kind` class and -represent a chemical species, with given properties (composing element or -elements, occupancies, mass, ...) and identified -by a label (normally, simply the element chemical symbol). - -Sites are instances of the :class:`~aiida:aiida.orm.nodes.data.structure.Site` class -and represent instead each single site. Each site refers -to a :class:`~aiida:aiida.orm.nodes.data.structure.Kind` to -identify its properties (which element it is, the mass, ...) and to its three -spatial coordinates. - -The :py:meth:`~aiida.orm.nodes.data.structure.StructureData.append_atom` works in -the following way: - -* It creates a new :class:`~aiida:aiida.orm.nodes.data.structure.Kind` - class with the properties passed as parameters - (i.e., all parameters except ``position``). - -* It tries to identify if an identical Kind already exists in the list - of kinds of the structure (e.g., in the same atom with the same mass was - already previously added). Comparison of kinds is performed using - :py:meth:`aiida.orm.nodes.data.structure.Kind.compare_with`, and in particular - it returns ``True`` if the mass and the list of symbols and of weights are - identical (within a threshold). If an identical kind ``k`` is found, - it simply adds a new site referencing to kind ``k`` and with the provided - ``position``. Otherwise, it appends ``k`` to the list of kinds of the current - structure and then creates the site referencing to ``k``. The name of the - kind is chosen, by default, equal to the name of the chemical symbol (e.g., - "Fe" for iron). - -* If you pass more than one species for the same chemical symbol, but e.g. with - different masses, a new kind is created and the name is obtained postponing - an integer to the chemical symbol name. For instance, the following lines:: - - s.append_atom(position = [0,0,0], symbols='Fe', mass = 55.8) - s.append_atom(position = [1,1,1], symbols='Fe', mass = 57) - s.append_atom(position = [1,1,1], symbols='Fe', mass = 59) - - will automatically create three kinds, all for iron, with names ``Fe``, - ``Fe1`` and ``Fe2``, and masses 55.8, 57. and 59. respecively. - -* In case of alloys, the kind name is obtained concatenating all chemical - symbols names (and a X is the sum of weights is less than one). The same - rules as above are used to append a digit to the kind name, if needed. - -* Finally, you can simply specify the kind_name to automatically generate a - new kind with a specific name. This is the case if you want a name different - from the automatically generated one, or for instance if you want to create - two different species with the same properties (same mass, symbols, ...). - This is for instance the case in Quantum ESPRESSO in order to describe an - antiferromagnetic cyrstal, with different magnetizations on the different - atoms in the unit cell. - - In this case, you can for instance use:: - - s.append_atom(position = [0,0,0], symbols='Fe', mass = 55.845, name='Fe1') - s.append_atom(position = [2,2,2], symbols='Fe', mass = 55.845, name='Fe2') - - To create two species ``Fe1`` and ``Fe2`` for iron, with the same mass. - - .. note:: You do not need to specify explicitly the mass if the default one - is ok for you. However, when you pass explicitly a name and it coincides - with the name of an existing species, all properties that you - specify must be identical to the ones of the existing species, or the - method will raise an exception. - - .. note:: If you prefer to work with the - internal :class:`~aiida:aiida.orm.nodes.data.structure.Kind` - and :class:`~aiida:aiida.orm.nodes.data.structure.Site` classes, - you can obtain the same - result of the two lines above with:: - - from aiida.orm.nodes.data.structure import Kind, Site - s.append_kind(Kind(symbols='Fe', mass=55.845, name='Fe1')) - s.append_kind(Kind(symbols='Fe', mass=55.845, name='Fe1')) - s.append_site(Site(kind_name='Fe1', position=[0.,0.,0.])) - s.append_site(Site(kind_name='Fe2', position=[2.,2.,2.])) - - -Conversion to/from ASE ----------------------- - -If you have an AiiDA structure ``s``, you can get an ``ase.Atom`` object by -just calling the :meth:`aiida:aiida.orm.nodes.data.structure.StructureData.get_ase` -method:: - - ase_atoms = s.get_ase() - -.. note:: As we support alloys and vacancies in AiiDA, while ``ase.Atom`` does not, - it is not possible to export to ASE a structure with vacancies or alloys. - -If instead you have as ASE Atoms object and you want to load the structure -from it, just pass it when initializing the class:: - - StructureData = DataFactory('structure') - # or: - # from aiida.orm import StructureData - aiida_structure = StructureData(ase = ase_atoms) - -Creating multiple species -+++++++++++++++++++++++++ - -We implemented the possibility of specifying different Kinds (species) in the -``ase.atoms`` and then importing them. - -In particular, if you specify atoms with different mass in ASE, during the -import phase different kinds will be created:: - - >>> import ase - >>> StructureData = DataFactory("structure") - >>> ase_structure = ase.Atoms('Fe2') - >>> ase_structure[0].mass = 55. - >>> ase_structure[1].mass = 56. - >>> ase_structure.cell = cell # defines a periodic cell - >>> s = StructureData(ase=ase_structure) - >>> for kind in s.kinds: - >>> print(kind.name, kind.mass) - Fe 55.0 - Fe1 56.0 - -Moreover, even if the mass is the same, but you want to get different species, -you can use the ASE ``tags`` to specify the number to append to the element -symbol in order to get the species name:: - - >>> import ase - >>> StructureData = DataFactory("structure") - >>> ase_structure = ase.Atoms('Fe2') - >>> ase_structure[0].tag = 1 - >>> ase_structure[1].tag = 2 - >>> ase_structure.cell = cell - >>> s = StructureData(ase=ase_structure) - >>> for kind in s.kinds: - >>> print(kind.name) - Fe1 - Fe2 - -.. note:: in complicated cases (multiple tags, masses, ...), - it is possible that exporting a AiiDA structure - to ASE and then importing it again will not perfectly preserve the kinds and - kind names. - -Conversion to/from pymatgen ---------------------------- - -AiiDA structure can be converted to pymatgen's `Molecule`_ and -`Structure`_ objects by using, accordingly, -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen_molecule` -and -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen_structure` -methods:: - - pymatgen_molecule = aiida_structure.get_pymatgen_molecule() - pymatgen_structure = aiida_structure.get_pymatgen_structure() - -A single method -:meth:`~aiida:aiida.orm.nodes.data.structure.StructureData.get_pymatgen` can be -used for both tasks: converting periodic structures (periodic boundary -conditions are met in all three directions) to pymatgen's Structure and -other structures to pymatgen's Molecule:: - - pymatgen_object = aiida_structure.get_pymatgen() - -It is also possible to convert pymatgen's Molecule and Structure -objects to AiiDA structures:: - - StructureData = DataFactory("structure") - from_mol = StructureData(pymatgen_molecule=mol) - from_struct = StructureData(pymatgen_structure=struct) - -Also in this case, a generic converter is provided:: - - StructureData = DataFactory("structure") - from_mol = StructureData(pymatgen=mol) - from_struct = StructureData(pymatgen=struct) - -.. note:: Converters work with version 3.0.13 or later of - pymatgen. Earlier versions may cause errors. - -.. _Molecule: http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Molecule -.. _Structure: http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Structure diff --git a/docs/pages/2019_SINTEF/sections/appendix_upf_data.rst b/docs/pages/2019_SINTEF/sections/appendix_upf_data.rst deleted file mode 100644 index ccd09de2..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_upf_data.rst +++ /dev/null @@ -1,123 +0,0 @@ -.. _2019_sintef_appendix_upf_data: - -Introduction: Pseudopotential families -++++++++++++++++++++++++++++++++++++++ - -The procedure of attaching a pseudopotential file to each atomic species -can easily become tedious. In many situations, you will not produce a different -pseudopotential file for every calculation you perform; most likely, when you start a project -you will stick to a pseudopotential file that is adequate for what you need. -Furthermore, in a high-throughput calculation, you will like to do calculations -over several elements while keeping the same functional. -That is also part of the reason why there are several projects -(like the `PSLibrary `_ -or `GBRV `_ -to name a few), that intend to develop a set of pseudopotentials that covers most -of the periodic table for different functionals. - -For this reason we introduced the concept of *pseudopotential families*. -Each family is a set of pseudopotentials that are grouped together in a special type of -`AiiDA Group of nodes`. Within each family, at most one pseudopotential -can be present for a given chemical element. - -A pseudopotential family does not necessarily have to cover the whole periodic table. -This means that you can create a pseudopotential family containing only the -pseudopotentials for a few elements that you are interested in. - -.. note:: - In principle, you can group different kinds of pseudopotentials into the same family. - It is your responsibility to group only those with the same type, - or obtained using the same functionals, approximations and / or levels of theory. - -Creating a pseudopotential family -+++++++++++++++++++++++++++++++++ - -.. note:: - The following commands are specific to the `Quantum ESPRESSO - interface `_. - For interfaces to other codes, please refer to the respective plugin documentation. - -In the following, we will create a pseudopotential family. -First, you need to collect the pseudopotential files which should go into the family in a -single folder -- we'll call it ``path/to/folder``. You can then add the family to -the AiiDA database with ``verdi``:: - - verdi data upf uploadfamily path/to/folder name_of_the_family "some description for your convenience" - -where ``name_of_the_family`` should be a unique name for the family, -and the final parameter is a string that is set in the ``description`` field of the group. - -If the a pseudopotential family with the same ``name_of_the_family`` exists already, -the pseudopotentials in the folder will be added to the existing group. -The code will raise an error if you try to add two (different) pseudopotentials for the same element. - -After the upload (which may take some seconds, so please be patient) -the pseudopotential family will be ready for use. - -.. hint:: - If you upload pseudopotentials which are already present in your database, - AiiDA will use the existing ``UPFData`` node instead of creating a duplicate one. - You can use the optional flag ``--stop-if-existing`` to instead abort - (without changing anything in the database) if an existing pseudopotential is found. - - -Getting the list of existing families -+++++++++++++++++++++++++++++++++++++ -To see wich pseudopotential families already exist in the database, type - -.. code-block:: console - - $ verdi data upf listfamiliesverdi data upf listfamilies - -Add a ``-d`` (or ``--with-description``) flag if you also want to read the description of each family. - -You can also filter the groups to get only a list of those containing a given set of elements -using the ``-e`` option. For instance, if you want to get only the families containing the -elements ``Ba``, ``Ti`` and ``O``, use - -.. code-block:: console - - $ verdi data upf listfamiliesverdi data upf listfamilies -e Ba Ti O - - -For more information on the command line options, type - -.. code-block:: console - - $ verdi data upf listfamiliesverdi data upf listfamilies -h - - -Manually adding pseudopotentials -++++++++++++++++++++++++++++++++ - -If you do not want to use pseudopotentials from a family, it is also possible to manually -add them to the database (even though we discourage this in general). - -A possible way of doing it is the following: we start by creating a list of -pseudopotential filenames that we need to use:: - - raw_pseudos = [ - "Ba.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF", - "Ti.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF", - "O.pbesol-n-rrkjus_psl.0.1-tested-pslib030.UPF"] - -In this simple example, we expect the pseudopotentials to be in the same folder -of the script. Then, we loop over the filenames and add them to the AiiDA database. -The ``get_or_create`` method checks if the pseudopotential is already in the database -and either stores it, or just returns the node already present in the database. -The second value returned is a boolean and tells us if the pseudopotential was -already present or not. - -:: - - UpfData = DataFactory('upf') - pseudos_to_use = [] - - for filename in raw_pseudos: - absname = os.path.abspath(filename) - pseudo, created = UpfData.get_or_create(absname, use_first=True) - pseudos_to_use.append(pseudo) - -.. note:: - When the pseudopotential is created, it is parsed and the elements to which it refers is stored - in the database and can be accessed using the ``pseudo.element`` property, as shown above. diff --git a/docs/pages/2019_SINTEF/sections/appendix_workflow_logic.rst b/docs/pages/2019_SINTEF/sections/appendix_workflow_logic.rst deleted file mode 100644 index db423b07..00000000 --- a/docs/pages/2019_SINTEF/sections/appendix_workflow_logic.rst +++ /dev/null @@ -1,118 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -.. _2019_sintef_workflow_logic: - -More workflow logic: while loops and conditional statements -=========================================================== - -In the previous sections, you have been introduced to ``WorkChains``, and the reason for using them over 'standard' work functions. -However, in the :ref:`original example<2019_sintef_workchainsimple>`, the ``spec.outline`` was quite simple, with a 'static' sequence of two steps. -Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). -To support this scenario, the ``spec.outline`` can support logic: `while` loops and `if/elif/else` blocks. -The simplest way to explain it is to show an example: - -.. code:: python - - from aiida.engine import if_, while_ - - spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), - ) - -that would *roughly* correspond, in a python syntax, to: - -.. code:: python - - s1() - if isA(): - s2() - elif isB(): - s3() - else: - s4() - s5() - while condition(): - s6() - -The only constraint is that condition functions (in the example above ``isA``, ``isB`` and ``condition``) must be class methods that returns ``True`` or ``False`` depending on whether the condition is met or not. - -A suggestion on how to write new workchains: use the outline to help you in designing the logic. -First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. -Then, define one by one the methods. - -.. _2019_sintef_convpressure: - -Pressure convergence --------------------- - -For example, we have prepared a simple workflow (using work chains, work functions and calculation functions) to optimize the lattice parameter of silicon efficiently using Newton's algorithm on the energy derivative, i.e. the pressure :math:`p=-dE/dV`. -You can download this code :download:`here <../scripts/pressure_convergence.py>` -The outline looks like this: - -.. code:: python - - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - -This outline already roughly explains the algorithm: after an initialization (``setup``) and putting the first step (number zero) in the ctx (``put_step0_in_ctx``), a function to move to the next step is called (``move_next_step``). -This is iterated while a given convergence criterion is not met (``not_converged``). -Finally, some reporting is done, including returning some output nodes (``finish``). - -If you are interested in the details of the algorithm, you can inspect the file. -The main ideas are described here: - -``setup`` -Generate a ``pw.x`` calculation for the input structure (with volume -(V)), and one for a structure where the volume is :math:`V+4 \mbox{\normalfont\AA}^3` (just to get a closeby volume). -Store the results in the context as ``r0`` and ``r1`` - -``put_step0_in_ctx`` -Store in the context :math:`V`, :math:`E(V)` and :math:`dE/dV` for the first calculation ``r0`` - -``move_next_step`` -This is the most important function. Calculate :math:`V`, :math:`E(V)` and :math:`dE/dV` for ``r1``. -Also, estimate :math:`d^2E/dV^2` from the finite difference of the first derivative of ``r0`` and ``r1`` (helper functions to achieve this are provided). -Get the :math:`a`, :math:`b` and :math:`c` coefficients of a parabolic fit :math:`E = aV^2 + bV + c` and estimate the expected minimum of the EOS function as the minimum of the fit :math:`V_0 = -b / 2a`. -Finally, replace ``r0`` with ``r1`` in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume :math:`V_0`, that will be stored in the context replacing ``r1``. -In this way, at the next iteration ``r0`` and ``r1`` will contain the latest two simulations. -Finally, at each step some relevant information (coefficients :math:`a`, :math:`b` and :math:`c`, volumes, energies, energy derivatives, ...) are stored in a list called ``steps``. -This whole list is stored in the context because it provides quantities to be preserved between different work chain steps. - -``not_converged`` -Return ``True`` if convergence has not been achieved yet. -Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold ``volume_tolerance``. - -``finish`` -This is the final step. -Mainly, we return the output nodes: ``steps`` with the list of results at each step, and ``structure`` with the final converged structure. - -The results returned in ``steps`` can be used to represent the evolution of the minimisation algorithm. -A possible way to visualize it is presented in :numref:`fig_convpressure` obtained with an initial lattice constant of `alat = 5.2`. - -.. _2019_sintef_fig_convpressure: -.. figure:: include/images/convergence_pressure.png - - Example of results of the convergence algorithm presented in this section. - The bottom plot is a zoom near the minimum. - The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. - The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step. diff --git a/docs/pages/2019_SINTEF/sections/calculations.rst b/docs/pages/2019_SINTEF/sections/calculations.rst deleted file mode 100644 index 7cd2e84a..00000000 --- a/docs/pages/2019_SINTEF/sections/calculations.rst +++ /dev/null @@ -1,625 +0,0 @@ -.. _2019_sintef_calculations: - -Submit, monitor and debug calculations -====================================== - -In this section we'll be learning how to create new data in AiiDA. - -We will use the `Quantum Espresso `_ package to launch a simple `density functional theory `_ calculation of a silicon crystal using the :doi:`PBE exchange-correlation functional <10.1103/PhysRevB.54.16533>` and check its results. -While we're going to debug these issues 'manually' here, workflows (which you'll encounter later in this tutorial) can help you avoid these issues systematically. - -Note that besides the ``aiida-quantumespresso`` plugin, AiiDA comes with plugins for a range of other codes, all of which are listed in the `AiiDA plugin registry `_. - -Computer setup --------------- - -In a production environment, AiiDA would typically be running on your work station or laptop, while launching calculations -on remote high-performance compute resources that you have SSH access to. -For this reason AiiDA has the concept of a ``Computer`` to run calculations on. - -To keep things simple, Quantum ESPRESSO (together with several other *ab initio* codes) has been installed directly in the -``codes`` subfolder of your virtual machine, -and you are going to launch your first calculations on the same computer where AiiDA is installed. -Nevertheless, we're now going to set up this computer for launching calculations: - -.. code:: bash - - verdi computer setup --config computer.yml - -where ``computer.yml`` is a configuration file in the `YAML format `_) that you can :download:`download here `. This is its content: - -.. literalinclude:: include/configuration/computer.yml - -.. note:: - When used without the ``--config`` option, ``verdi computer setup`` will prompt you for the required information, just like you have seen when :ref:`setting up a profile<2019_sintef_setup_verdi_quicksetup>`. - The configuration file should work for the virtual machine that comes with this tutorial - but may need to be adapted when you are running AiiDA in a different environment, - as explained in :ref:`this appendix<2019_sintef_appendix_computer_code_setup>`. - -Finally, you need to provide AiiDA with information on how to access the ``Computer``. -For remote computers with ``ssh`` transport, this would involve e.g. an SSH key. -For ``local`` computers, this is just a "formality" (press enter to confirm the default cooldown time): - -.. code:: bash - - verdi computer configure local localhost - -.. note:: - - For remote computers with ``ssh`` transport, use ``verdi computer configure ssh`` instead of ``verdi computer configure local``. - -Your ``localhost`` computer should now show up in - -.. code:: bash - - verdi computer list - -Before proceeding, test that it works: - -.. code:: bash - - verdi computer test localhost - - -Code setup ----------- - -Now that we have our localhost set up, let's configure the ``Code``, namely the ``pw.x`` executable. -As with the computer, we have prepared a configuration file for you to :download:`download `. -This is its content: - -.. literalinclude:: include/configuration/code.yml - :language: yaml - -Once you have the configuration file in your local working environment, set up the code: - -.. code:: bash - - verdi code setup --config code.yml - -.. warning:: - - The configuration should work for the virtual machine that comes with this tutorial. - If you are following this tutorial in a different environment, you will need to install Quantum ESPRESSO and adapt the configuration to your needs, - as explained in :ref:`this appendix<2019_sintef_appendix_computer_code_setup>`. - -Similar to the computers, you can list all the configured codes with: - -.. code:: bash - - verdi code list - -Verify that it now contains a code named ``qe-6.4.1-pw`` that we just configured. -You can always check the configuration details of an existing code using: - -.. code:: bash - - verdi code show qe-6.4.1-pw - -.. note:: - - The ``generic`` profile has already a number of other codes configured. - See ``verdi -p generic code list``. - - -The AiiDA daemon ----------------- - -First of all, check that the AiiDA daemon is actually running. -The AiiDA daemon is a program that - - * runs continuously in the background - * waits for new calculations to be submitted - * transfers the inputs of new calculations to your compute resource - * checks the status of your calculation at the compute resource, and - * retrieves the results from the compute resource - -Check the status of the daemon process by typing in the terminal: - -.. code:: bash - - verdi daemon status - -If the daemon is running, the output should look like - -.. code:: bash - - Profile: default - Daemon is running as PID 2050 since 2019-04-30 12:37:12 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 2055 2.147 0 2019-04-30 12:37:12 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers - -If this is not the case, type in the terminal - -.. code:: bash - - verdi daemon start - -to start the daemon. - -Creating a new calculation --------------------------- - -In the following, we'll be working in the ``verdi shell``. -As you go along, feel free to keep track of your commands by -copying them into a python script ``test_pw.py``. - -.. note:: - - The ``verdi shell`` imports a number of AiiDA internals so that you as the user don't have to. - You can also make those available to a python script, by running it using - - .. code:: bash - - verdi run - - -Every calculation sent to a cluster is linked to a *code*, which describes -the executable file to be used as well as some metadata. -Let's have a look at the codes already installed on your machine: - -.. code:: bash - - verdi code list - -There should be a number of them. Here, we're interested in the "PWscf" executable of Quantum Espresso, i.e. in codes for the ``quantumespresso.pw`` plugin: - -.. code:: bash - - verdi code list -P quantumespresso.pw - -Pick the correct codename, that might look like, e.g. -``qe-6.4.1-pw@localhost`` and load it in the verdi shell. - -.. code:: python - - code = load_code("") - -.. note:: - - ``load_code`` returns an object of type ``Code``, which is the general AiiDA class for describing simulation codes. - -Let's build the inputs for a new ``PwCalculation`` (defined by the ``quantumespresso.pw`` plugin, the default plugin for the code you chose before) - -.. code:: python - - builder = code.get_builder() - -As the first step, assign a (short) label or a (long) description to your -calculation, that you might find convenient in the future. - -.. code:: python - - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calc with Quantum ESPRESSO on Si" - -This information will be saved in the database for later queries or -inspection. Note that you can press TAB after writing ``builder.`` to -see all inputs available for this calculation. -In order to figure out which data type is expected for a particular input, such as ``builder.structure``, -and whether the input is optional or required, use ``builder.structure??``. - -Now, specify the number of machines (a.k.a. cluster nodes) -you are going to run on and the maximum time allowed for the -calculation. -The general options grouped under ``builder.metadata.options`` are independent of -the code or plugin, and will be passed to the scheduler that handles the -queue on your compute resource . - -.. code:: python - - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - -Again, to see the list of available options, type -``builder.metadata.options.`` and hit the TAB button. - -Preparation of inputs -~~~~~~~~~~~~~~~~~~~~~ - -A Quantum Espresso calculation needs a number of inputs: - -1. `Pseudopotentials `_ -2. a structure -3. a mesh in reciprocal space (k-points) -4. a number of input parameters - -These are mirrored in the inputs of the ``aiida-quantumespresso`` plugin -(see `documentation `_). -We'll start with the structure, k-points, and pseudopotentials -and leave the input parameters as the last thing to setup. - -.. admonition:: Exercise - - Use what you learned in the previous section to load the ``structure`` and ``kpoints`` inputs for your calculation: - - * Use a silicon crystal structure - * Define a ``2x2x2`` mesh of k-points. - - Note: If you just copy and paste code that you executed previously, - this may result in duplication of information on your database. - In fact, you can re-use an existing structure stored in your database [#f1]_. Use a combination of the bash command - ``verdi data structure list`` and of the shell command ``load_node()`` - to get an object representing the structure created earlier. - -Attaching the input information to the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once you've created a ``structure`` node and a ``kpoints`` node, -attach it to the calculation: - -.. code:: python - - builder.structure = structure - builder.kpoints = kpoints - -.. note:: - - The builder accepts both *stored* and *unstored* data nodes. - AiiDA will take care of storing the unstored nodes upon submission. - If you decide not to submit, nothing will be stored in the database. - -PWscf also needs information on the pseudopotentials, -in the form of a dictionary, where keys are the names of the elements and the values are the corresponding ``UpfData`` objects containing the information on the pseudopotential. -However, instead of creating the dictionary by hand, we can use a helper function that picks the right pseudopotentials for our structure from a pseudopotential *family*. -You can list the preconfigured families from the command line: - -.. code:: bash - - verdi data upf listfamilies - -Pick the one you configured earlier (or one of the ``SSSP`` families that -we provide) and link it to the calculation using the command: - -.. code:: python - - from aiida.orm.nodes.data.upf import get_pseudos_from_structure - builder.pseudos = get_pseudos_from_structure(structure, '') - -Print the content of the `pseudos` namespace, e.g. ``print(builder.pseudos)`` to see what the helper function created. - -Preparing and debugging input parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Finally, we need to specify a number of input parameters (i.e. plane wave cutoffs, convergence thresholds, etc.). -to launch the Quantum ESPRESSO calculation. -The structure of the parameter dictionary closely follows the structure of the `PWscf input file `_. - -Since these are often the parameters to tune in a calculation, -let's **introduce a few mistakes intentionally** -and use this part of the tutorial to learn how to debug problems. - -Define a set of input parameters for Quantum ESPRESSO, preparing a -dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, - } - -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, -except for an invalid key ``mickeymouse``. When Quantum ESPRESSO -receives an unrecognized key, it will stop. -By default, the AiiDA plugin will *not* validate your input and simply pass -it on to the code. - -Let's wrap the ``parameters_dict`` python dictionary in an AiiDA ``Dict`` node and see what happens. - -.. code:: python - - builder.parameters = Dict(dict=parameters_dict) - -Simulate submission -~~~~~~~~~~~~~~~~~~~ - -At this stage, you have created in memory (it's not yet stored in the -database) the input of the graph shown below. The outputs will -be created by the daemon later on. - -.. figure:: include/images/verdi_graph/si/graph-full.png - :alt: - -In order to check which input files AiiDA creates, -we can perform a *dry run* of the submission process. -Let's tell the builder that we want a dry run and that -we don't want to store the provenance of the dry run: - -.. code:: python - - builder.metadata.dry_run = True - builder.metadata.store_provenance = False - -It's time to run: - -.. code:: python - - from aiida.engine import run - run(builder) - -.. note:: - - Instead of using the builder, you can also simply pass the calculation class - as the first argument, followed by the inputs as keyword arguments, e.g.: - - .. code:: python - - run(PwCalculation, structure=structure, pseudos={'Si': pseudo_node}, ....) - - The builder is simply a convenience wrapper providing tab-completion in the shell and automatic help strings. - -This creates a folder of the form ``submit_test/[date]-0000[x]`` in the -current directory. In your second terminal: - - * open the input file ``aiida.in`` within this folder - * compare it to input data nodes you created earlier - * verify that the `pseudo` folder contains the needed pseudopotentials - * have a look at the submission script ``_aiidasubmit.sh`` - -.. note:: - - The files created by a dry run are only intended for inspection - and cannot be used to correct the inputs of your calculation. - -Submitting the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Up to now we've just been playing around and our calculation has been kept -in memory and not in the database. -Now that we have inspected the input files and convinced ourselves that -Quantum ESPRESSO will have all the information it needs to perform the -calculation, we will submit the calculation properly. -Doing so will make sure that all inputs are stored in the database, -will run and store the calculation and link the outputs to it. - -Let's revert the following values in our builder to their defaults: - -.. code:: python - - builder.metadata.dry_run = False - builder.metadata.store_provenance = True - -And then rely on the submit machinery of AiiDA, - -.. code:: python - - from aiida.engine import submit - calculation = submit(builder) - -As soon as you have executed these lines, the ``calculation`` variable contains a ``PwCalculation`` instance, already submitted to the daemon. - -.. note:: - - You may have noticed that we used ``submit`` here instead of ``run``. - The difference is that ``submit`` will hands over the calculation to the daemon running in the background, - while ``run`` will execute all tasks in the current shell. - - All processes in AiiDA (you will soon get to know more) can be "launched" using one of available functions: - - * run - * run_get_node - * run_get_pk - * submit - - which are explained in more detail in the `online documentation `_. - - -The calculation is now stored in the database and was assigned a "database primary key" or ``pk`` (``calculation.pk``) as well as a UUID (``calculation.uuid``). -See the :ref:`previous section <2019-aiida-identifiers>` for more details on these identifiers. - -Note that while AiiDA will prevent you from changing the content of stored nodes, -the concept of "extras" allows you to set extra attributes, e.g. as a way of -labelling nodes and providing information for querying. - -For example, let's add an extra attribute called ``element``, with value ``Si``: - -.. code:: python - - calculation.set_extra("element", "Si") - -In the mean time, after you submitted your calculation, the daemon -picked it up and started to: generate the input files, submit the calculation -to the queue, wait for it to run and finish, retrieve the output files, -parse them, store them in the database and set the state of the -calculation to ``Finished``. - -.. note:: - - If the daemon is not running, the calculation will remain in the - ``NEW`` state until you start the daemon. - -Checking the status of the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can check the calculation status from the command line: - -.. code:: bash - - verdi process list - -.. note:: - - Since you are running your DFT calculation directly on the VM, - ``verdi`` commands can be a bit slow until the calculation finishes. - -If you don't see any calculation in the -output, the calculation you submitted has already finished. - -By default, the command only prints calculations that are still active [#f2]_. -Let's also list your finished calculations (and limit those only -to the one created in the past day): - -.. code:: bash - - verdi process list -a -p1 - -as explained in the first section. - -Similar to the dry run, we can also inspect the input files of the *actual* -calculation: - -.. code:: bash - - verdi calcjob inputls -c - -for the ``pk_number`` of your calculation. This will show the -contents of the input directory (``-c`` prints directories in color). -Check the content of input files with - -.. code:: bash - - verdi calcjob inputcat | less - -Troubleshooting ---------------- - -Your calculation should end up in a FAILED state -(last column of ``verdi process list -a -p1``), and correspondingly the -error code near the "Finished" status of the State should be non-zero, - -.. code:: bash - - $ verdi process list -a -p1 - PK Created State Process label Process status - ---- --------- ---------------- --------------- ---------------- - 98 16h ago Finished [115] PwCalculation - ... - $ # Anything but [0] after the Finished state signals a failure - -This was expected, since we used an invalid key in the input parameters. -Situations like this happen in real life, so AiiDA provides -tools to trace back to the source of the problem and correct it. - -A first way to proceed is to inspect the output file of -PWscf. - -.. code:: bash - - verdi calcjob outputcat | less - -This might be enough to understand the reason why the calculation -failed. - -AiiDA provides further tools for troubleshooting in a more compact way. -For any calculation, both successful and failed, you can get a summary by: - -.. code:: bash - - $ verdi process show - Property Value - ------------- --------------------------------------------------- - type CalcJobNode - pk 98 - uuid 4c444afd-f6e2-4896-b9ae-8cb8a5ec75c5 - label PW test - description My first AiiDA calc with Quantum ESPRESSO on Si - ctime 2019-05-01 15:59:39.180018+00:00 - mtime 2019-05-01 16:01:44.870902+00:00 - process state Finished - exit status 115 - computer [1] localhost - - Inputs PK Type - ---------- ---- ------------- - pseudos - Si 50 UpfData - code 2 Code - kpoints 10 KpointsData - parameters 96 Dict - settings 97 Dict - structure 9 StructureData - - Outputs PK Type - ------------- ---- ---------- - remote_folder 99 RemoteData - retrieved 100 FolderData - - Log messages - --------------------------------------------- - There are 2 log messages for this calculation - Run 'verdi process report 98' to see them - -The last part of the output alerts you to the fact that there -are some log messages waiting for you, if you run -``verdi process report ``. - -Let's now correct our input parameters dictionary by leaving out the invalid -key and see if our calculation succeeds: - -.. code:: python - - parameters_dict = { - "CONTROL": { - "calculation": "scf", - }, - "SYSTEM": { - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } - } - builder.parameters = Dict(dict=parameters_dict) - calculation = submit(builder) - -If you have been using the separate script approach, modify -the script to remove the faulty input and run it again with: - -.. code:: bash - - verdi run test_pw.py - -Use ``verdi process list -a -p1`` to verify that -the calculation reaches the finished status, with exit code zero. - -Using the calculation results ------------------------------ - -Now you can access the results as you have seen earlier. For example, -note down the pk of the calculation so that you can load it in the -``verdi shell`` and check the total energy with the commands: - -.. code:: python - - calculation = load_node() - calculation.res.energy - -Besides writing input files, running the software for you, storing the output -files, and connecting it all together in your provenance graph, -many AiiDA plugins will parse the output of your code and make output values -of interest available through an output dictionary node (as depicted in the -graph above). -In the case of the ``aiida-quantumespresso`` plugin this output node -is available at ``calculation.outputs.output_parameters`` and you can access -all the available attributes (not only the energy) using: - -.. code:: python - - calculation.outputs.output_parameters.attributes - -While the name of this output dictionary node can be chosen by the plugin, -AiiDA provides the "results" shortcut ``calculation.res`` -that plugin developers can use to provide what they consider the result of the -calculation. - - -.. rubric:: Footnotes - -.. [#f1] In order to avoid duplication of KpointsData, you would first need to learn how to query the database, therefore we will ignore this issue for now. -.. [#f2] A process is considered active if it is either ``Created``, ``Running`` or ``Waiting``. If a process is no longer active, but terminated, it will have a state ``Finished``, ``Killed`` or ``Excepted``. diff --git a/docs/pages/2019_SINTEF/sections/first_taste.rst b/docs/pages/2019_SINTEF/sections/first_taste.rst deleted file mode 100644 index 4928a2c7..00000000 --- a/docs/pages/2019_SINTEF/sections/first_taste.rst +++ /dev/null @@ -1,328 +0,0 @@ -A first taste -============= - -Let's start with a quick demo of how AiiDA can make your life easier as a computational scientist. - -To get started, type ``workon aiida`` in your terminal to enter the *virtual environment* where AiiDA is installed. -You have entered the the virtual environment when the prompt starts with ``(aiida)``, e.g.:: - - (aiida) username@hostname:~$ - -.. note:: - - You need to retype ``workon aiida`` whenever you open a new terminal. - -We'll be using the ``verdi`` command-line interface, -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - -Here are some first tasks for you: - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on any ``verdi`` command, simply append the ``--help/-h`` flag: - - .. code:: bash - - verdi -h - -Getting help ------------- - -There are a number of helpful resources available to you for getting more information about AiiDA. -Please consider: - - * consulting the extensive `AiiDA documentation `_ - * asking in the `Slack channel of the tutorial `_ - * asking your neighbor - * asking a tutor - * opening a new issue on the `tutorial issue tracker `_ - -Importing a structure and running a calculation ------------------------------------------------ - -Let's download a structure from the `Crystallography Open Database `_ and import it into AiiDA: - -.. code:: bash - - wget http://crystallography.net/cod/9008565.cif - verdi data structure import ase 9008565.cif - -Each piece of data in AiiDA gets a PK number (a "primary key") that identifies it in your database. -The PK is printed to screen by the ``verdi data structure import`` command. -**Mark down the PK for your structure and use it to replace the placeholders in what follows.** - -.. note:: - - You can view the structure either `online `_ - or use ``jmol 9008565.cif`` locally. - -The following short python script sets up a self-consistent field calculation for the Quantum ESPRESSO code: - -.. literalinclude:: include/snippets/demo_calcjob.py - -Download the :download:`demo_calcjob.py ` script to your working directory. -It contains a few placeholders for you to fill in: - - #. the VM already has a number of codes preconfigured. Use ``verdi code list`` to find the label for the "PW" code and use it in the script. - #. replace the PK of the structure with the one you obtained - #. the VM already contains a number of pseudopotential families. Replace the PP family name with the one for the "SSSP efficiency" library found via ``verdi data upf listfamilies``. - -Then submit the calculation using: - -.. code:: bash - - verdi run demo_calcjob.py - -From this point onwards, the AiiDA daemon will take care of your calculation: creating the necessary input files, running the calculation, and parsing its results. -It should take less than one minute to complete. - -Analyzing the outputs of a calculation --------------------------------------- - -Let's have a look how your calculation is doing: - -.. code:: bash - - verdi process list # shows only running processes - verdi process list --all # shows all processes - -Again, your calculation will get a PK, which you can use to get more information on it: - -.. code:: bash - - verdi process show - -As you can see, AiiDA has tracked all the inputs provided to the calculation, allowing you (or anyone else) to reproduce it later on. -AiiDA's record of a calculation is best displayed in the form of a provenance graph - -.. figure:: include/images/demo_calc.png - :width: 100% - - Provenance graph for a single Quantum ESPRESSO calculation. - -You can generate such a provenance graph for any calculation or data in AiiDA by running: - -.. code:: bash - - verdi node graph generate - -Try to reproduce the figure using the PK of your calculation. - -.. note:: - - By default, AiiDA uses UUIDs to label nodes in provenance graphs (more about UUIDs vs PKs later). - Try using the ``-h`` option to figure out how to switch to the PK identifier. - - -You might wonder what happened under the hood, e.g. where to find the actual input and output files of the calculation. -You will learn more about this later -- until then, here are a few useful commands to try: - -.. code:: bash - - verdi calcjob inputcat # shows the input file of the calculation - verdi calcjob outputcat # shows the output file of the calculation - verdi calcjob res # shows the parsed output - -A few questions you could answer using these commands (optional) - * How many atoms did the structure contain? - How many electrons? - * How many k-points were specified? How many k-points were actually computed? Why? - * How many SCF iterations were needed for convergence? - * How long did Quantum ESPRESSO actually run (wall time)? - -Moving to a different computer ------------------------------- - -Now, this Quantum ESPRESSO calculation ran on your (virtual) machine. -This is fine for tests, but for production calculations you'll typically want to run on a remote compute cluster. -In AiiDA, moving a calculation from one computer to another means changing one line of code. - -For the purposes of this tutorial, you'll run on your neighbor's computer. -Ask your neighbor for the IP address of their VM. -Then, download the :download:`neighbor.yml ` setup template, replace the placeholder by the IP address and let AiiDA know about this computer by running: - -.. .. literalinclude:: include/configuration/neighbor.yml - -.. code:: bash - - verdi computer setup --config neighbor.yml - -.. note:: - - If you're completing this tutorial at a later time and have no partner machine, - simply use "localhost" instead of the IP address. - -AiiDA is now aware of the existence of the computer but you'll still need to let AiiDA -know how to connect to it. -AiiDA does this via `SSH `_ keys. -Your tutorial VM already contains a private SSH key for connecting to the ``compute`` user of your neighbor's machine, -so all that is left is to configure it in AiiDA. - -Download the :download:`neighbor-config.yml ` configuration template and run: - -.. .. literalinclude:: include/configuration/neighbor-config.yml - -.. code:: bash - - verdi computer configure ssh neighbor --config neighbor-config.yml --non-interactive - -.. note:: Both ``verdi computer setup`` and ``verdi computer configure`` can be used interactively without - configuration files, which are provided here just to avoid typing errors. - -AiiDA should now have access to your neighbor's computer. Let's quickly test this: - -.. code:: bash - - verdi computer test neighbor - -Finally, let AiiDA know about the **code** we are going to use. -We've again prepared a template that looks as follows: - -.. literalinclude:: include/configuration/qe.yml - -Download the :download:`qe.yml ` code template and run: - -.. code:: bash - - verdi code setup --config qe.yml - verdi code list # note the label of the new code you just set up! - -Now modify the code label in your ``demo_calcjob.py`` script to the label of your new code and simply run another calculation using ``verdi run demo_calcjob.py``. - -To see what is going on, AiiDA provides a command that lets you jump to the folder of the directory of the calculation on the remote computer: - -.. code:: bash - - verdi process list --all # get PK of new calculation - verdi calcjob gotocomputer - -Have a look around. - * Do you recognize the different files? - * Have a look at the submission script ``_aiidasubmit.sh``. - Compare it to the submission script of your previous calculation. - What are the differences? - -From calculations to workflows ------------------------------- - -AiiDA can help you run individual calculations but it is really designed to help you run workflows that involve several calculations, while automatically keeping track of the provenance for full reproducibility. -As the final step, you are going to run such a workflow. - -Let's have a look at the workflows that are currently installed: - -.. code:: bash - - verdi plugin list aiida.workflows - - -The ``quantumespresso.pw.band_structure`` workflow from the `aiida-quantumespresso `_ plugin computes the electronic band structure for a given atomic structure. -Let AiiDA tell you which inputs it takes and which outputs it produces: - - -.. code:: bash - - verdi plugin list aiida.workflows quantumespresso.pw.band_structure - -We have prepared a short python snippet that shows how to run this workflow. - -.. literalinclude:: include/snippets/demo_bands.py - -Download the :download:`demo_bands.py ` snippet, replace the PK, and run it using - -.. code:: bash - - verdi run demo_bands.py - -This workflow will: - - #. Determine the primitive cell of the input structure - #. Run a calculation on the primitive cell to relax both the cell and the atomic positions (``vc-relax``) - #. Refine the symmetry of the relaxed structure, and find a standardised primitive cell using SeeK-path_ - #. Run a self-consistent field calculation on the refined structure - #. Run a band structure calculation at fixed Kohn-Sham potential along a standard path between high-symmetry k-points determined by SeeK-path_ - -The workflow uses the PBE exchange-correlation functional with suitable pseudopotentials and energy cutoffs from the `SSSP library version 1.1 `_. - - -.. _SeeK-path: https://www.materialscloud.org/work/tools/seekpath - -.. K-point mesh is selected to have a minimum k-point density of 0.2 Å-1 - A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations - -The workflow should take ~10 minutes on your virtual machine. -You may notice that ``verdi process list`` now shows more than one entry. -While you wait for the workflow to complete, -let's start exploring its provenance. - -The full provenance graph obtained from ``verdi node graph generate`` will already be rather complex (you can try!), -so let's try browsing the provenance interactively instead. - -Start the AiiDA REST API: - -.. code:: bash - - verdi restapi - -and open the |provenance browser|. - -.. |provenance browser| raw:: html - - Materials Cloud provenance browser - -.. note:: - - In order for the provenance browser to work, you need to configure SSH to tunnel port 5000 from your VM to your local laptop (see here :ref:`2019_sintef_connect`). - - -The provenance browser is a Javascript application that connects to the AiiDA REST API. -Your data never leaves your computer. - -.. some general comment on importance of the graph? -.. a sentence on how to continue from here - -Browse your AiiDA database. - * Start by finding your structure in Data => Structure - * Use the provenance browser to explore the steps of the ``PwBandStructureWorkChain`` - -.. note:: - - When perfoming calculations for a publication, you can export your provenance graph using ``verdi export create`` and upload it to the `Materials Cloud Archive `_, enabling your peers to explore the provenance of your calculations online. - -Once the workchain is finished, use ``verdi process show `` to inspect the ``PwBandStructureWorkChain`` and find the PK of its ``band_structure`` output. -Use this to produce a PDF of the band structure: - -.. code:: bash - - verdi data bands export --format mpl_pdf --output band_structure.pdf - - -.. figure:: include/images/si_bands.png - :width: 80% - - Band structure computed by the `PwBandStructure` workchain. - -.. note:: - The ``BandsData`` node does contain information about the Fermi energy, so the energy zero in your plot will be arbitrary. - You can produce a plot with the Fermi energy set to zero (as above) using the following code in a jupyter notebook: - - .. code:: ipython - - %aiida - %matplotlib inline - - scf_params = load_node() # REPLACE with PK of "scf_parameters" output - fermi_energy = scf_params.dict.fermi_energy - - bands = load_node() # REPLACE with PK of "band_structure" output - bands.show_mpl(y_origin=fermi_energy, plot_zero_axis=True) - - - -What next? ----------- - -You now have a first taste of the type of problems AiiDA tries to solve. - -Continue with the in-depth tutorial to learn more about the ``verdi``, ``verdi shell`` and ``python`` interfaces to AiiDA. - - .. _Quantum Mobile: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.08.0 diff --git a/docs/pages/2019_SINTEF/sections/include/configuration/code.yml b/docs/pages/2019_SINTEF/sections/include/configuration/code.yml deleted file mode 100644 index e9bdaad2..00000000 --- a/docs/pages/2019_SINTEF/sections/include/configuration/code.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" -on_computer: true -computer: "localhost" -remote_abs_path: "/usr/local/bin/pw.x" -prepend_text: "ulimit -s unlimited" -append_text: " " diff --git a/docs/pages/2019_SINTEF/sections/include/configuration/computer.yml b/docs/pages/2019_SINTEF/sections/include/configuration/computer.yml deleted file mode 100644 index 01938930..00000000 --- a/docs/pages/2019_SINTEF/sections/include/configuration/computer.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -description: "localhost" -label: "localhost" -hostname: "localhost" -transport: local -scheduler: "direct" -work_dir: "/home/max/.aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_SINTEF/sections/include/configuration/neighbor-config.yml b/docs/pages/2019_SINTEF/sections/include/configuration/neighbor-config.yml deleted file mode 100644 index ae7a0c26..00000000 --- a/docs/pages/2019_SINTEF/sections/include/configuration/neighbor-config.yml +++ /dev/null @@ -1,6 +0,0 @@ ---- -username: compute -key_filename: /home/max/.ssh/aiida-tutorial-compute.pem -timeout: 30 # timout for SSH before giving up -key_policy: AutoAddPolicy # add to known hosts automatically -safe_interval: 5 # Minimum time (s) between consecutive connections diff --git a/docs/pages/2019_SINTEF/sections/include/configuration/neighbor.yml b/docs/pages/2019_SINTEF/sections/include/configuration/neighbor.yml deleted file mode 100644 index 9c311820..00000000 --- a/docs/pages/2019_SINTEF/sections/include/configuration/neighbor.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -label: neighbor -description: Your neighbor's VM. -hostname: IP_ADDRESS # << REPLACE -transport: ssh # connects via SSH -scheduler: slurm # use SLURM scheduler -work_dir: "/tmp/aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_SINTEF/sections/include/configuration/qe.yml b/docs/pages/2019_SINTEF/sections/include/configuration/qe.yml deleted file mode 100644 index 4f9fc76d..00000000 --- a/docs/pages/2019_SINTEF/sections/include/configuration/qe.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" # input plugin for pw.x code -computer: "neighbor" -on_computer: true -remote_abs_path: "/usr/local/bin/pw.x" # path to executable -prepend_text: "ulimit -s unlimited" # set stacksize to unlimited -append_text: " " diff --git a/docs/pages/2019_SINTEF/sections/include/images/Al_bands.png 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - - - Float(0.98) - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - - CALL CALL - - - - RETURN - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - Float(0.98) - - - - - - - - - - - W1 - - - - - - CREATE RESCALED - - - - a) b) diff --git a/docs/pages/2019_SINTEF/sections/include/images/qb_example_2.png b/docs/pages/2019_SINTEF/sections/include/images/qb_example_2.png deleted file mode 100644 index 6a0c9117..00000000 Binary files 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a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/batio3/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/graph.pdf b/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/graph.pdf deleted file mode 100644 index 50597454..00000000 Binary files a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/graph.pdf and /dev/null differ diff --git a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/graph.svg b/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/graph.svg deleted file mode 100644 index aa4259db..00000000 --- a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/graph.svg +++ /dev/null @@ -1,2678 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - diff --git a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/si/graph-full.png b/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/si/graph-full.png deleted file mode 100644 index 5cfe8472..00000000 Binary files a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/si/graph-full.png and /dev/null differ diff --git a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/si/graph-input.png b/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/si/graph-input.png deleted file mode 100644 index 306d45d9..00000000 Binary files a/docs/pages/2019_SINTEF/sections/include/images/verdi_graph/si/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_SINTEF/sections/include/snippets/__init__.py b/docs/pages/2019_SINTEF/sections/include/snippets/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_SINTEF/sections/include/snippets/demo_bands.py b/docs/pages/2019_SINTEF/sections/include/snippets/demo_bands.py deleted file mode 100644 index 57eaa03a..00000000 --- a/docs/pages/2019_SINTEF/sections/include/snippets/demo_bands.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Compute a band structure with Quantum ESPRESSO - -Uses the PwBandStructureWorkChain provided by aiida-quantumespresso. -""" -from aiida.engine import submit -PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure') - -results = submit( - PwBandStructureWorkChain, - code=Code.get_from_string("qe-6.4.1-pw@localhost"), - structure=load_node(), # REPLACE -) diff --git a/docs/pages/2019_SINTEF/sections/include/snippets/demo_calcjob.py b/docs/pages/2019_SINTEF/sections/include/snippets/demo_calcjob.py deleted file mode 100644 index 278731e3..00000000 --- a/docs/pages/2019_SINTEF/sections/include/snippets/demo_calcjob.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Run SCF calculation with Quantum ESPRESSO""" -from aiida.orm.nodes.data.upf import get_pseudos_from_structure -from aiida.engine import submit - -code = Code.get_from_string("") # REPLACE -builder = code.get_builder() - -# Select structure -structure = load_node() # REPLACE -builder.structure = structure - -# Define calculation -parameters = { - 'CONTROL': { - 'calculation': 'scf', # self-consistent field - }, - 'SYSTEM': { - 'ecutwfc': 30., # wave function cutoff in Ry - 'ecutrho': 240., # density cutoff in Ry - }, -} -builder.parameters = Dict(dict=parameters) - -# Select pseudopotentials -builder.pseudos = get_pseudos_from_structure(structure, '') # REPLACE - -# Define K-point mesh in reciprocal space -KpointsData = DataFactory('array.kpoints') -kpoints = KpointsData() -kpoints.set_kpoints_mesh([4,4,4]) -builder.kpoints = kpoints - -# Set resources -builder.metadata.options.resources = {'num_machines': 1} - -# Submit the job -calcjob = submit(builder) -print('Submitted CalcJob with PK=' + str(calcjob.pk)) diff --git a/docs/pages/2019_SINTEF/sections/include/snippets/workflows_create_diamond_fcc.py b/docs/pages/2019_SINTEF/sections/include/snippets/workflows_create_diamond_fcc.py deleted file mode 100644 index 0ad3d483..00000000 --- a/docs/pages/2019_SINTEF/sections/include/snippets/workflows_create_diamond_fcc.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import absolute_import -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - try: - symbol = element.value - except AttributeError: - symbol = str(element) - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure diff --git a/docs/pages/2019_SINTEF/sections/include/snippets/workflows_create_rescaled.py b/docs/pages/2019_SINTEF/sections/include/snippets/workflows_create_rescaled.py deleted file mode 100644 index c7d0a617..00000000 --- a/docs/pages/2019_SINTEF/sections/include/snippets/workflows_create_rescaled.py +++ /dev/null @@ -1,11 +0,0 @@ -from __future__ import absolute_import -from aiida.engine import workfunction - - -@workfunction -def create_rescaled(element, scale): - """Workfunction to create and immediately rescale a crystal structure of a given element.""" - structure = create_diamond_fcc(element) - rescaled = rescale(structure, scale) - - return rescaled diff --git a/docs/pages/2019_SINTEF/sections/include/snippets/workflows_rescale.py b/docs/pages/2019_SINTEF/sections/include/snippets/workflows_rescale.py deleted file mode 100644 index 99761a77..00000000 --- a/docs/pages/2019_SINTEF/sections/include/snippets/workflows_rescale.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import absolute_import -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_SINTEF/sections/querybuilder.rst b/docs/pages/2019_SINTEF/sections/querybuilder.rst deleted file mode 100644 index 9a878827..00000000 --- a/docs/pages/2019_SINTEF/sections/querybuilder.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _2019_sintef_querybuilder: - -Queries in AiiDA: The Querybuilder -================================== - -This part of the tutorial is provided only in interactive mode through a -Jupyter notebook, which you will be able to run in your browser. -For instructions on starting the Jupyter server, please refer to the -:ref:`setup section`. Once the server is running, -:download:`download this tutorial notebook <../notebooks/querybuilder-tutorial.ipynb>` -and open it in Jupyter. For instructions on downloading these files on -a machine through which you are connected through SSH, refer to -:ref:`this section`. - -The notebook will show you how the ``QueryBuilder`` can be used to query your -database for specific data. It will demonstrate certain concepts and then ask -you to use those to perform certain queries on your own database. Some of these -question cells will have partial solutions that you will have to complete. - -Once you have finished the notebook, you can download a -:download:`notebook with the solutions <../notebooks/querybuilder-solutions.ipynb>` -but try not to use them at first! - -See below for a rendered version of the notebook: - -.. toctree:: - - QueryBuilder Notebook <../notebooks/querybuilder-tutorial.ipynb> diff --git a/docs/pages/2019_SINTEF/sections/setup.rst b/docs/pages/2019_SINTEF/sections/setup.rst deleted file mode 100644 index 5950786f..00000000 --- a/docs/pages/2019_SINTEF/sections/setup.rst +++ /dev/null @@ -1,189 +0,0 @@ -Getting set up -============== - -.. note:: If you are using the Quantum Mobile virtual machine locally inside VirtualBox, you can skip this section. - -.. _2019_sintef_connect: - -Connect to your virtual machine -------------------------------- - -You should each have received from the instructors: - -- an IP address -- a private SSH key ``aiida_tutorial_NUM`` -- a public SSH key ``aiida_tutorial_NUM.pub`` - -The steps below explain how to use these in order to connect to your personal virtual machine (VM) on Amazon Elastic Cloud 2 using the `Secure Shell `_ protocol. -This VM is closely based on the `Quantum Mobile `_ VM and already includes a pre-configured AiiDA installation. - -Linux and MacOS -~~~~~~~~~~~~~~~ - -It's recommended for you to place the ssh key you received in a folder -dedicated to your ssh configuration, to do so: - -- If not already present, create a ``.ssh`` directory in your home - (``mkdir ~/.ssh``), and set its permissions: ``chmod 700 ~/.ssh`` -- Copy the two keys ``aiida_tutorial_NUM`` and ``aiida_tutorial_NUM.pub`` - in the ``~/.ssh`` directory -- Set the correct permissions on the private key: - ``chmod 600 ~/.ssh/aiida_tutorial_NUM``. You can check check with ``ls -l`` - that the permissions of this file are now ``-rw-------``. - -After that ssh key is in place, you can add the following block your -``~/.ssh/config`` file: - - .. code:: bash - - Host aiidatutorial - Hostname IP_ADDRESS - User max - IdentityFile ~/.ssh/aiida_tutorial_NUM - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - ServerAliveInterval 120 - -replacing the IP address (``IP_ADDRESS``) and the ``NUM`` by -the one you received. - -Afterwards you can connect to the server using this simple command: - -.. code:: console - - ssh aiidatutorial - -.. note:: - - Here's a copy-paste ready command for you to use directly with zero - configuration: - - .. code:: console - - ssh \ - -i ~/.ssh/aiida-tutorial-max.pem \ - -L 8888:localhost:8888 \ - -L 5000:localhost:5000 \ - -o ServerAliveInterval=120 \ - -X -C \ - max@IP_ADDRESS - -.. note:: - - On MacOS you need to install `XQuartz `_ - in order to use X-forwarding. - -Windows -~~~~~~~ - -If you're running Windows 10, you may want to consider `installing the Windows Subsystem for Linux `_ (and then follow the instructions above). Alternatively: - -- Install the `PuTTY SSH client `_. - -- Run PuTTYGen - - - Load the ``aiida_tutorial_NN`` private key (button - "Load"). You may need to choose to show "All files (*.*)", - and select the file without any extension (Type: File). - - In the same window, click on "Save private Key", and save the key - with the name ``aiida_tutorial_NN.ppk`` (don't specify a password). - -- Run Pageant - - - It will add a new icon near the clock, in the bottom right of your screen. - - Right click on this Pageant icon, and click on “View Keys”. - - Click on "Add key" and select the ``aiida_tutorial_NN.ppk`` you saved a few steps above. - -- Run PuTTY - - - Put the given IP address as hostname, type ``aiidatutorial`` in "Saved Sessions" - and click "Save". - - Go to Connection > Data and put ``max`` as autologin username. - - Go to Connection > SSH > Tunnels, type ``8888`` in the - "Source Port" box, type ``localhost:8888`` in "Destination" and click "Add". - - Repeat the previous step for port ``5000`` instead of ``8888``. - - Go back to the "Session" screen, select "aiidatutorial" and click "Save" - - Finally, click "Open" (and click "Yes" on the putty security alert - to add the VM to your known hosts). - You should be redirected to a bash terminal on the virtual machine. - -.. note:: - Next time you open PuTTY, select ``aiidatutorial`` and click "Load" - before clicking "Open". - - -In order to enable X-forwarding: - -- Install the `Xming X Server for Windows `_. - -- Configure PuTTy as described in the `Xming wiki `_. - -.. _2019_sintef_setup_jupyter: - -Start jupyter -------------- - -Once connected to your virtual machine, type in the remote terminal - -.. code:: bash - - workon aiida - -This will enable the virtual environment in which AiiDA is installed, -allowing you to use AiiDA. Now type in the same shell: - -.. code:: bash - - jupyter notebook --no-browser - -This will run a server with a web application called ``jupyter``, which -is used to create interactive python notebooks. -In order to connect to the jupyter notebook server: - - - copy the URL that has been printed to the terminal (similar to ``http://localhost:8888/?token=2a3ba37cd1...``) - - open a web browser **on your laptop** and paste the URL - - You will see a list of folders on your personal VM. - -While keeping the first ``ssh`` connection running, open another ``ssh`` -connection in a second terminal and type ``workon aiida`` here too. This -terminal is the one we will actually use in this tutorial. - -.. note:: - - Our SSH configuration assumes that ``jupyter`` will serve the notebooks on port 8888. - If you want to serve notebooks on different ports, you'll also need to adjust - the SSH configuration. - - -.. _2019_sintef_setup_downloading_files: - -Downloading files ------------------ - -Throughout this tutorial, you will encounter links to download python scripts, jupyter notebooks and more. -These files should be downloaded to the environment/working directory you use to run the tutorial. -In particular, when running the tutorial on a linux virtual machine, copy the link address and download the files to the machine using the ``wget`` utility on the terminal: - - wget - -where you replace ```` with the actual HTTPS link that you copied from the tutorial text in your browser. -This will download that file in your current directory. - - -Troubleshooting ---------------- - -- If you get errors ``ImportError: No module named aiida`` or - ``No command ’verdi’ found``, double check that you have loaded the - virtual environment with ``workon aiida`` before launching ``python``, - ``ipython`` or the ``jupyter`` notebook server. - -- If your browser cannot connect to the jupyter notebook server, check that - you have correctly configured SSH tunneling/forwarding as described - above. Keep in mind that you need to start the jupyter server from the - terminal connected to the VM, while the web browser should be opened locally - on your laptop. - -- See the `jupyter notebook documentation `_ for compatibility of jupyter with various web browsers. diff --git a/docs/pages/2019_SINTEF/sections/verdi_cmdline.rst b/docs/pages/2019_SINTEF/sections/verdi_cmdline.rst deleted file mode 100644 index 913737ba..00000000 --- a/docs/pages/2019_SINTEF/sections/verdi_cmdline.rst +++ /dev/null @@ -1,528 +0,0 @@ -Verdi command line -================== - -This part of the tutorial will familiarize you with the ``verdi`` command-line interface (CLI), -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``--help/-h`` flag, e.g.: - - .. code:: bash - - verdi quicksetup -h - -More details on ``verdi`` can be found in the `online documentation `_. - - -.. _2019_sintef_setup_verdi_quicksetup: - -Setting up a profile --------------------- - -After installing AiiDA, the first step is to create a "profile". -Typically, you would be using one profile per independent research project. - -The easiest way of setting up a new profile is through ``verdi quicksetup``. -Let's set up a new profile that we will use throughout this tutorial: - -.. code:: bash - - verdi quicksetup - -This will prompt you for some information, such as the name of the profile, your name, etc. -The information about you as a user will be associated with all the data that you create in AiiDA -and is important for attribution, when you start sharing your data with others. -After you have answered all the questions, a new profile will be created, along -with the required database and repository. - -.. note:: - - ``verdi quicksetup`` is a user-friendly wrapper around the ``verdi setup`` command that provides more control over the profile setup. - As explained in `the documentation `_, ``verdi setup`` expects certain external resources, such as the database to already have been pre-configured. - ``verdi quicksetup`` will try to do this for you, but may not be successful in certain environments. - -To see this profile, and any others that may have been configured, run: - -.. code:: bash - - verdi profile list - - Info: configuration folder: /home/max/.aiida - * generic - quicksetup - -Each line, ``generic`` and ``quicksetup`` in this example, corresponds to a profile. -The one marked with an asterisk is the "default" profile, meaning that any ``verdi`` command that you execute will be applied to that profile. - -.. note:: - - The output you get may differ. - The ``generic`` profile is pre-configured on the virtual machine built for the tutorial (but we are not going to use it here). - -Let's change the default profile to the newly created ``quicksetup`` for the rest of the tutorial: - -.. code:: bash - - verdi profile setdefault quicksetup - -From now on, all ``verdi`` commands will apply to the ``quicksetup`` profile. - -.. note:: - - To quickly perform a single command on a profile that is not the default, use the ``-p/--profile`` option: - For example, ``verdi -p generic code list`` will display the codes for the ``generic`` profile, despite it not being the current default profile. - - -Importing data --------------- - -Before we start running calculations ourselves, we are going to look at an AiiDA database already created by someone else. -Let's import one from the web: - -.. code:: bash - - verdi import https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2019_05_perovskites_v0.3.aiida - -Contrary to most databases, AiiDA databases contain not only *results* of calculations but also their inputs and information on how a particular result was obtained. -This information, the *data provenance*, is stored in the form of a *directed acyclic graph* (DAG). -In the following, we are going to introduce you to different ways of browsing this graph and will ask you to find out some information regarding the database you just imported. - -.. _2019_sintef_aiidagraph: - -Your first AiiDA graph ----------------------- - -:numref:`2019_sintef_fig_graph_input_only` shows a typcial example of a calculation represented in an AiiDA graph. -Have a look to the figure and its caption before moving on. - -.. _2019_sintef_fig_graph_input_only: -.. figure:: include/images/verdi_graph/batio3/graph-input.png - :width: 100% - - Graph with all inputs (data, circles; and code, diamond) to the Quantum ESPRESSO calculation (square) that you will create in the :ref:`calculations` section of this tutorial. - -.. _2019_sintef_fig_graph: -.. figure:: include/images/verdi_graph/batio3/graph-full.png - :width: 100% - - Same as :numref:`2019_sintef_fig_graph_input_only`, but also with the outputs that the engine will create and connect automatically. - The ``RemoteData`` node is created during submission and can be thought as a symbolic link to the remote folder in which the calculation runs on the cluster. - The other nodes are created when the calculation has finished, after retrieval and parsing. - The node with linkname 'retrieved' contains the raw output files stored in the AiiDA repository; all other nodes are added by the parser. - Additional nodes (symbolized in gray) can be added by the parser (e.g. an output ``StructureData`` if you performed a relaxation calculation, a ``TrajectoryData`` for molecular dynamics etc.). - -:numref:`2019_sintef_fig_graph_input_only` was drawn by hand but you can generate a similar graph automatically by passing the **identifier** of a calculation node to ``verdi node graph generate ``. -Identifiers in AiiDA come in three forms: - - * "Primary Key" (PK): An integer, e.g. ``723``, that identifies your entity within your database (automatically assigned) - * `Universally Unique Identifier `_ (UUID): A string, e.g. ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` that identifies your entity globally (automatically assigned) - * Label: A human-readable string, e.g. ``test_qe_calculation`` (manually assigned) - -Any ``verdi`` command that expects an identifier will accept a PK, a UUID or a label (although not all entities have a label by default). -While PKs are often shorter than UUIDs and can be easier to remember, they are only unique within your database. -**Whenever you intend to share your data with others, use UUIDs to refer to nodes.** - -.. note:: - For UUIDs, it is sufficient to specify a subset (starting at the beginning) as long as it can already be uniquely resolved. - For more information on identifiers in ``verdi`` and AiiDA in general, see the `documentation `_. - -For the remainder of this section, fields enclosed in angular brackets, such as ````, are placeholders that you should replace before executing the command. -With that in mind, let's generate a graph for the calculation node with UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``: - -.. code:: bash - - verdi node graph generate - -This command will create the file ``.dot.pdf`` that can be viewed with any PDF document viewer. -You can open this file on the Amazon machine by using ``evince`` or, if the ssh connection is too slow, copy it via ``scp`` to your local machine. -To do so, if you are using Linux/Mac OS X, you can type in your *local* machine: - -.. code:: bash - - scp aiidatutorial: - -and then open the file. -Alternatively, you can use graphical software to achieve the same, for instance: WinSCP on Windows, Cyberduck on the Mac, or the 'Connect to server' option in the main menu after clicking on the desktop for Ubuntu. - - -The provenance browser ----------------------- - -While the ``verdi`` CLI provides full access to the data underlying the provenance graph, a more intuitive tool for browsing AiiDA graphs is the interactive provenance browser available on `Materials Cloud `__. - -In order to use it, we first need to start the `AiiDA REST API `_: - -.. code:: bash - - verdi restapi - * Serving Flask app "aiida.restapi.run_api" (lazy loading) - * Environment: production - WARNING: Do not use the development server in a production environment. - Use a production WSGI server instead. - * Debug mode: off - * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) - -Now you can connect the provenance browser to your local REST API: - -- Open the |provenance_browser| on your laptop -- In the form, paste the (local) URL ``http://127.0.0.1:5000/api/v3`` - of our REST API -- Click "GO!" - -.. |provenance_browser| raw:: html - - provenance explorer - -Once the provenance browser javascript application has been loaded by your browser, it is communicating directly with the REST API and your data never leaves your computer. - -.. note:: - In order for this to work on your laptop, while the REST API is running on the virtual machine, we've enabled SSH tunneling for port ``5000`` in :ref:`2019_sintef_connect`. - -Start by clicking on the Details of a ``CalcJobNode`` and use the graph explorer to complete the exercise below. -If you ever get lost, just go to the "Details" tab, enter ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` and click on the "GO" button. - -.. admonition:: Exercise - - Use the provenance browser in order to figure out: - - - When was the calculation run and who run it? - - Was it a serial or a parallel calculation? How many MPI processes were used? - - What inputs did the calculation take? - - What code was used and what was the name of the executable? - - How many calculations were performed using this code? - - -Processes ---------- - -Anything that 'runs' in AiiDA, be it calculations or workflows, is considered a ``Process``. -To get a list of currently running processes, use: - -.. code:: bash - - verdi process list - -.. note:: - - The first time you run this command, it might take a few seconds. - Subsequent calls will be faster. - -which should be empty: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - - Total results: 0 - - Info: last time an entry changed state: never - -Let's see whether there are any *finished* processes in the database by passing the ``-S/--process-state`` flag: - -.. code:: bash - - verdi process list -S finished - -This command will list all the processes that have a process state ``Finished`` and should look something like: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - 1178 1653D ago PwCalculaton ⏹ Finished [0] - 1953 1653D ago PwCalculaton ⏹ Finished [0] - 1734 1653D ago PwCalculaton ⏹ Finished [0] - 336 1653D ago PwCalculaton ⏹ Finished [0] - 1056 1653D ago PwCalculaton ⏹ Finished [0] - 1369 1653D ago PwCalculaton ⏹ Finished [0] - - Total results: 6 - - Info: last time an entry changed state: never - -Processes can be in any of the following states: - - * ``Created`` - * ``Waiting`` - * ``Running`` - * ``Finished`` - * ``Excepted`` - * ``Killed`` - -The first three states are 'active' states, meaning the process is not done yet, and the last three are 'terminal' states. -Once a process is in a terminal state, it will never become active again. -The `official documentation `_ contains more details on process states. - -In order to list processes of *all* states, use the ``-a/--all`` flag: - -.. code:: bash - - verdi process list -a - -This command will list all the processes that have *ever* been launched. -As your database will grow, so will the output of this command. -To limit the number of results, you can use the ``-p/--past-days `` option, that will only show processes that were created ``NUM`` days ago. -For example, this lists all processes launched since yesterday: - -.. code:: bash - - verdi process list -a -p1 - -.. _2019-aiida-identifiers: - -Each row of the output identifies a process with some basic information about its status. -For a more detailed list of properties, you can use ``verdi process show``, but to address any specific process, you need an identifier for it. - -Let's revisit the process with the UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``, this time using the CLI: - -.. code:: bash - - verdi process show ce81c420-7751-48f6-af8e-eb7c6a30cec - -Producing the output: - -.. code:: bash - - Property Value - ------------- ------------------------------------ - type CalcJobNode - pk 828 - uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 - label - description - ctime 2014-10-27 17:51:21.781045+00:00 - mtime 2019-05-09 14:10:09.307986+00:00 - process state Finished - exit status 0 - computer [1] daint - - Inputs PK Type - ---------- ---- ------------- - pseudos - Ba 611 UpfData - O 661 UpfData - Ti 989 UpfData - code 825 Code - kpoints 811 KpointsData - parameters 829 Dict - settings 813 Dict - structure 27 StructureData - - Outputs PK Type - ----------------------- ---- ------------- - output_kpoints 1894 KpointsData - output_parameters 62 Dict - output_structure 61 StructureData - output_trajectory_array 63 ArrayData - remote_folder 357 RemoteData - retrieved 60 FolderData - -You can use the PKs shown for the inputs and outputs to get more information about those nodes. - -.. warning:: - - Since the inputs and outputs are ``Data`` nodes, not ``Process`` nodes, use ``verdi node show`` instead. - - -Dict and CalcJobNode -~~~~~~~~~~~~~~~~~~~~ - -Let's investigate some of the nodes appearing in the graph. -From the inputs of the process, let's choose the node of type ``Dict`` with input link name ``parameters`` and type in the terminal: - -.. code:: bash - - verdi data dict show - -where ```` is the PK of the node. - -A ``Dict`` contains a dictionary (i.e. key–value pairs), stored in the database in a format ready to be queried. -We will learn how to run queries later on in this tutorial. -The command above will print the content dictionary, containing the parameters used to define the input file for the calculation. -You can compare the dictionary with the content of the raw input file to Quantum ESPRESSO (that was generated by AiiDA) via the command: - -.. code:: bash - - verdi calcjob inputcat - -where you provide the identifier of the calculation node. -Check the consistency of the parameters written in the input file and those stored in the ``Dict`` node. -Even if you don't know the meaning of the input flags of a Quantum ESPRESSO calculation, you should be able to see how the input dictionary has been converted to Fortran namelists. - -The previous command just printed the content of the 'default' input file ``aiida.in``. -To see a list of all the files used to run a calculation (input file, submission script, etc.) instead type: - -.. code:: bash - - verdi calcjob inputls - -Adding a ``--color`` flag allows you to easily distinguish files from folders by a different coloring. -Once you know the name of the file you want to visualize, you can call the ``verdi calcjob inputcat [PATH]`` command specifying the path. -For instance, to see the submission script, you can do: - -.. code:: bash - - verdi calcjob inputcat _aiidasubmit.sh - -StructureData -~~~~~~~~~~~~~ - -Now let us focus on ``StructureData`` objects, which represent a crystal structure. -We can consider for instance the input structure to the calculation we were considering before (it should have the UUID ``3a4b1270``). -Such objects can be inspected interactively by means of an atomic viewer such as the one provided by ``ase``. -AiiDA however supports several other viewers such as ``xcrysden``, ``jmol``, and ``vmd``. -Type in the terminal: - -.. code:: bash - - verdi data structure show --format ase - -to show the selected structure, although it will take a few seconds to appear -You should be able to rotate the view with the right mouse button. - -.. note:: - - If you receive some errors, make sure you started your SSH connection with the ``-X`` or ``-Y`` flag. - -Alternatively, especially if showing them interactively is too slow over SSH, you can export the content of a structure node in various popular formats such as ``xyz`` or ``xsf``. -This is achieved by typing in the terminal: - -.. code:: bash - - # verdi data structure export --format xsf > .xsf - verdi data structure export --format xsf 254e5a86 > 254e5a86.xsf - -You can open the generated ``xsf`` file and observe the cell and the coordinates. -Then, you can then copy ``.xsf`` from the Amazon machine to your local one and then visualize it, e.g. with ``xcrysden`` (if you have it installed): - -.. code:: bash - - xcrysden --xsf .xsf - -Codes and computers -~~~~~~~~~~~~~~~~~~~ - -Let us focus now on the nodes of type ``Code``. -A code represents (in the database) the actual executable used to run the calculation. -Find the identifier of such a node in the graph and type: - -.. code:: bash - - verdi code show - -The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc. -To show a list of all available codes type: - -.. code:: bash - - verdi code list - -If you want to show all codes, including hidden ones and those created by other users, use ``verdi code list -a -A``. -Now, among the entries of the output you should also find the code just shown. - -Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command: - -.. code:: bash - - verdi computer list -a - -The ``-a`` flag shows all computers, also the one imported in your database but that you did not configure, i.e. to which you don't have access. -Details about each computer can be obtained by the command: - -.. code:: bash - - verdi computer show - -Now you have the tools to answer the question: what is the scheduler installed on the computer where the calculations of the graph have run? - -Calculation results -~~~~~~~~~~~~~~~~~~~ - -The results of a calculation can be accessed directly from the calculation node. -Type in the terminal: - -.. code:: bash - - verdi calcjob res - -which will print the output dictionary of the 'scalar' results parsed by AiiDA at the end of the calculation. -Note that this is actually a shortcut for: - -.. code:: bash - - verdi data dict show - -where ``IDENTIFIER`` refers to the ``Dict`` node attached as an output of the calculation node, with link name ``output_parameters``. -By looking at the output of the command, what is the Fermi energy of the calculation with UUID ``ce81c420``? - -Similarly to what you did for the calculation inputs, you can access the output files via the commands: - -.. code:: bash - - verdi calcjob outputls - -and - -.. code:: bash - - verdi calcjob outputcat - -Use the latter to verify that the Fermi energy that you have found in the last step has been extracted correctly from the output file - -.. note:: - - Hint: filter the lines containing the string 'Fermi', e.g. using ``grep``, to isolate the relevant lines - -The results of calculations are stored in two ways: ``Dict`` objects are stored in the database, which makes querying them very convenient, whereas ``ArrayData`` objects are stored on the disk. -Once more, use the command ``verdi data array show `` to determine the Fermi energy obtained from calculation with the UUID ``ce81c420``. -This time you will need to use the identifier of the output ``ArrayData`` of the calculation, with link name ``output_trajectory_array``. -As you might have realized the difference now is that the whole series of values of the Fermi energy calculated after each relax/vc-relax step are stored. -The choice of what to store in ``Dict`` and ``ArrayData`` nodes is made by the parser of ``pw.x`` implemented in the ``aiida-quantumespresso`` plugin. - -(Optional section) Comments -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -AiiDA offers the possibility to attach comments to a any node, in order to be able to remember more easily its details. -Node with UUID prefix ``ce81c420`` should have no comments, but you can add a very instructive one by typing in the terminal: - -.. code:: bash - - verdi node comment add "vc-relax of a BaTiO3 done with QE pw.x" -N - -Now, if you ask for a list of all comments associated to that calculation by typing: - -.. code:: bash - - verdi node comment show - -the comment that you just added will appear together with some useful information such as its creator and creation date. -We let you play with the other options of ``verdi node comment`` command to learn how to update or remove comments. - -AiiDA groups of calculations ----------------------------- - -In AiiDA, calculations (and more generally nodes) can be organized in groups, which are particularly useful to assign a set of calculations or data to a common project. -This allows you to have quick access to a whole set of calculations with no need for tedious browsing of the database or writing complex scripts for retrieving the desired nodes. -Type in the terminal: - -.. code:: bash - - verdi group list -a -A - -to show a list of **all** groups that exist in the database. -Choose the PK of the group named ``tutorial_pbesol`` and look at the calculations that it contains by typing: - -.. code:: bash - - verdi group show - -In this case, we have used the name of the group to organize calculations according to the pseudopotential that has been used to perform them. -Among the rows printed by the last command you will be able to find the calculation we have been inspecting until now. - -If, instead, you want to know all the groups to which a specific node belongs, you can run: - -.. code:: bash - - verdi group list -N/--node diff --git a/docs/pages/2019_SINTEF/sections/verdi_shell.rst b/docs/pages/2019_SINTEF/sections/verdi_shell.rst deleted file mode 100644 index 413e9a09..00000000 --- a/docs/pages/2019_SINTEF/sections/verdi_shell.rst +++ /dev/null @@ -1,419 +0,0 @@ -Verdi shell and AiiDA objects -============================= - -In this section we will use an interactive IPython environment with all the -basic AiiDA classes already loaded. We propose two realizations of such a -tool. The first consists of a special IPython shell where all the AiiDA -classes, methods and functions are accessible. Type in the terminal - -.. code:: bash - - verdi shell - -For all the everyday AiiDA-based operations, i.e. creating, querying, and -using AiiDA objects, the ``verdi shell`` is probably the best tool. In this -case, we suggest that you use two terminals, one for the ``verdi shell`` and -one to execute bash commands. - -The second option is based on Jupyter notebooks and is probably most suitable -to the purposes of our tutorial. Go to the browser where you have opened -``jupyter`` and click ``New`` → ``Python 3`` (top right corner). This will -open an IPython-based Jupyter notebook, made of cells in which you can type -portions of python code. The code will not be executed until you press -``Shift+Enter`` from within a cell. Type in the first cell - -.. code:: ipython - - %aiida - -and execute it. This will set exactly the same environment as the -``verdi shell``. The notebook will be automatically saved upon any -modification and when you think you are done, you can export your notebook in -many formats by going to ``File`` → ``Download as``. We suggest you to have a -look at the drop-down menus ``Insert`` and ``Cell`` where you will find the -main commands to manage the cells of your notebook. - -.. note:: - - The ``verdi shell`` and Jupyter - notebook are completely equivalent. Use either according to your - personal preference. - -You will still sometimes need to type command-line instructions in ``bash`` in -the first terminal you opened. To differentiate these from the commands to be -typed in the ``verdi shell``, the latter will be marked in this document by a -green background, like: - -.. code:: python - - some verdi shell command - -while command-line instructions in ``bash`` to be typed into a terminal will -be written with a blue background: - -.. code:: bash - - some bash command - -Alternatively, to avoid changing terminal, you can execute ``bash`` commands -within the ``verdi shell`` or the notebook by adding an exclamation mark before -the command itself: - -.. code:: ipython - - !some bash command - -.. _2019_sintef_loadnode: - -Loading a node --------------- - -Most AiiDA objects are represented by nodes, identified in the database by its -``PK`` (an integer). You can access a node using the following command -in the shell: - -.. code:: python - - node = load_node(PK) - -Load a node using one of the calculation ``PK`` s visible in the graph you -displayed in the previous section of the tutorial. Then get the energy of the -calculation with the command - -.. code:: python - - node.res.energy - -You can also type - -.. code:: python - - node.res. - -and then press ``TAB`` to see all the available output results of the -calculation. - -Loading specific kinds of nodes -------------------------------- - -Pseudopotentials -~~~~~~~~~~~~~~~~ - -From the graph you generated in section :ref:`2019_sintef_aiidagraph`, -find the ``UUID`` of the pseudopotential files (``UpfData``). Load one of them and -show what elements it corresponds to by typing: - -.. code:: python - - upf = load_node("") - upf.element - -All methods of ``UpfData`` are accessible by typing ``upf.`` and then pressing ``TAB``. - -k-points -~~~~~~~~ - -A set of k-points in the Brillouin zone is represented by an instance of the -``KpointsData`` class. Choose one from the graph of -produced in section :ref:`2019_sintef_aiidagraph`, -load it as ``kpoints`` and inspect its content: - -.. code:: python - - kpoints.get_kpoints_mesh() - -Then get the full (explicit) list of k-points belonging to this mesh using - -.. code:: python - - kpoints.get_kpoints_mesh(print_list=True) - -If this throws an ``AttributeError``, it means that the kpoints instance does not represent a regular mesh but rather a list of k-points defined by their crystal coordinates (typically used when plotting a band structure). -In this case, get the list of k-points coordinates using - -.. code:: python - - kpoints.get_kpoints() - -Conversely, if the `KpointsData` node `does` actually represent a mesh, this method is the one, that when called, will throw an ``AttributeError``. - -If you prefer Cartesian (rather than crystal) coordinates, type - -.. code:: python - - kpoints.get_kpoints(cartesian=True) - -For later use in this tutorial, let us try now to create a kpoints instance, -to describe a regular (2 x 2 x 2) mesh of k-points, centered at the Gamma -point (i.e. without offset). This can be done with the following commands: - -.. code:: python - - KpointsData = DataFactory('array.kpoints') - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh] * 3) - kpoints.store() - -This function loads the appropriate class defined in a string (here -``array.kpoints``). Therefore, ``KpointsData`` is not a class instance, but -the kpoints class itself! - -While it is also possible to import ``KpointsData`` directly, it is recommended -to use the ``DataFactory`` function instead, as this is more future-proof: -even if the import path of the class changes in the future, its entry point -string (``array.kpoints``) will remain stable. - -Parameters -~~~~~~~~~~ - -Dictionaries with various parameters are represented in AiiDA by ``Dict`` nodes. -Get the PK and load the input parameters of a calculation in the graph produced in section :ref:`2019_sintef_aiidagraph`. -Then display its content by typing - -.. code:: python - - params = load_node('') - YOUR_DICT = params.get_dict() - YOUR_DICT - -Modify the python dictionary ``YOUR_DICT`` so that the wave-function cutoff is now set to 20 -Ry. Note that you cannot modify an object already stored in the database. To -write the modified dictionary to the database, create a new object of class ``Dict``: - -.. code:: python - - Dict = DataFactory('dict') - new_params = Dict(dict=YOUR_DICT) - -where ``YOUR_DICT`` is the modified python dictionary. -Note that ``new_params`` is not yet stored in the database. -In fact, typing ``new_params`` in the verdi shell will print a string notifying you of its 'unstored' status. -Let's finish by storing the ``new_params`` dictionary node in the datbase: - -.. code:: python - - new_params.store() - -Structures -~~~~~~~~~~ - -Find a structure in the graph you generated in section :ref:`2019_sintef_aiidagraph` and load it. -Display its chemical formula, atomic positions and species using - -.. code:: python - - structure.get_formula() - structure.sites - -where ``structure`` is the structure you loaded. If you are familiar with ASE -and PYMATGEN, you can convert this structure to those formats by typing - -.. code:: python - - structure.get_ase() - structure.get_pymatgen() - -Let’s try now to define a new structure to study, specifically a silicon -crystal. In the ``verdi shell``, define a cubic unit cell as a 3 x 3 matrix, -with lattice parameter `a`\ :sub:`lat`\ `= 5.4` Å: - -.. code:: python - - alat = 5.4 - the_cell = [[alat/2, alat/2, 0.], [alat/2, 0., alat/2], [0., alat/2, alat/2]] - -.. note:: - - Default units for crystal structure cell and coordinates in AiiDA are Å (Ångström). - -Structures in AiiDA are instances of the class ``StructureData``: load it in the -verdi shell - -.. code:: python - - StructureData = DataFactory('structure') - -Now, initialize the class instance (i.e. the actual structure we want to study) by -the command - -.. code:: python - - structure = StructureData(cell=the_cell) - -which sets the cubic cell defined before. From now on, you can access the cell -with the command - -.. code:: python - - structure.cell - -Finally, append each of the 2 atoms of the cell command. You can do it using -commands like - -.. code:: python - - structure.append_atom(position=(alat/4., alat/4., alat/4.), symbols="Si") - -for the first ‘Si’ atom. Repeat it for the other atomic site (0, 0, 0). You -can access and inspect the structure sites with the command - -.. code:: python - - structure.sites - -If you make a mistake, start over from -``structure = StructureData(cell=the_cell)``, or equivalently use -``structure.clear_kinds()`` to remove all kinds (atomic species) and sites. -Alternatively, AiiDA structures can also be converted directly from ASE -structures [#f1]_ using - -.. code:: python - - from ase.spacegroup import crystal - ase_structure = crystal('Si', [(0, 0, 0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90], primitive_cell=True) - structure = StructureData(ase=ase_structure) - -Now you can store the new structure object in the database with the command: - -.. code:: python - - structure.store() - -Finally, we can also import the silicon structure from an external (online) -repository such as the Crystallography Open Database (COD): - -.. code:: python - - from aiida.tools.dbimporters.plugins.cod import CodDbImporter - importer = CodDbImporter() - for entry in importer.query(formula='Si', spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print("Formula", structure.get_formula()) - print("Unit cell volume: ", structure.get_cell_volume()) - -In that case two duplicate structures are found for 'Si'. -A more in depth tutorial can be found in :ref:`this appendix<2019_sintef_appendix_structure_data>`. - -Accessing inputs and outputs ----------------------------- - -Load again the calculation node used in Section :ref:`2019_sintef_loadnode`: - -.. code:: python - - calc = load_node(PK) - -Then type - -.. code:: python - - calc.inputs. - -and press ``TAB``: you will see all the link names between the calculation and -its input nodes. You can use a specific linkname to access the corresponding -input node, e.g.: - -.. code:: python - - calc.inputs.structure - -Similarly, if you type: - -.. code:: python - - calc.outputs. - -and then ``TAB``, you will list all output link names of the calculation. One -of them leads to the structure that was the input of ``calc`` we loaded -previously: - -.. code:: python - - calc.outputs.output_structure - -Note that links have a single name, that was assigned by the calculation that -used the corresponding input or produced the corresponding output, as -illustrated in section :ref:`2019_sintef_aiidagraph`. - -For a more programmatic approach, you can get a represenation of the inputs and outputs of a node, say ``calc``, through the following methods: - -.. code:: python - - calc_incoming = calc.get_incoming() - calc_outgoing = calc.get_outgoing() - -These methods will return an instance of the ``LinkManager`` class. -You can iterate over the neighboring nodes by calling the ``.all()`` method: - -.. code:: python - - for entry in calc.get_outgoing(): - print(entry.link_label, entry.link_type, entry.node) - -each entry returned by ``.all()`` is a ``LinkTriple``, a named tuple, from which you can get the link label and type and the neighboring node itself. -If you print one, you will see something like: - -.. code:: python - - LinkTriple(node=, link_type=, link_label=u'output_parameters') - -There are many other convenience methods on the ``LinkManager``. -For example if you are only interested in the link labels you can use: - -.. code:: python - - calc.get_outgoing().all_link_labels() - -which will return a list of all the labels of the outgoing links. -Likewise, ``.all_nodes()`` will give you a list of all the nodes to which links are going out from the ``calc`` node. -If you are looking for the node with a specific label, you can use: - -.. code:: python - - calc.get_outgoing().get_node_by_label('output_parameters') - -The ``get_outgoing`` and ``get_incoming`` methods also support filtering on various properties, such as the link label. -For example, if you only want to get the outgoing links whose label starts with ``output``, you can do the following: - -.. code:: python - - calc.get_outgoing(link_label_filter='output%').all_nodes() - - -Pseudopotential families ------------------------- - -Pseudopotentials in AiiDA are grouped in 'families' that contain one single -pseudo per element. We will see how to work with UPF pseudopotentials (the -format used by Quantum ESPRESSO and some other codes). Download and untar the -SSSP pseudopotentials via the commands: - -.. code:: bash - - wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz - -Then you can upload the whole set of pseudopotentials to AiiDA by using the -following ``verdi`` command: - -.. code:: bash - - verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' - -In the command above, ``SSSP_efficiency_pseudos`` is the folder containing the -pseudopotentials, ``'SSSP'`` is the name given to the family, and the last argument -is its description. Finally, you can list all the pseudo families present in -the database with - -.. code:: bash - - verdi data upf listfamilies - -A more in depth tutorial about working with `UpfData` nodes and pseudo potenial families can be found in :ref:`this appendix<2019_sintef_appendix_upf_data>`. - - -.. rubric:: Footnotes - -.. [#f1] We purposefully do not provide advanced commands for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks already exist (such as ASE or pymatgen). diff --git a/docs/pages/2019_SINTEF/sections/workflows.rst b/docs/pages/2019_SINTEF/sections/workflows.rst deleted file mode 100644 index e358f929..00000000 --- a/docs/pages/2019_SINTEF/sections/workflows.rst +++ /dev/null @@ -1,512 +0,0 @@ -Workflows -========= - -The aim of the last part of this tutorial is to introduce the concept of workflows in AiiDA. - -In this section, we will ask you to: - -1. Understand how to keep the provenance when running small python scripts to convert one data object into another (postprocessing, preparation of inputs, etc.) -2. Understand how to represent simple python functions in the AiiDA database -3. Learn how to write a simple workflow in AiiDA (without and with remote calculation submission) -4. Learn how to write a workflow with checkpoints: this means that, even if your workflow requires external calculations to start, they and their dependencies are managed through the daemon. - While you are waiting for the calculations to complete, you can stop and even shutdown the computer in which AiiDA is running. - When you restart, the workflow will continue from where it was. -5. (optional) Go a bit deeper in the syntax of workflows with checkpoints (``WorkChain``), e.g. implementing a convergence workflow using ``while`` loops. - -.. note:: - - This is probably the most 'complex' part of the tutorial. - We suggest that you try to understand the underlying logic behind the scripts, without focusing too much on the details of the workflows implementation or the syntax. - If you want, you can then focus more on the technicalities in a second reading. - -Introduction ------------- - -The ultimate aim of this section is to create a workflow to calculate the equation of state of silicon. -This is a very common task for an *ab initio* researcher. -An equation of state consists in calculating the total energy (E) as a function of the unit cell volume (V). -The minimal energy is reached at the equilibrium volume. -Equivalently, the equilibrium is defined by a vanishing pressure (p=-dE/dV). -In the vicinity of the minimum, the functional form of the equation of state can be approximated by a parabola. -Such an approximation greatly simplifies the calculation of the bulk modulus, that is proportional to the second derivative of the energy (a more advanced treatment requires fitting the curve with, e.g., the Birch–Murnaghan expression). - -The process of calculating an equation of state puts together several operations. -First, we need to define and store in the AiiDA database the basic structure of, e.g., bulk Si. -Next, one has to define several structures with different lattice parameters. -Those structures must be connected between them in the database, in order to ensure that their provenance is recorded. -In other words, we want to be sure that in the future we will know that if we find a bunch of rescaled structures in the database, they all descend from the same one. -How to link two nodes in the database in an easy way is the subject of :ref:`2019_sintef_provenancewf`. - -In the following sections, the newly created structures will then serve as an input for total energy calculations performed, in this tutorial, with Quantum ESPRESSO. -This task is very similar to what you have done in the previous part of the tutorial. -Finally, you will fit the resulting energies as a function of volume to get the bulk modulus. -As the EOS task is very common, we will show how to automate its computation with workflows, and how to deal with both serial and parallel (i.e., independent) execution of multiple tasks. -Finally, we will show how to introduce more complex logic in your workflows such as loops and conditional statements (:ref:`see this section<2019_sintef_convpressure>`), with an example on a convergence loop to find iteratively the minimum of an EOS. - -.. _2019_sintef_provenancewf: - -Process functions: a way to generalize provenance in AiiDA ----------------------------------------------------------- - -Imagine having a function that takes as input a string of the name of a chemical element and generates the corresponding bulk structure as a ``StructureData`` object. -The function might look like the following snippet: - -.. literalinclude:: include/snippets/workflows_create_diamond_fcc.py - :language: python - -For the equation of state you need another function that takes as input a ``StructureData`` object and a rescaling factor, and returns a ``StructureData`` object with the rescaled lattice parameter: - -.. literalinclude:: include/snippets/workflows_rescale.py - :language: python - -In order to generate the rescaled starting structures, say for five different lattice parameters you would combine the two functions. -Open a ``verdi shell``, define the two functions from the previous snippets and enter the following commands: - -.. code:: python - - initial_structure = create_diamond_fcc('Si') - rescaled_structures = [rescale(initial_structure, factor) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and store them in the database: - -.. code:: python - - initial_structure.store() - for structure in rescaled_structures: - structure.store() - -As expected, all the structures that you have created are not linked in any manner as you can verify via the ``get_incoming()``/``get_outgoing()`` methods of the ``StuctureData`` class. -Instead, you would like these objects to be connected as sketched in :numref:`2019_sintef_fig_provenance_process_functions`. - -.. _2019_sintef_fig_provenance_process_functions: -.. figure:: include/images/process_functions.png - - Typical graphs created by using calculation and work functions. - (a) The calculation function ``create_structure`` takes a ``Str`` object as input and returns a single ``StructureData`` object which is used as input for the calculation function ``rescale`` together with a ``Float`` object. - This latter calculation function returns another ``StructureData`` object, defining a crystal with a rescaled lattice constant. - (b) Graph generated by a work function that calls two calculation functions. - A wrapper work function ``create_rescaled`` calls serially the calculation functions ``create_structure`` and ``rescale``. - This relationship is stored via ``CALL`` links. - - -Now that you are familiar with AiiDA, you know that the way to connect two data nodes is through a calculation. -In order to 'wrap' python functions and automate the generation of the needed links, in AiiDA we provide you with what we call 'process functions'. -There are two variants of process functions: - - * calculation functions - * work functions - -These operate mostly the same, but they should be used for different purposes, which will become clear later. -A normal function can be converted to a calculation function by using the ``@calcfunction`` decorator [#f1]_ that takes care of storing the execution as a calculation and adding the links between the input and output data nodes. - -To turn the original functions ``create_diamond_fcc`` and ``rescale`` into calculation functions, simply change the definition as follows: - -.. code:: python - - # Add this import - from aiida.engine import calcfunction - - # Add decorators - @calcfunction - def create_diamond_fcc(element): - ... (same as above) - - @calcfunction - def rescale(structure, scale): - ... (same as above) - -Note that the only change is that the function definitions were 'decorated' with the ``@calcfunction`` line. -This is the only thing that is necessary to transform the plain python functions magically into fully-fledged AiiDA process functions. - -.. note:: - - The only additional change necessary, is that process function input and outputs need to be ``Data`` nodes, so that they can be stored in the database. - AiiDA objects such as ``StructureData``, ``Dict``, etc. carry around information about their provenance as stored in the database. - This is why we must use the special database-storable types ``Float``, ``Str``, etc. as shown in the snippet below. - -Try now to run the following script: - -.. code:: python - - from aiida.orm import Float, Str - initial_structure = create_diamond_fcc(Str('Si')) - rescaled_structures = [rescale(initial_structure, Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and check now that the output of ``initial_structure`` as well as the input of the rescaled structures point to an intermediate node, representing the execution of the calculation function, see :numref:`2019_sintef_fig_provenance_process_functions`. -For instance, you can check that the output links of ``initial_structure`` are the five ``rescale`` calculations: - -.. code:: python - - initial_structure.get_outgoing().all_nodes() - -which outputs - -.. code:: bash - - [, - , - , - , - ] - -and the inputs of each ``CalcFunctionNode`` 'rescale' are obtained with: - -.. code:: python - - for structure in initial_structure.get_outgoing().all_nodes(): - print(structure.get_incoming().all_nodes()) - -that will return - -.. code:: bash - - [, ] - [, ] - [, ] - [, ] - [, ] - -Function nesting -~~~~~~~~~~~~~~~~ -Calculation functions can be 'chained' together by wrapping them together in a work function. -The work function works almost exactly the same as a calculation function, except that it cannot 'create' data, but rather is used to 'call' calculation functions that do the calculations for it. -The calculation functions that it calls will be automatically linked in the provenance graph through 'call' links. -As an example, let us combine the two previously defined calculation functions by means of a wrapper work function called 'create_rescaled' that takes as input the element and the rescale factor. - -Type in your shell (or modify the functions defined in ``create_rescale.py`` and then run): - -.. include:: include/snippets/workflows_create_rescaled.py - :code: python - -and create an already rescaled structure by typing - -.. code:: python - - rescaled = create_rescaled(element=Str('Si'), scale=Float(0.98)) - -Now inspect the input links of ``rescaled``: - -.. code:: ipython - - In [6]: rescaled.get_incoming().all_nodes() - Out[6]: - [, - ] - -The object ``rescaled`` has two incoming links, corresponding to *two* different calculations as input. -These correspond to the calculations 'create_rescaled' and 'rescale' as shown in :numref:`2019_sintef_fig_provenance_process_functions`. -To see the 'call' link, inspect now the outputs of the ``WorkFunctionNode`` which corresponds to the ``create_rescaled`` work function. -Write down its ```` (in general, it will be different from 441), then in the shell load the corresponding node and inspect the outputs: - -.. code:: ipython - - In [12]: node = load_node() - In [13]: node.get_outgoing().all() - -You should be able to identify the two children calculations as well as the final structure (you will see the process nodes linked via CALL links: these are process-to-process links representing the fact that ``create_rescaled`` called two calculation functions). -The graphical representation of what you have in the database should match :numref:`2019_sintef_fig_provenance_process_functions`. - -.. _2019_sintef_sync: - -Run a simple workflow ---------------------- -Let us now use the work and calculation functions that we have just created to build a simple workflow to calculate the equation of state of silicon. -We will consider five different values of the lattice parameter obtained rescaling the experimental minimum, ``a=5.431``, by a factor in ``[0.96, 0.98, 1.0, 1.02, 1.04]``. -We will write a simple script that runs a series of five calculations and at the end returns the volume and the total energy corresponding to each value of the lattice parameter. -For your convenience, besides the functions that you have written so far in the file ``create_rescale.py``, we provide you with some other utilities to get the correct pseudopotential and to generate a pw input file, in :download:`common_wf.py <../scripts/common_wf.py>`. - -We have already created the following script, which you can :download:`download <../scripts/simple_sync_workflow.py>`, but please go through the lines carefully and make sure you understand them. -We suggest that you first have a careful look at it before running it: - -.. include:: ../scripts/simple_sync_workflow.py - :code: python - -If you look into the previous piece of code, you will notice that the way we submit a QE calculation is slightly different from what you have seen in the first part of the tutorial. -The following: - -.. code:: python - - calculations[label] = run(PwCalculation, **inputs) - -runs in the current python session (without the daemon), waits for its completion and returns the output in the user-defined variable ``result``. -The latter is a dictionary whose values are the output nodes generated by the work function, with the link labels as keys. -For example once the function is finished, in order to access the total energy, we need to access the ``Dict`` node which is linked via the 'output_parameters' link (see again Fig. 1 of Day 1 Tutorial, to see inputs and outputs of a Quantum ESPRESSO calculation). -Once the right node is retrieved as ``result['output_parameters']``, we need to get the ``energy`` attribute. -The global operation is achieved by the command - -.. code:: python - - result['output_parameters'].dict.energy - -To collect these results from the various calculations into a single data node, we need one final calculation function to do so. -Since the ``run_eos_wf`` is a 'workflow'-like processes, which cannot create data, this operation **cannot** simply be done in the work function body itself. -To keep the provenance **we have to** do this through a calculation function. -This is done by the ``create_eos_dictionary`` calculation function, that receives as inputs, the output parameters nodes from the completed calculations: - -.. code:: python - - @calcfunction - def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - -It constructs a new ``Dict`` node that contains a single value ``eos`` which is a list of tuples with the relevant data for each calculation. -If you were to inspect the returned data node, with for example ``verdi data dict show`` you would see something like: - -.. code:: bash - - { - "eos": [ - [ - 40.04786949775, - -308.193208771881, - "eV" - ], - [ - 37.6927343883263, - -307.990673492459, - "eV" - ], - [ - 35.4317918679613, - -307.666113525946, - "eV" - ], - [ - 45.048406674717, - -308.308206255253, - "eV" - ], - [ - 42.4991194939683, - -308.293522312459, - "eV" - ] - ] - } - -As you see, the function ``run_eos_wf`` has been decorated as a work function to keep track of the provenance. -To run the workflow it suffices to call the function ``run_eos_wf`` in a python script providing the required input parameters. -For simplicity, we have included few lines at the end of the script that invoke the function with a static choice of parameters: - -.. code:: python - - def run_eos(code=load_code('qe-6.4.1-pw@localhost'), pseudo_family='SSSP', element='Si'): - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - if __name__ == '__main__': - run_eos() - -To get a reference to the node that represents the function execution, we can ask the ``run`` function to return the node, in addition to the results. -Instead of calling the work function to run it, we can use the method ``run_get_node``: - -.. code:: python - - results, node = run_eos_wf.run_get_node(code, Str(pseudo_family), Str(element)) - print('run_eos_wf<{}> completed'.format(node.pk)) - -Run the workflow: - -.. code:: bash - - verdi run simple_sync_workflow.py - -The command above locks the shell until the full workflow has completed (we will see in a moment how to avoid this). -While the function is running, you can use (in a different shell) the command ``verdi process list`` to show ongoing and finished processes. -You can 'grep' for the ```` you are interested in. -Additionally, you can use the command ``verdi process status `` to show the tree of the calculatino functions called by the root work function with a given ````. - -Wait for the work function to finish, then call the function ``plot_eos()`` that we provided in the file ``common_wf.py`` to plot the equation of state and fit it with a Birch–Murnaghan equation. - -.. _2019_sintef_wf_multiple_calcs: - -Run multiple calculations -------------------------- - -You should have noticed that the calculations for different lattice parameters are executed serially, although they might perfectly be executed in parallel because their inputs and outputs are not connected in any way. -In the language of workflows, these calculations are executed in a synchronous (or blocking) way, whereas we would like to have them running *asynchronously* (i.e., in a non-blocking way, to run them in parallel). -One way to achieve this, is to submit the calculation to the daemon using the ``submit`` function. -Make a copy of the script ``simple_sync_workflow.py`` that we worked on in the previous section and name it ``simple_submit_workflow.py``. -To make the new script work asynchronously, simply change the following subset of lines: - -.. code:: python - - from aiida.engine import run - - [...] - - for label, factor in zip(labels, scale_factors): - - [...] - - calculations[label] = run(PwCalculation, **inputs) - - [...] - - inputs = {label: result['output_parameters'] for label, result in calculations.items()} - eos = create_eos_dictionary(**inputs) - - [...] - -replacing them with - -.. code:: python - - from aiida.engine import submit - from time import sleep - - [...] - - for label, factor in zip(labels, scale_factors): - [...] - - # Replace `run` with `submit` - calculations[label] = submit(PwCalculation, **inputs) - - [...] - - # Wait for the calculations to finish - for calculation in calculations.values(): - while not calculation.is_finished: - sleep(1) - - inputs = {label: node.get_outgoing().get_node_by_label('output_parameters') for label, node in calculations.items()} - eos = create_eos_dictionary(**inputs) - -The main differences are: - -- ``run`` is replaced by ``submit`` -- The return value of ``submit`` is not a dictionary describing the outputs of the calculation, but it is the node that represents the execution of that calculation. -- Each calculation starts in the background and calculation nodes are added to the ``calculations`` dictionary. -- At the end of the loop, when all calculations have been launched with ``submit``, another loop is used to wait for all calculations to finish before gathering the results as the final step. - -In the next section we will show you another way to achieve this, which has the added bonus that it introduces checkpoints in the work function, from which the process can be resumed should it be interrupted. -After applying the modifications, run the script. -You will see that all calculations start at the same time, without waiting for the previous ones to finish. - -If in the meantime you run ``verdi process status ``, all five calculations are already shown as output. -Also, if you run ``verdi process list``, you will see how the calculations are submitted to the scheduler. - -.. _2019_sintef_workchainsimple: - -Workchains, or how not to get lost if your computer shuts down or crashes -------------------------------------------------------------------------- - -The simple workflows that we have used so far have been launched by a python script that needs to be running for the whole time of the execution, namely the time in which the calculations are submitted, and the actual time needed by Quantum ESPRESSO to perform the calculation and the time taken to retrieve the results. -If you had killed the main python process during this time, the workflow would not have terminated correctly. -Perhaps you killed the calculation and you experienced the unpleasant consequences: intermediate calculation results are potentially lost and it is extremely difficult to restart a workflow from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way to insert checkpoints, where the main code defining a workflow can be stopped (you can even shut down the machine on which AiiDA is running!). -We call these work functions with checkpoints 'work chains' because, as you will see, they basically amount to splitting a work function in a chain of steps. -Each step is then ran by the daemon, in a way similar to the remote calculations. - -Here below you can find the basic rules that allow you to convert your workfunction-based script to a workchain-based one and a snippet example focusing on the code used to perform the calculation of an equation of state. - -.. include:: ../scripts/equation_of_state.py - :code: python - -.. warning:: - - WorkChains need to be defined in a **separate file** from the script used to run them. - E.g. save your WorkChain in ``workchains.py`` and use ``from workchains import MyWorkChain`` to import it in your script. - - Furthermore, the directory containing the WorkChain definition needs to be in the ``PYTHONPATH`` in order for the AiiDA daemon to find it. - If your ``workchains.py`` sits in ``/home/max/workchains``, add a line ``export PYTHONPATH=$PYTHONPATH:/home/max`` to the ``/home/max/.virtualenvs/aiida/bin/activate`` script, followed by ``verdi daemon restart``. - -- Instead of using decorated functions you need to define a class, inheriting from a prototype class called ``WorkChain`` that is provided by AiiDA in the ``aiida.engine`` module. - -- Within your class you need to implement a ``define`` classmethod that always takes ``cls`` and ``spec`` as inputs. - Here you specify the main information on the workchain, in particular: - - - The *inputs* that the workchain expects. - This is obtained by means of the ``spec.input()`` method, which provides as the key feature the automatic validation of the input types via the ``valid_type`` argument. - The same holds true for outputs, as you can use the ``spec.output()`` method to state what output types are expected to be returned by the workchain. - - - The ``outline`` consisting in a list of 'steps' that you want to run, put in the right sequence. - This is obtained by means of the method ``spec.outline()`` which takes as input the steps. - *Note*: in this example we just split the main execution in two sequential steps, that is, first ``run_eos`` then ``results``. - However, more complex logic is allowed, as will be explained in :ref:`another section<2019_sintef_convpressure>`. - -- You need to split your main code into methods, with the names you specified before into the outline (``run_eos`` and ``results`` in this example). - Where exactly should you split the code? - Well, the splitting points should be put where you would normally block the execution of the script for collecting results in a standard work function, namely whenever you call the method ``.result()``. - Each method should accept only one parameter, ``self``, e.g. ``def step_name(self)``. - -- You will notice that the methods reference the attribute ``ctx`` through ``self.ctx``, which is called the *context* and is inherited from the base class ``WorkChain``. - A python function or process function normally just stores variables in the local scope of the function. - For instance, in the example of :ref:`this subsection<2019_sintef_sync>`, you stored the completed calculations in the ``calculations`` dictionary, that was a local variable. - - In work chains, instead, to preserve variables between different steps, you need to store them in a special dictionary called *context*. - As explained above, the context variable ``ctx`` is inherited from the base class ``WorkChain``, and at each step method you just need to update its content. - AiiDA will take care of saving the context somewhere between workflow steps (on disk, in the database, depending on how AiiDA was configured). - For your convenience, you can also access the value of a context variable as ``self.ctx.varname`` instead of ``self.ctx['varname']``. - -- Any submission within the workflow should not call the normal ``run`` or ``submit`` functions, but ``self.submit`` to which you have to pass the process class, and a dictionary of inputs. - -- The submission in ``run_eos`` returns a future and not the actual calculation, because at that point in time we have only just launched the calculation to the daemon and it is not yet completed. - Therefore it literally is a 'future' result. - Yet we still need to add these futures to the context, so that in the next step of the workchain, when the calculations are in fact completed, we can access them and continue the work. - To do this, we can use the ``ToContext`` class. - This class takes a dictionary, where the values are the futures and the keys will be the names under which the corresponding calculations will be made available in the context when they are done. - See how the ``ToContext`` object is created and returned in ``run_eos``. - By doing this, the workchain will implicitly wait for the results of all the futures you have specified, and then call the next step *only when all futures have completed*. - -- *Return values*: While in normal process functions you attach output nodes to the node by invoking the *return* statement, in a workchain you need to call ``self.out(link_name, node)`` for each node you want to return. - Of course, if you have already prepared a dictionary of outputs, you can just use the following syntax: - - .. code:: python - - self.out_many(retdict) # Keys are link names, value the nodes - - The advantage of this different syntax is that you can start emitting output nodes already in the middle of the execution, and not necessarily at the very end as it happens for normal functions (*return* is always the last instruction executed in a function or method). - Also, note that once you have called ``self.out(link_name, node)`` on a given ``link_name``, you can no longer call ``self.out()`` on the same ``link_name``: this will raise an exception. - -Finally, the workflow has to be run. -For this you have to use the function ``run`` passing as arguments the ``EquationOfState`` class and the inputs as key-value arguments. -For example, you can execute: - -.. code:: python - - from aiida.engine import run - run(EquationOfState, element=Str('Si'), code=load_code('qe-6.4.1-pw@localhost'), pseudo_family=Str('SSSP')) - -While the workflow is running, you can check (in a different terminal) what is happening to the calculations using ``verdi process list``. -You will see that after a few seconds the calculations are all submitted to the scheduler and can potentially run at the same time. - -.. warning:: - - The calculations launched by the work chain will now be submitted to the daemon, instead of run in the same interpreter - However, by using ``run`` on the work chain itself, it will still not be able to restart. - To make the work chain save its checkpoints, you should use the ``submit`` launcher instead. - -As an additional exercise (optional), instead of running the main workflow ``EquationOfState``, try to submit it. -Note that the file where the work chains is defined will need to be globally importable (so the daemon knows how to load it) and you need to launch it (with ``submit``) from a different python file. -The easiest way to achieve this is typically to embed the workflow inside a python package. - -.. note:: - - As good practice, you should try to keep the steps as short as possible in term of execution time. - The reason is that the daemon can be stopped and restarted only between execution steps and not if a step is in the middle of a long execution. - -Finally, as an optional exercise if you have time, you can jump to :ref:`this appendix<2019_sintef_convpressure>`, which shows how to introduce more complex logic into your work chains (if conditionals, while loops etc.). -The exercise will show how to realize a convergence loop to obtain the minimum-volume structure in a EOS using the Newton's algorithm. - -.. note:: - - You might see warnings that say ``Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint``. - These are generated by the ``PwCalculation`` which is unable to save a checkpoint because it is not in a so called 'importable path'. - Simply put, this means that if AiiDA were to try and reload the class it wouldn't know which file to find it in. - To get around this you could simply put the workchain in a different file that is in the 'PYTHONPATH' and then launch it by importing it in your launch file. - In this way AiiDA knows where to find it next time it loads the checkpoint. - - -.. rubric:: Footnotes - -.. [#f1] In simple words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form ``@decorating_function_name`` on the line just before the ``def`` line of the decorated function. 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - -     Main attributes and methods -   - - - - - - - - - - - - - - - The - cheat sheet - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Note: each derived class inherits all the methods of the parent class -   - - - - Node - pklabeluuidctimemtimeget_incoming()get_outgoing()inputsoutputsattributesget_attribute(k)extrasget_extra(<k>)set_extra(<k>,<v>) get_comments()add_comment(<c>) store() Node IDShort labelUnique IDCreation timeModification timeGet inputGet outputAll inputs generatorAll outputs generatorQueryable attributesAttribute 'k'Queryable extrasExtra 'k'Set extra k = vAll commentsAdd comment with content <c>Save node in DB - - - - Code - load_code(<id>) get_builder()  Load code using pk, UUID, or labelReturn new builder using this code  - - - - Data - export()_exportcontent()importfile()importstring()  Export to fileExport to stringImport from fileImport from string - - - - - - Dict - dict.<k>keys()get_dict()set_dict(<dict>)  Get value for key 'k'Get all keys generatorGet all key/valuesReplace all key/values - - - - ArrayData - get_arraynames()get_array(<n>)set_array(<n>,<a>)   Names of all arraysGet array named 'n'Set/store array 'a' with name 'n' - - - - StructureData - cellsiteskinds pbc get_formula()get_cell_volume()convert(<fmt>) set_cell(<c>)set_ase(<a>)set_pymatgen(<p>) append_atom(symbols=<symb>,position=<p>) Lattice vectorsAtomic sitesSpecies with masses, symbols, ...Periodic bound. cond. along each axisChemical formulaCompute cell volumeConvert to ASE, pymatgen, ...Set lattice vectorsCreate cell from ASECreate cell from pymatgenAdd atom of type 'symb' at position 'p'   - - - - - - KpointsData - set_kpoints(<k>)  get_kpoints() set_kpoints_mesh( <m>)get_kpoints_mesh()   Set an explicit list of kpoints 'k' (optionally with weights)Get explicit list of kpts(if stored explicitly)Set an implicit mesh (e.g. 'm'=3x2x5)Get the implicit mesh(if stored implicitly)    - - - CalcJobNode - process_stateexit_statusis_finishedis_failedcomputer inputs.codeget_job_id()get_options() res.<k> Calc. process stateExit status or int codeHas calc. finished?Has calc. failed?Computer where it is runningCode used to runScheduler job IDGet # machines, MPI procs per machine, ...Value of parsed output 'k' v19.05 - - - WorkChainNode - get_state()computer inputs.codeget_job_id()get_options() res.<k> Calculation stateComputer where it is runningCode used to runScheduler job IDGet # machines, MPI procs per machine, ...Value of parsed output 'k' aiida-core v1.0.0b3 - - To load an existing node: load_node(<id>)Where <id> may be either the pk, UUID, or label To load a class, either import it from aiida.orm or use the DataFactory (returning Data subclasses) or the CalculationFactory (returning CalcJobNode subclasses) - - - - - Import aiida-core Node classes from aiida.orm using their class name: from aiida.orm import CalcJobNodefrom aiida.orm import Dict Import Data classes via the DataFactory using <label>s:: KpointsData = DataFactory("array.kpoints")MyData = DataFactory("plugin.my") Other <label>s for Data:"upf", "array", "array.bands", "dict", ... Import CalcJob classes via the CalculationFactory: PwCalculation = CalculationFactory("quantumespresso.pw") Other <label>s for Calculations:"quantumespresso.ph", "vasp.scf", ... Import WorkChain classes via the WorkflowFactory. ORM and the Factories - - - The main AiiDA Node subclasses - - Node - - - CalculationNode - - - Data - - - - - - - WorkFunctionNode - - - - CalcFunctionNode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... - ArrayData - ... - StructureData - UpfData - KpointsData - BandsData - TrajectoryData - WorkflowNode - - - - CalcJobNode - - - - - WorkChainNode - - - - - - - - - - - Code - - - - Dict, Float, Int, ... - - - ProcessNode - - - - - - - Importing classes - Useful links: Tutorial website:aiida-tutorials.readthedocs.io AiiDA documentation:aiida-core.readthedocs.io/en/latest - - - - - - - - Test1 - Test1 - Test1 - Test1 - Test1 - - diff --git a/docs/pages/2019_Xiamen/cheatsheet/required_fonts/HelveticaNeue-otf/HelveticaNeue-Black.otf b/docs/pages/2019_Xiamen/cheatsheet/required_fonts/HelveticaNeue-otf/HelveticaNeue-Black.otf deleted file mode 100644 index 981ea5a1..00000000 Binary files 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19.08.0a1`_ | -+-----------------+--------------------------------------------------------------------------------+ -| codes | `Quantum Espresso 6.4.1`_ | -+-----------------+--------------------------------------------------------------------------------+ - -.. _Quantum Mobile 19.09.0: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.09.0 -.. _aiida-core 1.0.0b6: https://pypi.org/project/aiida-core/1.0.0b6 -.. _aiida-quantumespresso 3.0.0a5: https://github.com/aiidateam/aiida-quantumespresso/releases/tag/v3.0.0a5 -.. _aiidalab 19.08.0a1: https://pypi.org/project/aiidalab/19.8.0a1 -.. _Quantum Espresso 6.4.1: https://github.com/QEF/q-e/releases/tag/qe-6.4.1 - -These are the hands-on materials from the 1-day AiiDA tutorial, part of the `International Workshop on Chemistry and Machine-learning at Xiamen University `__ from September 2-6, 2019. - -While participants of the tutorial used virtual machines on a cloud service, one can follow the tutorial just as well using the Quantum Mobile VirtualBox image linked above. The image already contains all the required software. - -AiiDA hands-on materials -^^^^^^^^^^^^^^^^^^^^^^^^ - -Demo ----- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/setup - ./sections/first_taste - -In-depth tutorial ------------------ - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/verdi_cmdline - ./sections/verdi_shell - ./sections/calculations - ./sections/querybuilder - ./sections/workflows - ./sections/bands - -Appendices ----------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - ./sections/appendix_computer_code_setup - ./sections/appendix_input_validation - ./sections/appendix_restarting_calculations - ./sections/appendix_queries - ./sections/appendix_workflow_logic diff --git a/docs/pages/2019_Xiamen/notebooks/bandstructure.ipynb b/docs/pages/2019_Xiamen/notebooks/bandstructure.ipynb deleted file mode 100644 index 35032823..00000000 --- a/docs/pages/2019_Xiamen/notebooks/bandstructure.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A real world workchain example: electronic band structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%aiida\n", - "\n", - "from datetime import datetime, timedelta\n", - "\n", - "from aiida.tools.dbimporters.plugins.cod import CodDbImporter\n", - "from aiida.engine import launch\n", - "\n", - "PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculating the electronic band structure with an AiiDA workchain\n", - "This tutorial will show how useful a workchain can be in performing a well defined task, such as computing and visualizing the electronic band structure for a simple crystal structure. The goal of this tutorial is not to show you the intricacies of the actual workchain itself, but rather to serve as an example that workchains can simplify standard workflows in computational materials science. The workchain that we will use here will employ Quantum Espresso's pw.x code to calculate the charge densities for several crystal structures and compute a band structure from those. Many choices that normally face the researcher before being able to perform this calculation, such as the selection of suitable pseudo potentials, energy cutoff values, k-point grids and k-point paths along high symmetry points, are now performed automatically by the workchain. All that remains for the user to do is to simply define a structure, pass it to the workchain and sit back!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we import the crystal structure of Al as an example, and run the PwBandStructureWorkChain for that structure. The estimated run time is noted in a comment in the calculation cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading the COD importer so we can directly import structure from COD id's\n", - "importer = CodDbImporter()\n", - "# Make sure here to define the correct codename that corresponds to the pw.x code installed on your machine of choice\n", - "codename = 'qe-6.3-pw@localhost'\n", - "code = Code.get_from_string(codename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Importing example crystal structures from COD to AiiDA structure objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Al COD ID='9008460'\n", - "structure_Al = importer.query(id='9008460')[0].get_aiida_structure()\n", - "\n", - "structure_Al.get_formula()\n", - "\n", - "# The following structure can be used instead of Al, but will take much longer on the AWS machine.\n", - "# CaF2 COD ID='1000043' -- approximately 1/2 hour to run\n", - "# h-BN COD ID='9008997' -- approximately 45 mins to run\n", - "# GaAs COD ID='9008845' -- approximately 2 hours to run " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now we run the bandstructure workchain for the selected structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bandstructure workchain follows the following protocol:\n", - "* Determine the primitive cell of the input structure\n", - "* Run a vc-relax to relax the structure\n", - "* Refine the symmetry of the relaxed structure to ensure the primitive cell is used and run a self-consistent field calculation on it\n", - "* Run a non self-consistent field band structure calculation along a path of high symmetry k-points determined by [seekpath](http://materialscloud.org/tools/seekpath)\n", - "\n", - "Numerical parameters for the default 'theos-ht-1.0' protocol are determined as follows: \n", - "* Suitable pseudopotentials and energy cutoffs are automatically searched from the [SSSP library](http://materialscloud.org/sssp) installed on your machine (it uses the efficiency version 1.1).\n", - "* K-point mesh is selected to have a minimum k-point density of 0.2 Å-1\n", - "* A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations\n", - "\n", - "In case the pseudopotentials are not installed, they can be downloaded in a terminal as:\n", - "\n", - " wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz\n", - " tar -zxvf SSSP_efficiency_pseudos.tar.gz\n", - " \n", - "The protocol looks for a UPF file with a specific hash code, that is unique for each different file. \n", - "You can check that you have the right\n", - "one by performing a search in the database:\n", - "\n", - " qb=QueryBuilder()\n", - " qb.append(UpfData, filters={'attributes.md5':{'==':'cfc449ca30b5f3223ec38ddd88ac046d'}})\n", - " len(qb.all())\n", - "\n", - "'md5' is a searchable attribute of the pseudopotential data object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fermi_energy = results['scf_parameters'].dict.fermi_energy\n", - "results['band_structure'].show_mpl(y_origin=fermi_energy, plot_zero_axis=True)\n", - "\n", - "print(\"\"\"Final crystal symmetry: {spacegroup_international} (number {spacegroup_number})\n", - "Extended Bravais lattice symbol: {bravais_lattice_extended}\n", - "The system has inversion symmetry: {has_inversion_symmetry}\"\"\".format(\n", - " **results['seekpath_parameters'].get_dict()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to use a different pseudopotential family (or version of the family) (for instance [SSSP v1.0](https://archive.materialscloud.org/file/2018.0001/v1/SSSP_efficiency_pseudos.tar.gz) instead of the default SSSP v1.1) you can pass an additional parameter when calling the WorkChain, as follows:\n", - " \n", - " protocol = Dict(dict={\n", - " 'name':'theos-ht-1.0', \n", - " 'modifiers': {\n", - " 'pseudo' : 'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - "\n", - "(note that only some values are accepted for pseudo, that you can find [here](https://github.com/aiidateam/aiida-quantumespresso/blob/b02250146576eb573ccb45d05047075f54853f9d/aiida_quantumespresso/utils/protocols/pw.py#L24))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will take approximately 6 minutes on the tutorial AWS (for Al)\n", - "results = launch.run(\n", - " PwBandStructureWorkChain,\n", - " code=code,\n", - " structure=structure_Al,\n", - " protocol=Dict(dict={\n", - " 'name':'theos-ht-1.0',\n", - " 'modifiers': {\n", - " 'pseudo':'SSSP-efficiency-1.0'\n", - " }\n", - " })\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/pages/2019_Xiamen/notebooks/querybuilder-template.ipynb b/docs/pages/2019_Xiamen/notebooks/querybuilder-template.ipynb deleted file mode 100644 index 65dd70ae..00000000 --- a/docs/pages/2019_Xiamen/notebooks/querybuilder-template.ipynb +++ /dev/null @@ -1,973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AiiDA's QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Import statements - make sure to execute the cell below this one (it may be hidden)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hide_input": false - }, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from datetime import datetime, timedelta\n", - "import numpy as np\n", - "from aiida import load_profile\n", - "from matplotlib import gridspec, pyplot as plt\n", - "load_profile()\n", - "from aiida.orm import load_node, Node, Group, Computer, User, CalcJobNode, Code\n", - "from aiida.plugins import CalculationFactory, DataFactory\n", - "\n", - "PwCalculation = CalculationFactory('quantumespresso.pw')\n", - "StructureData = DataFactory('structure')\n", - "KpointsData = DataFactory('array.kpoints')\n", - "Dict = DataFactory('dict')\n", - "UpfData = DataFactory('upf')\n", - "\n", - "def plot_results(query_res):\n", - " \"\"\"\n", - " :param query_res: The result of an instance of the QueryBuilder\n", - " \"\"\"\n", - " smearing_unit_set,magnetization_unit_set,pseudo_family_set = set(), set(), set()\n", - " # Storing results:\n", - " results_dict = {}\n", - " for pseudo_family, formula, smearing, smearing_units, mag, mag_units in query_res:\n", - " if formula not in results_dict:\n", - " results_dict[formula] = {}\n", - " # Storing the results:\n", - " results_dict[formula][pseudo_family] = (smearing, mag)\n", - " # Adding to the unit set:\n", - " smearing_unit_set.add(smearing_units)\n", - " magnetization_unit_set.add(mag_units)\n", - " pseudo_family_set.add(pseudo_family)\n", - "\n", - " # Sorting by formula:\n", - " sorted_results = sorted(results_dict.items())\n", - " formula_list = next(zip(*sorted_results))\n", - " nr_of_results = len(formula_list)\n", - "\n", - " # Checks that I have not more than 3 pseudo families.\n", - " # If more are needed, define more colors\n", - " #pseudo_list = list(pseudo_family_set)\n", - " if len(pseudo_family_set) > 3:\n", - " raise Exception('I was expecting 3 or less pseudo families')\n", - "\n", - " colors = ['b', 'r', 'g']\n", - "\n", - " # Plotting:\n", - " plt.clf()\n", - " fig=plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None)\n", - " gs = gridspec.GridSpec(2,1, hspace=0.01, left=0.1, right=0.94)\n", - "\n", - " # Defining barwidth\n", - " barwidth = 1. / (len(pseudo_family_set)+1)\n", - " offset = [-0.5+(0.5+n)*barwidth for n in range(len(pseudo_family_set))]\n", - " # Axing labels with units:\n", - " yaxis = (\"Smearing energy [{}]\".format(smearing_unit_set.pop()),\n", - " \"Total magnetization [{}]\".format(magnetization_unit_set.pop()))\n", - " # If more than one unit was specified, I will exit:\n", - " if smearing_unit_set:\n", - " raise ValueError('Found different units for smearing')\n", - " if magnetization_unit_set:\n", - " raise ValueError('Found different units for magnetization')\n", - " \n", - " # Making two plots, the top one for the smearing, the bottom one for the magnetization\n", - " for index in range(2):\n", - " ax=fig.add_subplot(gs[index])\n", - " for i,pseudo_family in enumerate(pseudo_family_set):\n", - " X = np.arange(nr_of_results)+offset[i]\n", - " Y = np.array([thisres[1][pseudo_family][index] for thisres in sorted_results])\n", - " ax.bar(X, Y, width=0.2, facecolor=colors[i], edgecolor=colors[i], label=pseudo_family)\n", - " ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15*index+5)\n", - " ax.set_xlim(-0.5, nr_of_results-0.5)\n", - " ax.set_xticks(np.arange(nr_of_results))\n", - " if index == 0:\n", - " plt.setp(ax.get_yticklabels()[0], visible=False)\n", - " ax.xaxis.tick_top()\n", - " ax.legend(loc=3, prop={'size': 18})\n", - " else:\n", - " plt.setp(ax.get_yticklabels()[-1], visible=False)\n", - " for i in range(0, nr_of_results, 2):\n", - " ax.axvspan(i-0.5, i+0.5, facecolor='y', alpha=0.2)\n", - " ax.set_xticklabels(list(formula_list),rotation=90, size=14, ha='center')\n", - " plt.show()\n", - "\n", - "def generate_query_graph(qh, out_file_name):\n", - "\n", - " def draw_vertice_settings(idx, vertice, **kwargs):\n", - " \"\"\"\n", - " Returns a string with all infos needed in a .dot file to define a node of a graph.\n", - " :param node:\n", - " :param kwargs: Additional key-value pairs to be added to the returned string\n", - " :return: a string\n", - " \"\"\"\n", - " if vertice['entity_type'].startswith('process'):\n", - " shape = \"shape=polygon,sides=4\"\n", - " elif vertice['entity_type'].startswith('data.code'):\n", - " shape = \"shape=diamond\"\n", - " else:\n", - " shape = \"shape=ellipse\"\n", - " filters = kwargs.pop('filters', None)\n", - " additional_string = \"\"\n", - " if filters:\n", - " additional_string += '\\nFilters:'\n", - " for k,v in filters.items():\n", - " additional_string += \"\\n {} : {}\".format(k,v)\n", - "\n", - "\n", - " label_string = \" ('{}')\".format(vertice['tag'])\n", - "\n", - " labelstring = 'label=\"{} {}{}\"'.format(\n", - " vertice['entity_type'], #.split('.')[-2] or 'Node',\n", - " label_string,\n", - " additional_string)\n", - " #~ return \"N{} [{},{}{}];\".format(idx, shape, labelstring,\n", - " return \"{} [{},{}];\".format(vertice['tag'], shape, labelstring)\n", - " nodes = {v['tag']:draw_vertice_settings(idx, v, filters=qh['filters'][v['tag']]) for idx, v in enumerate(qh['path'])}\n", - " links = [(v['tag'], v['joining_value'], v['joining_keyword']) for v in qh['path'][1:]]\n", - "\n", - " with open('temp.dot','w') as fout:\n", - " fout.write(\"digraph G {\\n\")\n", - " for l in links:\n", - " fout.write(' {} -> {} [label=\" {}\"];\\n'.format(*l))\n", - " for _, n_values in nodes.items():\n", - " fout.write(\" {}\\n\".format(n_values))\n", - "\n", - " fout.write(\"}\\n\")\n", - " import os\n", - " os.system('dot temp.dot -Tpng -o {}'.format(out_file_name))\n", - "\n", - "def store_formula_in_extra():\n", - " from aiida.orm import QueryBuilder\n", - " qb = QueryBuilder()\n", - " qb.append(StructureData, filters={'extras':{'!has_key':'formula'}})\n", - " for structure, in qb.all():\n", - " structure.set_extra('formula', structure.get_formula(mode='count'))\n", - "\n", - "store_formula_in_extra()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction to the QueryBuilder\n", - "As you will use `AiiDA` to run your calculations, the database that stores all the data and the provenance will quickly grow to be very large. To help you find the needle that you might be looking for in this big haystack, we need an efficient search tool. `AiiDA` provides a tool to do exactly this: the `QueryBuilder`. The `QueryBuilder` acts as the gatekeeper to your database, to whom you can ask questions about the contents of your database (also referred to as queries), by specifying what are looking for. In this part of the tutorial, we will focus on how to use the `QueryBuilder` to make these queries and understand/use the results.\n", - "\n", - "In order to use the `QueryBuilder`, we first need to import it. We can accomplish this by executing the `import` statement in the following cell. Go ahead and select the next cell, and press `Shift+Enter`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida.orm import QueryBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we can ask the `QueryBuilder` questions about our database, we first need to create an instance of it (i.e., we create a new query):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have an instance of our `QueryBuilder` which we named `qb`, we are ready to start asking about the content of our database. For example, we may want to know exactly how many nodes there are in our database. To tell `qb` that we are interested in all the occurrences of the `Node` class in our database, we `append` it to the list of objects it should find. \n", - "\n", - "*Note*: The method is called `append` because, as we will see later, you can append multiple nodes to a `QueryBuilder` instance consecutively to search in the graph, as if you had a list: what we are doing is querying a graph, and for every vertice of the graph in our subquery, we will use one `append` call. But we'll see this use better in a few steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(Node)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now narrowed down the scope of `qb` to just the nodes that are present in our database. To learn how many nodes there are exactly, all we have to do is to ask `qb` to count them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now as you may have learned in previous sections of the tutorial, nodes come in different kinds and flavors. For example, all our crystal structures that we have stored in the database are saved in nodes that are of the type `StructureData`. If instead of all the nodes, we would rather like to count only the crystal structure nodes, we simply tell our `QueryBuilder` to narrow its scope only to objects of type `StructureData`. Since we want to create a new independent query, we must create a new instance of the `QueryBuilder`. In the next cell, we have typed part of the code to count all the structure nodes. See if you can finish the line with the comment, to tell the `QueryBuilder` that you are only interested in `StructureData` nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "qb.append() # How do we finish this line to tell the query builder to count only the structure nodes?\n", - "qb.count()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.count()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of just counting how many structure nodes we have, we may also actually want to see some of them. This is as easy as telling our `QueryBuilder` that we are not interested in the `count` but rather that we want to retrieve `all` the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this command is very literal and does in fact retrieve **all** the structure nodes that are stored in your database, which may be very slow if your database becomes very large. One solution is to simply tell the `QueryBuilder` that we are, for example, only interested in 5 structure nodes. This can be done with the `limit` method as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another option is to simply use the concept of array slicing, native to python, to specify a subset of the total return set to be returned. Notice that this example can be very slow in big databases. When you want performance, use the functionality native to the QueryBuilder like `limit`, that limit the number of results directly at the database level!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(None)\n", - "qb.all()[:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to know a little bit more about the retrieved structure nodes, we can loop through our results. This allows you, for instance, to print the formula of the structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(StructureData)\n", - "qb.limit(5)\n", - "for structure, in qb.all():\n", - " print(structure.get_formula())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just a simple example how we can employ the `QueryBuilder` to get details about the contents of our database. We have now seen simple queries for the `Node` and `StructureData` classes, but the same rules apply to all the `AiiDA` node classes. For example we may want to count the number of entries for each of the node classes in the following list:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class_list = [Node, StructureData, KpointsData, Dict, UpfData, Code]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the tools we have learned so far, we can build a table of the number of occurrences of each of these node classes that are stored in our database. We simply loop over the `class_list` and create a `QueryBuilder` for each and count the entries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for class_name in class_list:\n", - " qb = QueryBuilder()\n", - " qb.append(class_name)\n", - "#TUT_USER_START\n", - " print() # Finish this line to print the results!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - " print('{:>15} | {:6}'.format(class_name.__name__, qb.count()))\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, you should see something like the following, where of course the numbers may differ for your database\n", - "\n", - "Class name | Entries\n", - "---------------|--------\n", - " Node | 10273 \n", - " StructureData | 271 \n", - " KpointsData | 953 \n", - " Dict | 2922 \n", - " UpfData | 85 \n", - " Code | 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Projection and filters\n", - "Up until now we have always asked the `QueryBuilder` to return the node class. However, we might not necessarily be interested in all the node's properties, but rather just a selected set or even just a single property. We can tell the `QueryBuilder` which properties we would like to be returned, by asking it to **project** those properties in the result. For example, we may only want to get the `uuid`s of a set of nodes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['uuid'])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By using the `project` keyword in the `append` call, we are informing the `QueryBuilder` that we are only interested in the `uuid` property of the `Node` class. Note that the value that we assign to `project` is a list, since we may want to specify more than one property. See if you can get the `QueryBuilder` to return *both* the `id` and the `uuid` of the first 5 `Node`s in the following cell.\n", - "\n", - "**Note**: in the context of the QueryBuilder, the `id` is the `pk` of the node." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_USER_START\n", - "qb.append(Node, project=)#? What should the value be for the project key\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Node, project=['id', 'uuid'])\n", - "#TUT_SOLUTION_END\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To give you an idea of the various properties that you can project for some of the `AiiDA` node classes you can consult the following table.\n", - "Note that this is by no means an exhaustive list:\n", - "\n", - "Class | Properties\n", - "---------|-----------\n", - "Node | `id`, `uuid`, `node_type`, `label`, `description`, `ctime`, `mtime`\n", - "Computer | `id`, `uuid`, `name`, `hostname`, `description`, `transport_type`, `scheduler_type`\n", - "User | `id`, `email`, `first_name`, `last_name`, `institution`\n", - "Group | `id`, `uuid`, `label`, `type_string`, `time`, `description`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The same properties can also be used to *filter* for specific nodes in your database. Indeed, up until now, we only asked the `QueryBuilder` to return *all* the instances of a certain type of node, or at best a limited number of those (without specifying which ones). But in general we might be interested in a very specific node. For example, we may have the `id` of a certain node and we would like to know when it was created and last modified. We can tell the `QueryBuilder` to select nodes that only match that criterion, by telling it to **filter** based on that property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(Node, project=['ctime', 'mtime'], filters={'id': {'==': 1}})\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the syntax of the `filters` keyword. The value is a dictionary, where the keys indicate the node property that it is supposed to operate on, in this case the `id` property. The value of that key is again itself a dictionary, where the key indicates the logical operator `==` and the value corresponds to the value of the property.\n", - "\n", - "You may have multiple criteria that you want to filter for, in which case you can use the logical `or` and `and` operators. Let's say, for example, that you want the `QueryBuilder` to retrieve all the `StructureData` nodes that have a certain `label` **and** were created no longer than 12 days ago. You can translate this criterion by making use of the `and` operator which allows you to specify multiple filters that all have to be satisfied." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(\n", - " Node, \n", - " filters={\n", - " 'and': [\n", - " {'ctime': {'>': datetime.now() - timedelta(days=12)}},\n", - " {'label': {'==':'graphene'}}\n", - " ]\n", - " }\n", - ")\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will have noticed that the `>` operator, and its related operators, can work with python `datetime` objects. These are just a few of the operators that `QueryBuilder` understands. Below you find a table with some of the logical operators that you can use:\n", - "\n", - "Operator | Data type | Example | Description\n", - "---------------------|-----------------------|------------------------------------|------------------\n", - "`==` | all | `{'==': '12'}` | equality operator\n", - "`in` | all | `{'in':['FINISHED', 'PARSING']}` | member of a set\n", - "`<`, `>`, `<=`, `>=` | float, int, datetime | `{'>': 5.2}` | size comparison operator\n", - "`like` | char, str | `{'like': 'calculation%'}` | string comparison, `%` is wildcard\n", - "`ilike` | char, str | `{'ilike': 'caLCulAtion%'}` | string comparison, capital insensitive\n", - "`or` | | `{'or': [{'<': 5.3}, {'>': 6.3}]}` | logical or operator\n", - "`and` | | `{'and': [{'>=': 2}, {'<=': 6}]}` | logical and operator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, try to write a query below that will retrieve all `Group` nodes whose `label` property starts with the string `tutorial`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your query here\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'tutorial%'}})\n", - "qb.limit(5)\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Defining relationships between query clauses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how we can use the `QueryBuilder` to search the database for entries of a specific node type, potentially projecting only specific properties and filtering for certain property values. However, our nodes do not live in a vacuum, but they are part of a graph and thus are linked one another. Therefore, we typically want to be able to search for nodes based on a certain relationship that they might have with other nodes. Consider for example that you have a `StructureData` node that was produced by some calculation. How would we be able to retrieve that calculation?\n", - "\n", - "To accomplish this, we need to be able to tell the `QueryBuilder` what the relationship is between the nodes that we are interested in. With the `QueryBuilder`, we can do the following to find all the structure nodes that have been created as an output by a `PwCalculation` process.\n", - "\n", - "**IMPORTANT NOTE**: In the graph, we are not looking for a `PwCalculation` process (since processes do not live in the graph, as you have learnt). We are actually looking for a `CalcJobNode` whose `process_type` column indicates that it was run by a `PwCalculation` process. Since this is a very common pattern, the `QueryBuilder` allows to directly append the `PwCalculation` process class as a shortcut, but it internally unwraps this into a query for a `CalcJobNode` with the appropriate filter on the `process_type`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are looking for pairs of nodes, we need to `append` the second node as well to the `QueryBuilder` instance. In the next line, to specify the relationship between the nodes, we need to be able to reference back to the `CalcJobNode` that is matched by the previous clause: therefore, we give it a `tag` with the `tag` keyword." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.append(StructureData, with_incoming='calculation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal was to find `StructureData` nodes, so we `append` that to the `qb`. However, we didn't want to find just any `StructureData` nodes; they had to be an output of `PwCalculation`. \n", - "\n", - "Note how we expressed this relation by the `with_incoming` keyword, because we want a `StructureData` node having an *incoming* link from the `CalcJobNode` referenced by the `calculation` tag (i.e., the `StructureData` must be an *output* of the calculation).\n", - "\n", - "Now all we have to do is execute the query to retrieve our structures:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we did can be visualized schematically, thanks to a little tool we have written for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query1.png')\n", - "Image(filename='query1.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `with_incoming` keyword is only one of many potential relationships that exist between the various `AiiDA` nodes and that are implemented in the `QueryBuilder`. The table below gives an overview of the implemented relationships, which nodes they are defined for and what relation it implicates. The full documentation can be found [on this page of the AiiDA documentation](https://aiida-core.readthedocs.io/en/latest/querying/querybuilder/append.html#joining-entities)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entity from\t| Entity to\t| Relationship | Explanation\n", - "------------|-----------|------------------|------------\n", - "Node | Node | with_outgoing | One node as input of another node\n", - "Node | Node | with_incoming | One node as output of another node\n", - "Node | Node | with_descendants | One node as the ancestor of another node\n", - "Node | Node | with_ancestors | One node as descendant of another node\n", - "Group | Node | with_node | The group of a node\n", - "Node | Group | with_group | The node is a member of a group\n", - "Computer | Node | with_node | The computer of a node\n", - "Node | Computer | with_computer | The node of a computer\n", - "User | Node | with_node | The creator of a node is a user\n", - "Node | User | with_user | The node was created by a user" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an exercise, see if you can write a query that will return all the `UpfData` nodes that are a member of a `Group` whose name starts with the string `SSSP`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "qb = QueryBuilder()\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb = QueryBuilder()\n", - "qb.append(Group, filters={'label': {'like': 'SSSP%'}}, tag='group')\n", - "qb.append(UpfData, with_group='group')\n", - "qb.all()\n", - "#TUT_SOLUTION_END\n", - "# Here I also visualize what's going on:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query2.png')\n", - "Image(filename='query2.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Attributes and extras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In section 2, we showed you how you can `project` specific properties of a `Node` and gave a list of properties that a `Node` instance possesses. Since then, we have come across a lot of different `AiiDA` data nodes, such as `StructureData` and `UpfData`, that were secretly `Node`'s in disguise. Or to put it correctly, as `AiiDA` employs the object-oriented programming paradigm, both `StructureData` and `UpfData` are examples of subclasses of the `Node` class and therefore inherit its properties. That means that whatever property a `Node` has, both `StructureData` and `UpfData` will have too. However, there is a semantic difference between a `StructureData` node and a `UpfData`, and so we may want to add a property to one that would not make sense for the other. To solve this, `AiiDA` introduces the concept of `attributes`. These are similar to properties, except that they are specific to the `Node` type that they are attached to. This allows you to add an `attribute` to a certain node, without having to change the implementation of all the others.\n", - "\n", - "For example, the `Dict` nodes that are generated as output of `PwCalculation`'s may have an attribute named `wfc_cutoff`. To project for this particular `attribute`, one can use exactly the same syntax as shown in section 2 for the regular `Node` properties, and one has to only prepend `attributes.` to the attribute name. Demonstration:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation, tag='pw')\n", - "qb.append(Dict, with_incoming='pw', project=[\"attributes.wfc_cutoff\"])\n", - "qb.limit(5)\n", - "qb.all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not every `Dict` node has to have this attribute, in which case the `QueryBuilder` will return the python keyword `None`. Similar to the `attributes`, nodes also can have `extras`, which work in the same way, except that `extras` are mutable, which means that their value can be changed even after a node instance has been stored.\n", - "\n", - "If you are not sure which attributes a given node has, you can use the `.attributes` property to simply retrieve them all. It will return a dictionary with all the attributes the node has.\n", - "\n", - "Note that a node also has a number of additional methods. For instance, you can do `list(node.attributes_keys())` to get only the attribute keys, or `node.get_attribute('wfc_cutoff')` to get the value of a single attribute (these two variants are more efficient if the node has a lot of attributes and you don't need all data). Similarly, for extras, you have `node.extras`, `list(node.extras_keys())`, and `node.get_attribute('SOME_EXTRA_KEY')`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "qb.append(PwCalculation)\n", - "node, = qb.first()\n", - "node.attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chemical-element symbol of a pseudopotential, that is represented by a `UpfData` node, is stored in the `element` attribute. Using the knowledge that filtering on attributes works exactly as for normal node properties, see if you can write a query that will search your database for pseudopotentials for silicon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qb = QueryBuilder()\n", - "#TUT_SOLUTION_START\n", - "qb.append(UpfData, filters={'attributes.element': {'==': 'Si'}})\n", - "qb.all()\n", - "#TUT_SOLUTION_END" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**For more exercises on relationships and attributes/extras, have a look at the appendix section on queries.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 5. A small high-throughput study" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The following section assumes that a specific dataset is present in your current AiiDA profile. If you are not running this script on the Virtual Machine of the AiiDA tutorial, this script will not produce the desired output.\n", - "You can download the Virtual Machine image from [https://aiida-tutorials.readthedocs.io](https://aiida-tutorials.readthedocs.io) along with the tutorial text (choose the correct version of the tutorial, depending on which version of AiiDA you want to try).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part of the tutorial, we will focus on how to systematically retrieve, parse and analyze the results of\n", - "multiple calculations using AiiDA. We know you’re able to do this yourself, but to save time, a set of calculations\n", - "have already been done with AiiDA for you on 57 perovskites, using three different pseudopotential families (LDA,\n", - "PBE and PBESOL, all from GBRV 1.2).\n", - "\n", - "**Note**: if you are not following the tutorial on the official virtual machine that comes with this data, but you are working with your own database, you first have to import this archive (download it from: https://drive.google.com/open?id=1eqJaJr7q1VsYYvGxqHxmDN7gBMs534rz) using `verdi import`\n", - "\n", - "These calculations are spin-polarized (without spin-orbit coupling), use a Gaussian smearing and perform a variable-cell relaxation of the full unit cell. The idea of this part of the tutorial is to “screen” for magnetic and metallic perovskites in a “high-throughput” way. As you learned in the first part of the tutorial, AiiDA allows to organize calculations in groups. Once more check the list of groups in your database by typing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!verdi group list -A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The calculations needed for this task were put in three different groups whose names start with \"tutorial\" (one for each pseudopotential family). The main task is to make a plot showing, for all perovskites and for each pseudopotential family, the total magnetization and the $-TS$ contribution from the smearing to the total energy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start building the query\n", - "So we first of all need to instantiate a QueryBuilder instance. We `append` the groups of interest, which means that we select only groups that start with the string `tutorial_`. We can execute the query after this append (this will not affect the final results) and check whether we have retrieved 3 groups." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiating QB:\n", - "qb = QueryBuilder()\n", - "# Appending the groups I care about:\n", - "qb.append(Group, filters={'label':{'like':'tutorial_%'}}, project='label', tag='group')\n", - "# Visualize:\n", - "print(\"Groups:\", ', '.join([g for g, in qb.all()]))\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query3.png')\n", - "Image(filename='query3.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the calculations that are members of each group" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# I want every PwCalculation that is a member of the specified groups:\n", - "#TUT_USER_START\n", - "qb.append(PwCalculation, tag='calculation', with_group=) # Complete the function call with the correct relationship-tag!\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(PwCalculation, tag='calculation', with_group='group')\n", - "#TUT_SOLUTION_END\n", - "#Visualize\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query4.png') \n", - "Image(filename='query4.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the structures that are input of the calculation. \n", - "We extend the query to include the structures that are input of the calculations that match the query so far.\n", - "This means that we `append` StructureData, and defining the relationship with the calculation with corresponding keyword.\n", - "\n", - "For simplicity the formulas have been added in the extras of each structure node under the key `formula`. \n", - "(The function that does this is called `store_formula_in_extra` and can be found in the top cell of this notebook. It also uses the QueryBuilder!)\n", - "\n", - "Project the formula, stored in the extras under the key `formula`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(StructureData, project=, tag='structure', with_outgoing=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(StructureData, project=['extras.formula'], tag='structure', with_outgoing='calculation')\n", - "#TUT_SOLUTION_END\n", - "# Visualize:\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query5.png')\n", - "Image(filename='query5.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Append the output of the calculation\n", - "Every successful PwCalculation outputs a `Dict` node that stores the parsed results as key-value pairs. You can find these pairs among the attributes of the `Dict` node. To facilitate querying, the parser takes care of storing values always in the same units, and these are documented. For convenience, the units are also added as key/value pairs (with the same key name, but with `_units` appended). \n", - "Extend the query so that also the output `Dict` of each calculation is returned. Project only\n", - "the attributes relevant to your analysis.\n", - "\n", - "In particular, project (in this order):\n", - "\n", - "* The smearing contribution \n", - "* The units of the smearing contribution\n", - "* The magnetization \n", - "* The units of the magnetization\n", - "\n", - "(to know the projection keys, you can try to load one `CalcJobNode` from one of the groups, get its output `Dict` and inspect its `attributes` as discussed before, to see the key-value pairs that have been parsed)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#TUT_USER_START\n", - "# Complete the function call with the correct relationship-tag!\n", - "qb.append(Dict, tag='results', project=['attributes.energy_smearing', ...], with_incoming=)\n", - "#TUT_USER_END\n", - "#TUT_SOLUTION_START\n", - "qb.append(Dict, tag='results',\n", - " project=['attributes.energy_smearing', 'attributes.energy_smearing_units',\n", - " 'attributes.total_magnetization', 'attributes.total_magnetization_units',\n", - " ], with_incoming='calculation'\n", - " )\n", - "#TUT_SOLUTION_END\n", - "\n", - "generate_query_graph(qb.get_json_compatible_queryhelp(), 'query6.png') \n", - "Image(filename='query6.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print the query results\n", - "We can print the results to see if everything worked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = qb.all()\n", - "for item in results:\n", - " print(', '.join(map(str, item)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results\n", - "Getting a long list is not always helpful, and a graph can be much more clear and useful. To help you, we have already prepared a function that visualizes the results of the query. Run the following cell and, if you did everything correctly, you should get a graph with the results of your queries!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_results(results)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/docs/pages/2019_Xiamen/scripts/__init__.py b/docs/pages/2019_Xiamen/scripts/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_Xiamen/scripts/common_wf.py b/docs/pages/2019_Xiamen/scripts/common_wf.py deleted file mode 100644 index 4a75d0ee..00000000 --- a/docs/pages/2019_Xiamen/scripts/common_wf.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper functions.""" -from __future__ import absolute_import -import numpy as np -from aiida.plugins import CalculationFactory, DataFactory -from aiida_quantumespresso.utils.pseudopotential import validate_and_prepare_pseudos_inputs - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def generate_scf_input_params(structure, code, pseudo_family): - """Construct a builder for the `PwCalculation` class and populate its inputs. - - :return: `ProcessBuilder` instance for `PwCalculation` with preset inputs - """ - parameters = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, # Important that this stays to get stress - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - - kpoints = KpointsData() - kpoints.set_kpoints_mesh([2, 2, 2]) - - builder = PwCalculation.get_builder() - builder.code = code - builder.structure = structure - builder.kpoints = kpoints - builder.parameters = Dict(dict=parameters) - builder.pseudos = validate_and_prepare_pseudos_inputs( - structure, pseudo_family=pseudo_family) - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calculation of Silicon with Quantum ESPRESSO" - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - - return builder - - -def birch_murnaghan(V, E0, V0, B0, B01): - """Compute energy by Birch Murnaghan formula.""" - r = (V0 / V)**(2. / 3.) - return E0 + 9. / 16. * B0 * V0 * (r - 1.)**2 * (2. + (B01 - 4.) * (r - 1.)) - - -def fit_birch_murnaghan_params(volumes_, energies_): - """Fit Birch Murnaghan parameters.""" - from scipy.optimize import curve_fit - - volumes = np.array(volumes_) - energies = np.array(energies_) - params, covariance = curve_fit( - birch_murnaghan, - xdata=volumes, - ydata=energies, - p0=( - energies.min(), # E0 - volumes.mean(), # V0 - 0.1, # B0 - 3., # B01 - ), - sigma=None) - return params, covariance - - -def plot_eos(eos_pk): - """ - Plots equation of state taking as input the pk of the ProcessCalculation - printed at the beginning of the execution of run_eos_wf - """ - import pylab as pl - from aiida.orm import load_node - eos_calc = load_node(eos_pk) - - data = [] - for V, E, units in eos_calc.outputs.eos.dict.eos: - data.append((V, E)) - - data = np.array(data) - params, _covariance = fit_birch_murnaghan_params(data[:, 0], data[:, 1]) - - vmin = data[:, 0].min() - vmax = data[:, 0].max() - vrange = np.linspace(vmin, vmax, 300) - - pl.plot(data[:, 0], data[:, 1], 'o') - pl.plot(vrange, birch_murnaghan(vrange, *params)) - - pl.xlabel("Volume (ang^3)") - # I take the last value in the list of units assuming units do not change - pl.ylabel("Energy ({})".format(units)) # pylint: disable=undefined-loop-variable - pl.show() diff --git a/docs/pages/2019_Xiamen/scripts/create_rescale.py b/docs/pages/2019_Xiamen/scripts/create_rescale.py deleted file mode 100644 index 508df377..00000000 --- a/docs/pages/2019_Xiamen/scripts/create_rescale.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -"""Helper CalcFunctions for equation of state.""" -from __future__ import absolute_import -from aiida.engine import calcfunction - - -@calcfunction -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - symbol = element.value - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure - - -@calcfunction -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_Xiamen/scripts/equation_of_state.py b/docs/pages/2019_Xiamen/scripts/equation_of_state.py deleted file mode 100644 index 703dc7e2..00000000 --- a/docs/pages/2019_Xiamen/scripts/equation_of_state.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -"""Equation of State WorkChain.""" -from __future__ import absolute_import -from six.moves import zip - -from aiida.engine import WorkChain, ToContext, calcfunction -from aiida.orm import Code, Dict, Float, Str, StructureData -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -PwCalculation = CalculationFactory('quantumespresso.pw') -scale_facs = (0.96, 0.98, 1.0, 1.02, 1.04) -labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - -@calcfunction -def get_eos_data(**kwargs): - """Store EOS data in Dict node.""" - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -class EquationOfState(WorkChain): - """WorkChain to compute Equation of State using Quantum Espresso.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super(EquationOfState, cls).define(spec) - spec.input('code', valid_type=Code) - spec.input('element', valid_type=Str) - spec.input('pseudo_family', valid_type=Str) - spec.output('initial_structure', valid_type=StructureData) - spec.output('eos', valid_type=Dict) - spec.outline( - cls.run_eos, - cls.results, - ) - - def run_eos(self): - """Run calculations for equation of state.""" - # Create basic structure and attach it as an output - initial_structure = create_diamond_fcc(self.inputs.element) - self.out('initial_structure', initial_structure) - - calculations = {} - - for label, factor in zip(labels, scale_facs): - - structure = rescale(initial_structure, Float(factor)) - inputs = generate_scf_input_params(structure, self.inputs.code, - self.inputs.pseudo_family) - - self.report( - 'Running an SCF calculation for {} with scale factor {}'. - format(self.inputs.element, factor)) - future = self.submit(PwCalculation, **inputs) - calculations[label] = future - - # Ask the workflow to continue when the results are ready and store them in the context - return ToContext(**calculations) - - def results(self): - """Process results.""" - inputs = { - label: self.ctx[label].get_outgoing().get_node_by_label( - 'output_parameters') - for label in labels - } - eos = get_eos_data(**inputs) - - # Attach Equation of State results as output node to be able to plot the EOS later - self.out('eos', eos) diff --git a/docs/pages/2019_Xiamen/scripts/plot_calculation_results.py b/docs/pages/2019_Xiamen/scripts/plot_calculation_results.py deleted file mode 100644 index 628f8a7f..00000000 --- a/docs/pages/2019_Xiamen/scripts/plot_calculation_results.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot results of your calculations.""" -from __future__ import absolute_import -from __future__ import print_function -import sys -from argparse import ArgumentParser -from six.moves import map, range, zip - -import numpy as np -from matplotlib import gridspec, pyplot as plt - - -def plot_results(inputfname, outputfname=None, plotformat=None): # pylint: disable=too-many-locals,too-many-statements,too-many-branches - """ - :param inputfile: the filename of the inputfile - :param (opt) outputfile: - The (optional) name for the outputfile. - :param (opt) format: - The (optional) file format, if not clear from the input file name - - Reads the input file provided by the user. - The format should be comma separated values: - formula, pseudo_family, smearing, smearing_units, magnetization, magnetization_units - - - This order has to be respected! - - If an outputfile is provided, use matplotlib.pyplot.savefig to save the figure - Else, calls matplotlib.pyplot.show to show in screen - """ - - # Reading the input file given to me: - with open(inputfname) as f: - lines = f.readlines() - - # Definining the variables I need to read the units and pseudo families - smearing_unit_set = set() - magnetization_unit_set = set() - pseudo_family_set = set() - - # Storing results: - results_dict = {} - - # Looping through results: - for line in lines: - try: - (formula, pseudo_family, smearing, smearing_units, magnetization, - magnetization_units) = [i.strip() for i in line.split(',')] - smearing, magnetization = list( - map(float, (smearing, magnetization))) - except Exception as e: # pylint: disable=broad-except - print("There was an {} reading line:\n" - "{}" - "Exception: {}\n" - "Skipping this line\n".format(type(e), line, e)) - continue - - # If this is a new formula, not present in results, - # Creating the key-value pair: - if formula not in results_dict: - results_dict[formula] = {} - - # Storing the results: - results_dict[formula][pseudo_family] = (smearing, magnetization) - - # Adding to the unit set: - smearing_unit_set.add(smearing_units) - magnetization_unit_set.add(magnetization_units) - pseudo_family_set.add(pseudo_family) - - # Sorting by formula: - sorted_results = sorted(results_dict.items()) - formula_list = list(zip(*sorted_results))[0] - nr_of_results = len(formula_list) - - # Checks that I have not more than 3 pseudo families. - # If more are needed, define more colors - - if len(pseudo_family_set) > 3: - raise Exception('I was expecting 3 or less pseudo families') - - colors = ['b', 'r', 'g'] - - # Plotting: - gs = gridspec.GridSpec(2, 1, hspace=0.01, left=0.1, right=0.94) - fig = plt.figure(figsize=(16, 9), facecolor='w', edgecolor=None) - - # Defining barwidth - barwidth = 1. / (len(pseudo_family_set) + 1) - offset = [ - -0.5 + (0.5 + n) * barwidth for n in range(len(pseudo_family_set)) - ] - - # Axing labels with units: - yaxis = ("Smearing energy [{}]".format(smearing_unit_set.pop()), - "Total magnetization [{}]".format(magnetization_unit_set.pop())) - # If more than one unit was specified, I will exit: - if smearing_unit_set: - raise Exception('Found different units for smearing') - if magnetization_unit_set: - raise Exception('Found different units for magnetization') - - # Making two plots, one for the smearing, the lower for magnetization - for index in range(2): - ax = fig.add_subplot(gs[index]) - for i, pseudo_family in enumerate(pseudo_family_set): - X = np.arange(nr_of_results) + offset[i] - Y = np.array([ - thisres[1][pseudo_family][index] for thisres in sorted_results - ]) - - ax.bar(X, - Y, - width=0.2, - facecolor=colors[i], - edgecolor=colors[i], - label=pseudo_family) - ax.set_ylabel(yaxis[index], fontsize=14, labelpad=15 * index + 5) - ax.set_xlim(-0.5, nr_of_results - 0.5) - ax.set_xticks(np.arange(nr_of_results)) - - if index: - plt.setp(ax.get_yticklabels()[-1], visible=False) - else: - plt.setp(ax.get_yticklabels()[0], visible=False) - ax.xaxis.tick_top() - ax.legend(loc=3, prop={'size': 18}) - - for i in range(0, nr_of_results, 2): - ax.axvspan(i - 0.5, i + 0.5, facecolor='y', alpha=0.2) - ax.set_xticklabels(list(formula_list), - rotation=90, - size=14, - ha='center') - - if outputfname is not None: - plt.savefig(outputfname, format=plotformat) - else: - plt.show() - - -if __name__ == '__main__': - parser = ArgumentParser() - parser.add_argument('inputfile', - help='The input file with the data to plot') - parser.add_argument('-o', - '--outputfile', - help='The outputfile to plot results to', - default=None) - parser.add_argument( - '-f', - '--format', - default=None, - help= - 'The format to plot, if outputfile is specified and no valid extension is provided' - ) - parsed_args = parser.parse_args(sys.argv[1:]) - plot_results(inputfname=parsed_args.inputfile, - outputfname=parsed_args.outputfile, - plotformat=parsed_args.format) diff --git a/docs/pages/2019_Xiamen/scripts/plot_convergence_pressure.py b/docs/pages/2019_Xiamen/scripts/plot_convergence_pressure.py deleted file mode 100644 index 725133d3..00000000 --- a/docs/pages/2019_Xiamen/scripts/plot_convergence_pressure.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- -"""Plot parabola using matplotlib.""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import range -from aiida.orm import load_node - - -def parabola(x, a, b, c): - """Parabola.""" - return a * x**2 + b * x + c - - -def show_parabola_results(pk): # pylint: disable=too-many-locals - """Plot (and show) parabola using matplotlib.""" - out_p = load_node(pk).outputs.steps.get_dict() - - step0 = out_p['step0'] - steps = out_p['steps'] - - import numpy as np - import pylab as pl - - data = [] - data.append((step0['V'], step0['E'])) - for step in steps: - data.append((step['V'], step['E'])) - - data = np.array(data) - min_V = data[:, 0].min() - max_V = data[:, 0].max() - - min_E = data[:, 1].min() - max_E = data[:, 1].max() - - min_V -= 10. - max_V += 10. - min_E -= 0.1 - max_E += 0.5 - - V_range = np.linspace(min_V, max_V, 500) - - for subplot in range(2): - pl.subplot(1, 2, 1 + subplot) - - pl.plot(data[:1, 0], data[:1, 1], 'o', color='red') - pl.annotate('0', xy=(data[0, 0], data[0, 1])) - - pl.plot(data[1:, 0], data[1:, 1], 'o', color='blue') - - color = 0.8 - for idx, step in enumerate(steps, start=1): - pl.plot(V_range, - parabola(V_range, step['a'], step['b'], step['c']), - color=(color, color, color)) - parabola_Vmin = -step['b'] / 2. / step['a'] - parabola_Emin = parabola(parabola_Vmin, step['a'], step['b'], - step['c']) - pl.plot([parabola_Vmin], [parabola_Emin], - 'x', - color=(color, color, color)) - pl.annotate(str(idx), xy=(step['V'], step['E'])) - pl.axvline(step['V'], color=(1., 0.7, 1.), linestyle='--') - color -= 0.25 - if color <= 0.: - color = 0. - - pl.axis([min_V, max_V, min_E, max_E]) - if subplot == 1: - # some zoom - pl.xlim((41.5, 44.)) - pl.ylim((-259.47, -259.45)) - pl.xlabel('Volume (ang^3)') - pl.ylabel('Energy (eV)') - - pl.show() - - -if __name__ == '__main__': - import sys - try: - pk_value = int(sys.argv[1]) - except (IndexError, ValueError): - print( - 'Pass as parameter the PK of the WorkChain calculating the pressure', - file=sys.stderr) - print('convergence to generate the plot.', file=sys.stderr) - sys.exit(1) - show_parabola_results(pk_value) diff --git a/docs/pages/2019_Xiamen/scripts/pressure_convergence.py b/docs/pages/2019_Xiamen/scripts/pressure_convergence.py deleted file mode 100644 index 89f42195..00000000 --- a/docs/pages/2019_Xiamen/scripts/pressure_convergence.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding: utf-8 -*- -"""Pressure convergence WorkChain""" -from __future__ import absolute_import -from __future__ import print_function -from aiida.engine import WorkChain, ToContext, while_, calcfunction, workfunction -from aiida.orm import Code, Float, Str, StructureData -from aiida.plugins import CalculationFactory, DataFactory - -from common_wf import generate_scf_input_params -from create_rescale import rescale - -Dict = DataFactory('dict') -KpointsData = DataFactory('array.kpoints') -PwCalculation = CalculationFactory('quantumespresso.pw') - - -def get_energy_first_derivative(stress): - """Return the energy first derivative from the stress. - - :param stress: the stress tensor in GPa - :return: first derivative of the energy in eV/Å^3 - """ - from numpy import trace - - GPa_to_eV_over_ang3 = 1. / 160.21766208 - - # Get the pressure (GPa) - pressure = trace(stress) / 3. - - # Pressure is -dE/dV; moreover p in kbar, we need to convert it to eV/Å^3 to be consistent - dE = -pressure * GPa_to_eV_over_ang3 - - return dE - - -def get_volume_energy_and_derivative(output_parameters): - """Return the volume, energy and energy derivative for given `PwCalculation` output parameters. - - Volume is in Å^3, energy in eV and energy derivative eV/Å^3 - - :param output_parameters: the `output_parameters` `Dict` result node of a `PwCalculation` - :return: tuple with volume, energy and energy derivative - """ - V = output_parameters.dict.volume - E = output_parameters.dict.energy - dE = get_energy_first_derivative(output_parameters.dict.stress) - - return V, E, dE - - -def get_energy_second_derivative(output_parameters_one, output_parameters_two): - """Return second derivative of the energy with respect to the volume given the results of two `PwCalculations`. - - The derivate is computed using the finite differences method. - - :param output_parameters_one: the `output_parameters` `Dict` result node of the first `PwCalculation` - :param output_parameters_two: the `output_parameters` `Dict` result node of the second `PwCalculation` - :return: the second derivative of the energy with respect to the volume - """ - dE1 = get_energy_first_derivative(output_parameters_one.dict.stress) - dE2 = get_energy_first_derivative(output_parameters_two.dict.stress) - V1 = output_parameters_one.dict.volume - V2 = output_parameters_two.dict.volume - - return (dE2 - dE1) / (V2 - V1) - - -def get_parabola_coefficients(V, E, dE, ddE): - """Return coefficients of a parabola fit to E = a*V^2 + b*V + c for the given volume and energy. - - :param V: volume in Å^3 - :param E: energy in eV - :param dE: the first derivative of the energy with respect to the volume - :param ddE: the second derivative of the energy with respect to the volume - :return: coefficients a, b and c - """ - a = ddE / 2. - b = dE - ddE * V - c = E - V * dE + V**2 * ddE / 2. - - return a, b, c - - -@workfunction -def get_structure(structure, step_data=None): - """Return a scaled version of the given structure, where the new volume is determined by the given step data.""" - initial_volume = structure.get_cell_volume() - - if step_data is None: - new_volume = initial_volume + 4. # In Å^3 - else: - # Minimum of a parabola - new_volume = -step_data.dict.b / 2. / step_data.dict.a - - scale_factor = (new_volume / initial_volume)**(1. / 3.) - scaled_structure = rescale(structure, Float(scale_factor)) - return scaled_structure - - -@calcfunction -def get_step_data(parameters_first, parameters_second=None): - """Generate a dictionary with the step parameters from the output parameters of a completed `PwCalculation`.""" - - # If the parameters of the second calculation are not passed, this is for the first step and we only return - # a dictionary with the volume, energy and derivative - if parameters_second is None: - V, E, dE = get_volume_energy_and_derivative(parameters_first) - return Dict(dict={'V': V, 'E': E, 'dE': dE}) - - # Otherwise, this is an iteration step and we return the difference between the steps - V, E, dE = get_volume_energy_and_derivative(parameters_first) - ddE = get_energy_second_derivative(parameters_first, parameters_second) - a, b, c = get_parabola_coefficients(V, E, dE, ddE) - - return Dict(dict={ - 'V': V, - 'E': E, - 'dE': dE, - 'ddE': ddE, - 'a': a, - 'b': b, - 'c': c - }) - - -@calcfunction -def bundle_step_data(step0, **kwargs): - """Bundle step data into Dict.""" - steps = [step.get_dict() for step in kwargs.values()] - print((step0, type(step0))) - print((steps, type(steps))) - return Dict(dict={'step0': step0.get_dict(), 'steps': steps}) - - -class PressureConvergence(WorkChain): - """Relax a structure using Newton's algorithm on the first derivative of the energy (minus the pressure).""" - - @classmethod - def define(cls, spec): - """Define spec of WorkChain.""" - # yapf: disable - # pylint: disable=bad-continuation - super(PressureConvergence, cls).define(spec) - spec.input('code', valid_type=Code, - help='Code setup to run `pw.x` to use for the calculations.') - spec.input('structure', valid_type=StructureData, - help='The structure to minimize.') - spec.input('pseudo_family', valid_type=Str, - help='Family of pseudopotentials to use for the calculations.') - spec.input('volume_tolerance', valid_type=Float, - help='Stop if the volume difference of two consecutive calculations is less than this threshold.') - spec.output('steps', valid_type=Dict, - help='The data of all the steps in the minimization process containing info about energy and volume') - spec.output('structure', valid_type=StructureData, - help='Final relaxed structure.') - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - - def setup(self): - """Launch the first calculation for the input structure, and a second calculation for a shifted volume.""" - scaled_structure = get_structure(self.inputs.structure) - self.ctx.last_structure = scaled_structure - - inputs0 = generate_scf_input_params(self.inputs.structure, - self.inputs.code, - self.inputs.pseudo_family) - inputs1 = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - - # Run two `PwCalculations` - future0 = self.submit(PwCalculation, **inputs0) - future1 = self.submit(PwCalculation, **inputs1) - - # Wait for them to complete before going to the next step - return ToContext(r0=future0, r1=future1) - - def put_step0_in_ctx(self): - """Store the outputs of the very first step in a specific dictionary.""" - self.ctx.step0 = get_step_data(self.ctx.r0.outputs.output_parameters) - - # Prepare the list containing the steps: step 1 will be stored here by move_next_step - self.ctx.steps = [] - - def move_next_step(self): - """Main part of the algorithm. - - Compare the results of two consecutive calculations and use Newton's algorithm on the pressure by fitting the - results with a parabola and setting the next volume to calculate to the parabola minimum. - - The oldest calculation gets replaced by the most recent and a new calculation is launched that will replace - the most recent. - """ - # Computer the new Volume using Newton's algorithm and create the new corresponding structure by scaling it - new_step_data = get_step_data(self.ctx.r0.outputs.output_parameters, - self.ctx.r1.outputs.output_parameters) - scaled_structure = get_structure(self.inputs.structure, new_step_data) - self.ctx.steps.append(new_step_data) - - # Replace the older step with the latest and set the current structure - self.ctx.r0 = self.ctx.r1 - self.ctx.last_structure = scaled_structure - - inputs = generate_scf_input_params(scaled_structure, self.inputs.code, - self.inputs.pseudo_family) - future = self.submit(PwCalculation, **inputs) - - return ToContext(r1=future) - - def not_converged(self): - """Return True if the worflow is not converged yet (i.e. the volume changed significantly).""" - r0_out = self.ctx.r0.outputs.output_parameters - r1_out = self.ctx.r1.outputs.output_parameters - - return abs(r1_out.dict.volume - - r0_out.dict.volume) > self.inputs.volume_tolerance - - def finish(self): - """Attach the result nodes as outputs.""" - steps = { - 'step{}'.format(index + 1): step - for index, step in enumerate(self.ctx.steps) - } - bundled_steps = bundle_step_data(step0=self.ctx.step0, **steps) - - self.out('steps', bundled_steps) - self.out('structure', self.ctx.last_structure) diff --git a/docs/pages/2019_Xiamen/scripts/simple_sync_workflow.py b/docs/pages/2019_Xiamen/scripts/simple_sync_workflow.py deleted file mode 100644 index acf01b36..00000000 --- a/docs/pages/2019_Xiamen/scripts/simple_sync_workflow.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -"""Simple workflow example""" -from __future__ import absolute_import -from __future__ import print_function -from six.moves import zip - -from aiida.engine import run, Process, calcfunction, workfunction -from aiida.orm import load_code, Dict, Float, Str -from aiida.plugins import CalculationFactory - -from create_rescale import create_diamond_fcc, rescale -from common_wf import generate_scf_input_params - -# Load the calculation class 'PwCalculation' using its entry point 'quantumespresso.pw' -PwCalculation = CalculationFactory('quantumespresso.pw') - - -@calcfunction -def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) - for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - - -@workfunction -def run_eos_wf(code, pseudo_family, element): - """Run an equation of state of a bulk crystal structure for the given element.""" - - # This will print the pk of the work function - print('Running run_eos_wf<{}>'.format(Process.current().pid)) - - scale_factors = (0.96, 0.98, 1.0, 1.02, 1.04) - labels = ['c1', 'c2', 'c3', 'c4', 'c5'] - - calculations = {} - - # Create an initial bulk crystal structure for the given element, using the calculation function defined earlier - initial_structure = create_diamond_fcc(element) - - # Loop over the label and scale_factor pairs - for label, factor in list(zip(labels, scale_factors)): - - # Generated the scaled structure from the initial structure - structure = rescale(initial_structure, Float(factor)) - - # Generate the inputs for the `PwCalculation` - inputs = generate_scf_input_params(structure, code, pseudo_family) - - # Launch a `PwCalculation` for each scaled structure - print('Running a scf for {} with scale factor {}'.format( - element, factor)) - calculations[label] = run(PwCalculation, **inputs) - - # Bundle the individual results from each `PwCalculation` in a single dictionary node. - # Note: since we are 'creating' new data from existing data, we *have* to go through a `calcfunction`, otherwise - # the provenance would be lost! - inputs = { - label: result['output_parameters'] - for label, result in calculations.items() - } - eos = create_eos_dictionary(**inputs) - - # Finally, return the results of this work function - result = {'initial_structure': initial_structure, 'eos': eos} - - return result - - -def run_eos(code=load_code('qe-6.3-pw@localhost'), - pseudo_family='SSSP', - element='Si'): - """Helper function to run EOS WorkChain.""" - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - -if __name__ == '__main__': - run_eos() diff --git a/docs/pages/2019_Xiamen/sections/appendix_computer_code_setup.rst b/docs/pages/2019_Xiamen/sections/appendix_computer_code_setup.rst deleted file mode 100644 index 8fde7b4b..00000000 --- a/docs/pages/2019_Xiamen/sections/appendix_computer_code_setup.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _2019_xmn_appendix_computer_code_setup: - -Setting up computers and codes -============================== - -To setup a computer, please refer to the `online documentation `_. -Instructions to setup a code for a computer can be `found here `_. diff --git a/docs/pages/2019_Xiamen/sections/appendix_input_validation.rst b/docs/pages/2019_Xiamen/sections/appendix_input_validation.rst deleted file mode 100644 index f41e6e0c..00000000 --- a/docs/pages/2019_Xiamen/sections/appendix_input_validation.rst +++ /dev/null @@ -1,94 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Calculation input validation -============================ - -This appendix shows additional ways to debug errors with Quantum ESPRESSO, namely -how to take advantage of a powerful tool to validate the input to Quantum ESPRESSO -and possibly suggest the correct name to misspelled keywords. - -There are various reasons why you might end up providing a wrong input to a Quantum ESPRESSO calculation. -Let's check for example this input dictionary, where we inserted two mistakes: - -.. code:: python - - parameters_dict = { - 'CTRL': { - 'calculation': 'scf', - 'restart_mode': 'from_scratch', - }, - 'SYSTEM': { - 'nat': 2, - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } - -The two mistakes in the dictionary are the following: - -- We wrote a wrong namelist name (``CTRL`` instead of ``CONTROL``). -- We inserted the number of atoms explicitly: while that is indeed how the - number of atoms is specified in Quantum ESPRESSO, in AiiDA this key is reserved by the system, - since this information is already contained within the StructureData. - -After we correct the dictionary ``parameters_dict``, we could run the calculation directly. -Alternatively, we can use a tool of the the `aiida-quantumespresso` plugin, -the 'input helper', which checks the inputs before submitting the calculation. - -In your script, after you define ``parameters_dict``, -you can validate it with the following command (note that you need to pass also an input crystal structure, -``structure``, to allow the validator to perform all the needed checks): - -.. code:: python - - PwCalculation = CalculationFactory('quantumespresso.pw') - validated_dict = PwCalculation.input_helper(parameters_dict, structure=structure) - parameters = Dict(dict=validated_dict) - - -The ``input_helper`` method will check for the correctness of the input parameters. -If misspelling or incorrect keys are detected, the method raises an exception, -which stops the script before submitting the calculation and thus allows for a more effective debugging. - -With this utility, you can also provide a list of keys, -without providing the namelists (useful if you don't remember where to put some variables in the input file). -Hence, you can provide to ``input_helper`` a dictionary like this one: - -.. code:: python - - parameters_dict = { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - 'ecutwfc': 30., - 'ecutrho': 200., - 'conv_thr': 1.e-6, - } - - validated_dict = PwCalculation.input_helper( - parameters_dict, - structure=structure, - flat_mode=True - ) - -If you print ``validated_dict``, it will look like the following: - -.. code:: python - - { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-6, - } - } diff --git a/docs/pages/2019_Xiamen/sections/appendix_queries.rst b/docs/pages/2019_Xiamen/sections/appendix_queries.rst deleted file mode 100644 index 146669d7..00000000 --- a/docs/pages/2019_Xiamen/sections/appendix_queries.rst +++ /dev/null @@ -1,89 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned in earlier parts of the tutorial. Go through them only if you have time.* - -Queries in AiiDA - Examples and Exercises -========================================= - -Optional exercises on relationships (Task 3) --------------------------------------------- - -**Hint for the exercises:** - -- You can have projections on properties of more than one entity in your query. - You just have to add the ``project`` key (specifying the list of properties that you want to project) along with the corresponding entity when you append it. - -**Exercises:**   Try to write the following queries: - -- Find all descendants of a ``StructureData`` with a certain ``uuid`` (descendants indicate all nodes that can be reached by following outgoing links starting from a given node, with any number of hops). - For every match, print both the ``StructureData`` node and the descendant. - -- Find all the ``FolderData`` nodes created by a specific user. - -Optional exercises on attributes and extras (Task 4) ----------------------------------------------------- - -**Hint for the exercises:** - -- You can easily order or limit the number of the results by using the ``order_by()`` and ``limit()`` methods of the QueryBuilder. - For example, we can order all our ``CalcJobNode``s by their ``id`` and limit the result to the first 10 as follows: - - .. code:: python - - qb = QueryBuilder() - qb.append(CalcJobNode, tag='calc') - qb.order_by({'calc': 'id'}) - qb.limit(10) - qb.all() - -**Exercises:** - -- Write a code snippet to check how many pseudopotentials you have for each element. - -- Smearing contribution to the total energy for calculations: - - 1. Write a query that returns the smearing contribution to the energy stored in some instances of ``Dict`` output by a ``CalcJobNode`` generated by running a ``PwCalculation`` process. - 2. Extend the previous query to also get the input structures of the ``CalcJobNode``. - 3. Which structures have a smearing contribution to the energy smaller or equal to ``-0.02``? - -Summarizing what we learned by now - An example ------------------------------------------------ - -At this point you should be able to do queries with projections, joins and filters. -Moreover, you saw how to apply filters and projections even on attributes and extras. -Let's discover the full power of the ``QueryBuilder`` with a complex graph query that allows you to project various properties from different nodes and apply different filters and joins. - -Imagine that you would like to get the smearing energy for all the calculations that have a smearing energy below a given value and have a Sn\ :sub:`2`\ O\ :sub:`3` as input. -Moreover, besides the smearing energy, you would like to print the units of this energy and the formula of the structure that was given to the calculation. -The graphical representation of this query can be seen in :numref:`fig_qb_example_2_2019` and the actual query follows: - -.. _2019_xmn_fig_qb_example_2_2019: -.. figure:: include/images/qb_example_2.png - :width: 50% - :align: center - - Complex graph query. - -.. code:: python - - qb = QueryBuilder() - qb.append( - StructureData, - project=['extras.formula'], - filters={'extras.formula': 'BaO3Ti'}, - tag='structure' - ) - qb.append( - CalcJobNode, - tag='calculation', - with_incoming='structure' - ) - qb.append( - Dict, - tag='results', - filters={'attributes.energy_smearing':{'<=':-1e-8}}, - project=[ - 'attributes.energy_smearing', - 'attributes.energy_smearing_units', - ], - with_incoming='calculation' - ) - qb.all() diff --git a/docs/pages/2019_Xiamen/sections/appendix_restarting_calculations.rst b/docs/pages/2019_Xiamen/sections/appendix_restarting_calculations.rst deleted file mode 100644 index bb06ea24..00000000 --- a/docs/pages/2019_Xiamen/sections/appendix_restarting_calculations.rst +++ /dev/null @@ -1,90 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -Restarting calculations -======================= - -Up till now, we have presented several cases where, for example, the wrong input parameters were passed to a calculation, causing it to fail. -Often we had to retype a lot of code, to setup a new calculation with the correct inputs. -In addition, there are many real-life scenarios where one would need to restart calculations from completed calculations with largely the same inputs. -For example when we run molecular dynamics, we might want to add more time steps than we initially thought, or as another example you might want to refine the relaxation of a structure with tighter parameters. - -In this section, you will learn how to relaunch a calculation from one that has already completed, while having the chance to add or change inputs before launching it. -As an example, let us first submit a total energy calculation using a parameters dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-14, - 'electron_maxstep': 3, - }, - } - -In this case, we set a very low number of self consistent iterations (3), which is too small to be able to reach the desired accuracy of 10\ :sup:`-14`. -This means the calculation will not converge and will not be successful, despite there not being any actual mistake in the parameters dictionary. - -To easily restart from the previous calculation, instead of retyping all the inputs, you can simply use the ``get_builder_restart`` method of the ``CalcJobNode``. -Just like the ``get_builder`` method of the ``Process`` class, this will create an instance of the ``ProcessBuilder``, except this one will have all the inputs pre-populated based on the node. -To do so, simply load the node of a terminated calculation job in a ``verdi shell``, that you want to restart from, e.g.: - -.. code:: python - - failed_calculation = load_node('') - restart_builder = failed_calculation.get_builder_restart() - -If you simply type ``restart_builder``, you can see that all the inputs have already been set to those that were used for the original calculation. -The only thing that now remains to be done, is to replace those inputs that caused the failure in the first place. - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['ELECTRONS']['electron_maxstep'] = 80 - restart_builder.parameters = Dict(dict=parameters) - -Simply giving the calculation some more steps in the convergence cycle will probably already fix the problem. - -.. note:: - - We have to create a new ``Dict`` node as we are changing its contents, and the original input is immutable as it would break the provenance. - -However, in this Quantum ESPRESSO example even more can be done, by using the results of the previous calculation to speed up the restart. -To do so, we simply have to set the input ``parent_folder`` to the remote working directory of the old calculation, i.e.: - -.. code:: python - - parameters = restart_builder.parameters.get_dict() - parameters['CONTROL']['restart_mode'] = 'restart' - restart_builder.parameters = Dict(dict=parameters) - restart_builder.parent_folder = failed_calculation.outputs.remote_folder - -Note that this particular step is specific to a ``PwCalculation``, but the restart builder concept works for any calculation class. -Any of the inputs can be changed or set. -For example, you may also want to record that this calculation is a restart, in addition to the provenance graph, by setting the label or description: - -.. code:: python - - restart_builder.metadata.label = 'Restart from PwCalculation<{}>'.format(failed_calculation.pk) - -Ultimately whatever needs to be changed for the restart is up to you, but the restart builder makes it a lot easier. -Finally, to submit the restart, since it is a process builder, it works exactly as any other builder: - -.. code:: python - - from aiida.engine import launch - results, node = launch.run.get_node(restart_builder) - -You can now inspect the restarted calculation to verify that this time it actually completed successfully. -Using the restart builder, the required code to setup a calculation is much shorter than the one needed to launch a new one from scratch. -There is no need to load or create many of the inputs such as the pseudopotentials, structures and k-points, -because they were reused from the first calculation. diff --git a/docs/pages/2019_Xiamen/sections/appendix_workflow_logic.rst b/docs/pages/2019_Xiamen/sections/appendix_workflow_logic.rst deleted file mode 100644 index ce5c5c55..00000000 --- a/docs/pages/2019_Xiamen/sections/appendix_workflow_logic.rst +++ /dev/null @@ -1,118 +0,0 @@ -*These appendices consist of optional exercises, and are mentioned -in earlier parts of the tutorial. Go through them only if you -have time.* - -.. _2019_xmn_workflow_logic: - -More workflow logic: while loops and conditional statements -=========================================================== - -In the previous sections, you have been introduced to ``WorkChains``, and the reason for using them over 'standard' work functions. -However, in the :ref:`original example<2019_xmn_workchainsimple>`, the ``spec.outline`` was quite simple, with a 'static' sequence of two steps. -Most often, however, you need dynamic workflows, where you need to decide at runtime whether to continue to compute or not (e.g. in a convergence loop, where you need to stop if convergence has been achieved). -To support this scenario, the ``spec.outline`` can support logic: `while` loops and `if/elif/else` blocks. -The simplest way to explain it is to show an example: - -.. code:: python - - from aiida.engine import if_, while_ - - spec.outline( - cls.s1, - if_(cls.isA)( - cls.s2 - ).elif_(cls.isB)( - cls.s3 - ).else_( - cls.s4 - ), - cls.s5, - while_(cls.condition)( - cls.s6 - ), - ) - -that would *roughly* correspond, in a python syntax, to: - -.. code:: python - - s1() - if isA(): - s2() - elif isB(): - s3() - else: - s4() - s5() - while condition(): - s6() - -The only constraint is that condition functions (in the example above ``isA``, ``isB`` and ``condition``) must be class methods that returns ``True`` or ``False`` depending on whether the condition is met or not. - -A suggestion on how to write new workchains: use the outline to help you in designing the logic. -First create the spec outline writing, almost if you were explaining it in words, what you expect the workflow to do. -Then, define one by one the methods. - -.. _2019_xmn_convpressure: - -Pressure convergence --------------------- - -For example, we have prepared a simple workflow (using work chains, work functions and calculation functions) to optimize the lattice parameter of silicon efficiently using Newton's algorithm on the energy derivative, i.e. the pressure :math:`p=-dE/dV`. -You can download this code :download:`here <../scripts/pressure_convergence.py>` -The outline looks like this: - -.. code:: python - - spec.outline( - cls.setup, - cls.put_step0_in_ctx, - cls.move_next_step, - while_(cls.not_converged)( - cls.move_next_step, - ), - cls.finish - ) - -This outline already roughly explains the algorithm: after an initialization (``setup``) and putting the first step (number zero) in the ctx (``put_step0_in_ctx``), a function to move to the next step is called (``move_next_step``). -This is iterated while a given convergence criterion is not met (``not_converged``). -Finally, some reporting is done, including returning some output nodes (``finish``). - -If you are interested in the details of the algorithm, you can inspect the file. -The main ideas are described here: - -``setup`` -Generate a ``pw.x`` calculation for the input structure (with volume -(V)), and one for a structure where the volume is :math:`V+4 \mbox{\normalfont\AA}^3` (just to get a closeby volume). -Store the results in the context as ``r0`` and ``r1`` - -``put_step0_in_ctx`` -Store in the context :math:`V`, :math:`E(V)` and :math:`dE/dV` for the first calculation ``r0`` - -``move_next_step`` -This is the most important function. Calculate :math:`V`, :math:`E(V)` and :math:`dE/dV` for ``r1``. -Also, estimate :math:`d^2E/dV^2` from the finite difference of the first derivative of ``r0`` and ``r1`` (helper functions to achieve this are provided). -Get the :math:`a`, :math:`b` and :math:`c` coefficients of a parabolic fit :math:`E = aV^2 + bV + c` and estimate the expected minimum of the EOS function as the minimum of the fit :math:`V_0 = -b / 2a`. -Finally, replace ``r0`` with ``r1`` in the context (i.e., get rid of the oldest point) and launch a new pw calculation at volume :math:`V_0`, that will be stored in the context replacing ``r1``. -In this way, at the next iteration ``r0`` and ``r1`` will contain the latest two simulations. -Finally, at each step some relevant information (coefficients :math:`a`, :math:`b` and :math:`c`, volumes, energies, energy derivatives, ...) are stored in a list called ``steps``. -This whole list is stored in the context because it provides quantities to be preserved between different work chain steps. - -``not_converged`` -Return ``True`` if convergence has not been achieved yet. -Convergence is achieved if the difference in volume between the two latest simulations is smaller than a given threshold ``volume_tolerance``. - -``finish`` -This is the final step. -Mainly, we return the output nodes: ``steps`` with the list of results at each step, and ``structure`` with the final converged structure. - -The results returned in ``steps`` can be used to represent the evolution of the minimisation algorithm. -A possible way to visualize it is presented in :numref:`fig_convpressure` obtained with an initial lattice constant of `alat = 5.2`. - -.. _2019_xmn_fig_convpressure: -.. figure:: include/images/convergence_pressure.png - - Example of results of the convergence algorithm presented in this section. - The bottom plot is a zoom near the minimum. - The dots represent the (volume,energy) points obtained from Quantum ESPRESSO, and the numbers indicate at which iteration they were obtained. - The parabolas represent the parabolic fits used in the algorithm; the minimum of the parabola is represented with a small cross, in correspondence of the vertical lines, used as the volume for the following step. diff --git a/docs/pages/2019_Xiamen/sections/bands.rst b/docs/pages/2019_Xiamen/sections/bands.rst deleted file mode 100644 index 721025b5..00000000 --- a/docs/pages/2019_Xiamen/sections/bands.rst +++ /dev/null @@ -1,52 +0,0 @@ -.. _2019_xmn_bands: - -A real-world ``WorkChain``: Computing a band structure -====================================================== - -.. note:: *If you still have enough time, you might want to first check* - :numref:`Appendix %s <2019_xmn_workflow_logic>` *before continuing with this section.* - -As a final demonstration of the power of ``WorkChain``\ s in AiiDA, we want to -give a demonstration of a ``WorkChain``, which will take a structure as its -only input and compute its band structure. All of the steps that would -normally have to be done manually by the researcher -- choosing appropriate -pseudopotentials, energy cutoffs, k-point meshes, high-symmetry k-point paths, -and performing the various calculation steps -- are performed automatically by -the ``WorkChain``. - -The demonstration of the WorkChain will be performed in a Jupyter notebook, -that you can :download:`download from here <../notebooks/bandstructure.ipynb>`. -There you will find some example structures that are loaded from the -Crystallography Open Database (COD), using the COD-importer integrated in -AiiDA. - -Note that the required time to calculate the bandstructure for the given -structures ranges from ~5 minutes to more than an hour, given that the virtual -machine is running on two cores with CPU throttling. It is not necessary to -run all these examples as they may take too long to complete. For reference, -the expected output band structures are plotted in :numref:`2019_xmn_fig_calc_bands_1` -to :numref:`2019_xmn_fig_calc_bands_4`. - -.. _2019_xmn_fig_calc_bands_1: -.. figure:: include/images/bandstructures/Al_bands.png - :width: 100% - - Electronic band structures of Al computed with AiiDA’s PwBandsWorkChain - -.. _2019_xmn_fig_calc_bands_2: -.. figure:: include/images/bandstructures/GaAs_bands.png - :width: 100% - - Electronic band structures of GaAs computed with AiiDA’s PwBandsWorkChain - -.. _2019_xmn_fig_calc_bands_3: -.. figure:: include/images/bandstructures/CaF2_bands.png - :width: 100% - - Electronic band structures of CaF\ :sub:`2` computed with AiiDA’s PwBandsWorkChain - -.. _2019_xmn_fig_calc_bands_4: -.. figure:: include/images/bandstructures/hBN_bands.png - :width: 100% - - Electronic band structures of BN computed with AiiDA’s PwBandsWorkChain diff --git a/docs/pages/2019_Xiamen/sections/calculations.rst b/docs/pages/2019_Xiamen/sections/calculations.rst deleted file mode 100644 index 149946c2..00000000 --- a/docs/pages/2019_Xiamen/sections/calculations.rst +++ /dev/null @@ -1,625 +0,0 @@ -.. _2019_xmn_calculations: - -Submit, monitor and debug calculations -====================================== - -In this section we'll be learning how to create new data in AiiDA. - -We will use the `Quantum Espresso `_ package to launch a simple `density functional theory `_ calculation of a silicon crystal using the :doi:`PBE exchange-correlation functional <10.1103/PhysRevB.54.16533>` and check its results. -While we're going to debug these issues 'manually' here, workflows (which you'll encounter later in this tutorial) can help you avoid these issues systematically. - -Note that besides the ``aiida-quantumespresso`` plugin, AiiDA comes with plugins for a range of other codes, all of which are listed in the `AiiDA plugin registry `_. - -Computer setup --------------- - -In a production environment, AiiDA would typically be running on your work station or laptop, while launching calculations -on remote high-performance compute resources that you have SSH access to. -For this reason AiiDA has the concept of a ``Computer`` to run calculations on. - -To keep things simple, Quantum ESPRESSO (together with several other *ab initio* codes) has been installed directly in the -``codes`` subfolder of your virtual machine, -and you are going to launch your first calculations on the same computer where AiiDA is installed. -Nevertheless, we're now going to set up this computer for launching calculations: - -.. code:: bash - - verdi computer setup --config computer.yml - -where ``computer.yml`` is a configuration file in the `YAML format `_) that you can :download:`download here `. This is its content: - -.. literalinclude:: include/configuration/computer.yml - -.. note:: - When used without the ``--config`` option, ``verdi computer setup`` will prompt you for the required information, just like you have seen when :ref:`setting up a profile<2019_xmn_setup_verdi_quicksetup>`. - The configuration file should work for the virtual machine that comes with this tutorial - but may need to be adapted when you are running AiiDA in a different environment, - as explained in :ref:`this appendix<2019_xmn_appendix_computer_code_setup>`. - -Finally, you need to provide AiiDA with information on how to access the ``Computer``. -For remote computers with ``ssh`` transport, this would involve e.g. an SSH key. -For ``local`` computers, this is just a "formality" (press enter to confirm the default cooldown time): - -.. code:: bash - - verdi computer configure local localhost - -.. note:: - - For remote computers with ``ssh`` transport, use ``verdi computer configure ssh`` instead of ``verdi computer configure local``. - -Your ``localhost`` computer should now show up in - -.. code:: bash - - verdi computer list - -Before proceeding, test that it works: - -.. code:: bash - - verdi computer test localhost - - -Code setup ----------- - -Now that we have our localhost set up, let's configure the ``Code``, namely the ``pw.x`` executable. -As with the computer, we have prepared a configuration file for you to :download:`download `. -This is its content: - -.. literalinclude:: include/configuration/code.yml - :language: yaml - -Once you have the configuration file in your local working environment, set up the code: - -.. code:: bash - - verdi code setup --config code.yml - -.. warning:: - - The configuration should work for the virtual machine that comes with this tutorial. - If you are following this tutorial in a different environment, you will need to install Quantum ESPRESSO and adapt the configuration to your needs, - as explained in :ref:`this appendix<2019_xmn_appendix_computer_code_setup>`. - -Similar to the computers, you can list all the configured codes with: - -.. code:: bash - - verdi code list - -Verify that it now contains a code named ``qe-6.3-pw`` that we just configured. -You can always check the configuration details of an existing code using: - -.. code:: bash - - verdi code show qe-6.3-pw - -.. note:: - - The ``generic`` profile has already a number of other codes configured. - See ``verdi -p generic code list``. - - -The AiiDA daemon ----------------- - -First of all, check that the AiiDA daemon is actually running. -The AiiDA daemon is a program that - - * runs continuously in the background - * waits for new calculations to be submitted - * transfers the inputs of new calculations to your compute resource - * checks the status of your calculation at the compute resource, and - * retrieves the results from the compute resource - -Check the status of the daemon process by typing in the terminal: - -.. code:: bash - - verdi daemon status - -If the daemon is running, the output should look like - -.. code:: bash - - Profile: default - Daemon is running as PID 2050 since 2019-04-30 12:37:12 - Active workers [1]: - PID MEM % CPU % started - ----- ------- ------- ------------------- - 2055 2.147 0 2019-04-30 12:37:12 - Use verdi daemon [incr | decr] [num] to increase / decrease the amount of workers - -If this is not the case, type in the terminal - -.. code:: bash - - verdi daemon start - -to start the daemon. - -Creating a new calculation --------------------------- - -In the following, we'll be working in the ``verdi shell``. -As you go along, feel free to keep track of your commands by -copying them into a python script ``test_pw.py``. - -.. note:: - - The ``verdi shell`` imports a number of AiiDA internals so that you as the user don't have to. - You can also make those available to a python script, by running it using - - .. code:: bash - - verdi run - - -Every calculation sent to a cluster is linked to a *code*, which describes -the executable file to be used as well as some metadata. -Let's have a look at the codes already installed on your machine: - -.. code:: bash - - verdi code list - -There should be a number of them. Here, we're interested in the "PWscf" executable of Quantum Espresso, i.e. in codes for the ``quantumespresso.pw`` plugin: - -.. code:: bash - - verdi code list -P quantumespresso.pw - -Pick the correct codename, that might look like, e.g. -``qe-6.3-pw@localhost`` and load it in the verdi shell. - -.. code:: python - - code = load_code("") - -.. note:: - - ``load_code`` returns an object of type ``Code``, which is the general AiiDA class for describing simulation codes. - -Let's build the inputs for a new ``PwCalculation`` (defined by the ``quantumespresso.pw`` plugin, the default plugin for the code you chose before) - -.. code:: python - - builder = code.get_builder() - -As the first step, assign a (short) label or a (long) description to your -calculation, that you might find convenient in the future. - -.. code:: python - - builder.metadata.label = "PW test" - builder.metadata.description = "My first AiiDA calc with Quantum ESPRESSO on Si" - -This information will be saved in the database for later queries or -inspection. Note that you can press TAB after writing ``builder.`` to -see all inputs available for this calculation. -In order to figure out which data type is expected for a particular input, such as ``builder.structure``, -and whether the input is optional or required, use ``builder.structure??``. - -Now, specify the number of machines (a.k.a. cluster nodes) -you are going to run on and the maximum time allowed for the -calculation. -The general options grouped under ``builder.metadata.options`` are independent of -the code or plugin, and will be passed to the scheduler that handles the -queue on your compute resource . - -.. code:: python - - builder.metadata.options.resources = {'num_machines': 1} - builder.metadata.options.max_wallclock_seconds = 30 * 60 - -Again, to see the list of available options, type -``builder.metadata.options.`` and hit the TAB button. - -Preparation of inputs -~~~~~~~~~~~~~~~~~~~~~ - -A Quantum Espresso calculation needs a number of inputs: - -1. `Pseudopotentials `_ -2. a structure -3. a mesh in reciprocal space (k-points) -4. a number of input parameters - -These are mirrored in the inputs of the ``aiida-quantumespresso`` plugin -(see `documentation `_). -We'll start with the structure, k-points, and pseudopotentials -and leave the input parameters as the last thing to setup. - -.. admonition:: Exercise - - Use what you learned in the previous section to load the ``structure`` and ``kpoints`` inputs for your calculation: - - * Use a silicon crystal structure - * Define a ``2x2x2`` mesh of k-points. - - Note: If you just copy and paste code that you executed previously, - this may result in duplication of information on your database. - In fact, you can re-use an existing structure stored in your database [#f1]_. Use a combination of the bash command - ``verdi data structure list`` and of the shell command ``load_node()`` - to get an object representing the structure created earlier. - -Attaching the input information to the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once you've created a ``structure`` node and a ``kpoints`` node, -attach it to the calculation: - -.. code:: python - - builder.structure = structure - builder.kpoints = kpoints - -.. note:: - - The builder accepts both *stored* and *unstored* data nodes. - AiiDA will take care of storing the unstored nodes upon submission. - If you decide not to submit, nothing will be stored in the database. - -PWscf also needs information on the pseudopotentials, -in the form of a dictionary, where keys are the names of the elements and the values are the corresponding ``UpfData`` objects containing the information on the pseudopotential. -However, instead of creating the dictionary by hand, we can use a helper function that picks the right pseudopotentials for our structure from a pseudopotential *family*. -You can list the preconfigured families from the command line: - -.. code:: bash - - verdi data upf listfamilies - -Pick the one you configured earlier (or one of the ``SSSP`` families that -we provide) and link it to the calculation using the command: - -.. code:: python - - from aiida.orm.nodes.data.upf import get_pseudos_from_structure - builder.pseudos = get_pseudos_from_structure(structure, '') - -Print the content of the `pseudos` namespace (`print(builder.pseudos)`) to see what the helper function created. - -Preparing and debugging input parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Finally, we need to specify a number of input parameters (i.e. plane wave cutoffs, convergence thresholds, etc.). -to launch the Quantum ESPRESSO calculation. -The structure of the parameter dictionary closely follows the structure of the `PWscf input file `_. - -Since these are often the parameters to tune in a calculation, -let's **introduce a few mistakes intentionally** -and use this part of the tutorial to learn how to debug problems. - -Define a set of input parameters for Quantum ESPRESSO, preparing a -dictionary of the form: - -.. code:: python - - parameters_dict = { - 'CONTROL': { - 'calculation': 'scf', - 'tstress': True, - 'tprnfor': True, - }, - 'SYSTEM': { - 'ecutwfc': 30., - 'ecutrho': 200., - 'mickeymouse': 240., - }, - 'ELECTRONS': { - 'conv_thr': 1.e-8, - }, - } - -This dictionary is almost a valid input for the Quantum ESPRESSO plugin, -except for an invalid key ``mickeymouse``. When Quantum ESPRESSO -receives an unrecognized key, it will stop. -By default, the AiiDA plugin will *not* validate your input and simply pass -it on to the code. - -Let's wrap the ``parameters_dict`` python dictionary in an AiiDA ``Dict`` node and see what happens. - -.. code:: python - - builder.parameters = Dict(dict=parameters_dict) - -Simulate submission -~~~~~~~~~~~~~~~~~~~ - -At this stage, you have created in memory (it's not yet stored in the -database) the input of the graph shown below. The outputs will -be created by the daemon later on. - -.. figure:: include/images/verdi_graph/si/graph-full.png - :alt: - -In order to check which input files AiiDA creates, -we can perform a *dry run* of the submission process. -Let's tell the builder that we want a dry run and that -we don't want to store the provenance of the dry run: - -.. code:: python - - builder.metadata.dry_run = True - builder.metadata.store_provenance = False - -It's time to run: - -.. code:: python - - from aiida.engine import run - run(builder) - -.. note:: - - Instead of using the builder, you can also simply pass the calculation class - as the first argument, followed by the inputs as keyword arguments, e.g.: - - .. code:: python - - run(PwCalculation, structure=structure, pseudos={'Si': pseudo_node}, ....) - - The builder is simply a convenience wrapper providing tab-completion in the shell and automatic help strings. - -This creates a folder of the form ``submit_test/[date]-0000[x]`` in the -current directory. In your second terminal: - - * open the input file ``aiida.in`` within this folder - * compare it to input data nodes you created earlier - * verify that the `pseudo` folder contains the needed pseudopotentials - * have a look at the submission script ``_aiidasubmit.sh`` - -.. note:: - - The files created by a dry run are only intended for inspection - and cannot be used to correct the inputs of your calculation. - -Submitting the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Up to now we've just been playing around and our calculation has been kept -in memory and not in the database. -Now that we have inspected the input files and convinced ourselves that -Quantum ESPRESSO will have all the information it needs to perform the -calculation, we will submit the calculation properly. -Doing so will make sure that all inputs are stored in the database, -will run and store the calculation and link the outputs to it. - -Let's revert the following values in our builder to their defaults: - -.. code:: python - - builder.metadata.dry_run = False - builder.metadata.store_provenance = True - -And then rely on the submit machinery of AiiDA, - -.. code:: python - - from aiida.engine import submit - calculation = submit(builder) - -As soon as you have executed these lines, the ``calculation`` variable contains a ``PwCalculation`` instance, already submitted to the daemon. - -.. note:: - - You may have noticed that we used ``submit`` here instead of ``run``. - The difference is that ``submit`` will hands over the calculation to the daemon running in the background, - while ``run`` will execute all tasks in the current shell. - - All processes in AiiDA (you will soon get to know more) can be "launched" using one of available functions: - - * run - * run_get_node - * run_get_pk - * submit - - which are explained in more detail in the `online documentation `_. - - -The calculation is now stored in the database and was assigned a "database primary key" or ``pk`` (``calculation.pk``) as well as a UUID (``calculation.uuid``). -See the :ref:`previous section <2019-aiida-identifiers>` for more details on these identifiers. - -Note that while AiiDA will prevent you from changing the content of stored nodes, -the concept of "extras" allows you to set extra attributes, e.g. as a way of -labelling nodes and providing information for querying. - -For example, let's add an extra attribute called ``element``, with value ``Si``: - -.. code:: python - - calculation.set_extra("element", "Si") - -In the mean time, after you submitted your calculation, the daemon -picked it up and started to: generate the input files, submit the calculation -to the queue, wait for it to run and finish, retrieve the output files, -parse them, store them in the database and set the state of the -calculation to ``Finished``. - -.. note:: - - If the daemon is not running, the calculation will remain in the - ``NEW`` state until you start the daemon. - -Checking the status of the calculation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can check the calculation status from the command line: - -.. code:: bash - - verdi process list - -.. note:: - - Since you are running your DFT calculation directly on the VM, - ``verdi`` commands can be a bit slow until the calculation finishes. - -If you don't see any calculation in the -output, the calculation you submitted has already finished. - -By default, the command only prints calculations that are still active [#f2]_. -Let's also list your finished calculations (and limit those only -to the one created in the past day): - -.. code:: bash - - verdi process list -a -p1 - -as explained in the first section. - -Similar to the dry run, we can also inspect the input files of the *actual* -calculation: - -.. code:: bash - - verdi calcjob inputls -c - -for the ``pk_number`` of your calculation. This will show the -contents of the input directory (``-c`` prints directories in color). -Check the content of input files with - -.. code:: bash - - verdi calcjob inputcat | less - -Troubleshooting ---------------- - -Your calculation should end up in a FAILED state -(last column of ``verdi process list -a -p1``), and correspondingly the -error code near the "Finished" status of the State should be non-zero, - -.. code:: bash - - $ verdi process list -a -p1 - PK Created State Process label Process status - ---- --------- ---------------- --------------- ---------------- - 98 16h ago Finished [115] PwCalculation - ... - $ # Anything but [0] after the Finished state signals a failure - -This was expected, since we used an invalid key in the input parameters. -Situations like this happen in real life, so AiiDA provides -tools to trace back to the source of the problem and correct it. - -A first way to proceed is to inspect the output file of -PWscf. - -.. code:: bash - - verdi calcjob outputcat | less - -This might be enough to understand the reason why the calculation -failed. - -AiiDA provides further tools for troubleshooting in a more compact way. -For any calculation, both successful and failed, you can get a summary by: - -.. code:: bash - - $ verdi process show - Property Value - ------------- --------------------------------------------------- - type CalcJobNode - pk 98 - uuid 4c444afd-f6e2-4896-b9ae-8cb8a5ec75c5 - label PW test - description My first AiiDA calc with Quantum ESPRESSO on Si - ctime 2019-05-01 15:59:39.180018+00:00 - mtime 2019-05-01 16:01:44.870902+00:00 - process state Finished - exit status 115 - computer [1] localhost - - Inputs PK Type - ---------- ---- ------------- - pseudos - Si 50 UpfData - code 2 Code - kpoints 10 KpointsData - parameters 96 Dict - settings 97 Dict - structure 9 StructureData - - Outputs PK Type - ------------- ---- ---------- - remote_folder 99 RemoteData - retrieved 100 FolderData - - Log messages - --------------------------------------------- - There are 2 log messages for this calculation - Run 'verdi process report 98' to see them - -The last part of the output alerts you to the fact that there -are some log messages waiting for you, if you run -``verdi process report ``. - -Let's now correct our input parameters dictionary by leaving out the invalid -key and see if our calculation succeeds: - -.. code:: python - - parameters_dict = { - "CONTROL": { - "calculation": "scf", - }, - "SYSTEM": { - "ecutwfc": 30., - "ecutrho": 200., - }, - "ELECTRONS": { - "conv_thr": 1.e-6, - } - } - builder.parameters = Dict(dict=parameters_dict) - calculation = submit(builder) - -If you have been using the separate script approach, modify -the script to remove the faulty input and run it again with: - -.. code:: bash - - verdi run test_pw.py - -Use ``verdi process list -a -p1`` to verify that -the calculation reaches the finished status, with exit code zero. - -Using the calculation results ------------------------------ - -Now you can access the results as you have seen earlier. For example, -note down the pk of the calculation so that you can load it in the -``verdi shell`` and check the total energy with the commands: - -.. code:: python - - calculation = load_node() - calculation.res.energy - -Besides writing input files, running the software for you, storing the output -files, and connecting it all together in your provenance graph, -many AiiDA plugins will parse the output of your code and make output values -of interest available through an output dictionary node (as depicted in the -graph above). -In the case of the ``aiida-quantumespresso`` plugin this output node -is available at ``calculation.outputs.output_parameters`` and you can access -all the available attributes (not only the energy) using: - -.. code:: python - - calculation.outputs.output_parameters.attributes - -While the name of this output dictionary node can be chosen by the plugin, -AiiDA provides the "results" shortcut ``calculation.res`` -that plugin developers can use to provide what they consider the result of the -calculation. - - -.. rubric:: Footnotes - -.. [#f1] In order to avoid duplication of KpointsData, you would first need to learn how to query the database, therefore we will ignore this issue for now. -.. [#f2] A process is considered active if it is either ``Created``, ``Running`` or ``Waiting``. If a process is no longer active, but terminated, it will have a state ``Finished``, ``Killed`` or ``Excepted``. diff --git a/docs/pages/2019_Xiamen/sections/first_taste.rst b/docs/pages/2019_Xiamen/sections/first_taste.rst deleted file mode 100644 index 1f19a12f..00000000 --- a/docs/pages/2019_Xiamen/sections/first_taste.rst +++ /dev/null @@ -1,298 +0,0 @@ -A first taste -============= - -Let's start with a quick demo of how AiiDA can make your life easier as a computational scientist. - -We'll be using the ``verdi`` command-line interface, -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``--help/-h`` flag: - - .. code:: bash - - verdi -h - -Importing a structure and running a calculation ------------------------------------------------ - -Let's download a structure from the `Crystallography Open Database `_ and import it into AiiDA: - -.. code:: bash - - wget http://crystallography.net/cod/9008565.cif - verdi data structure import ase 9008565.cif - -Each piece of data in AiiDA gets a PK number (and a UUID, more about this later). -The PK allows you to easily reuse data anywhere in AiiDA. -Remember the PK of the structure, which we will now use to run our first calculation. - -.. note:: - - You can view the structure either `online `_ - or use ``jmol 9008565.cif`` locally. - -.. Let jason/jianxing test speed of SSH forwarding - potentially mention jupyter - -The following short python script sets up a self-consistent field calculation for the Quantum ESPRESSO code: - -.. literalinclude:: include/snippets/demo_calcjob.py - -Download the :download:`demo_calcjob.py ` script to your working directory. -It contains a few placeholders for you to fill in: - - #. the VM already has a number of codes preconfigured. Use ``verdi code list`` to find the label for the "PW" code and use it in the script. - #. replace the PK of the structure with the one you obtained - #. the VM already contains a number of pseudopotential families. Replace the PP family name with the one for the "SSSP efficiency" library found via ``verdi data upf listfamilies``. - -Then submit the calculation using: - -.. code:: bash - - verdi run demo_calcjob.py - -From this point onwards, the AiiDA daemon will take care of your calculation: creating the necessary input files, running the calculation, and parsing its results. -It should take less than one minute to complete. - -Analyzing the outputs of a calculation --------------------------------------- - -Let's have a look how your calculation is doing: - -.. code:: bash - - verdi process list # shows only running processes - verdi process list --all # shows all processes - -Again, your calculation will get a PK, which you can use to get more information on it: - -.. code:: bash - - verdi process show - -As you can see, AiiDA has tracked all the inputs provided to the calculation, allowing you (or anyone else) to reproduce it later on. -AiiDA's record of a calculation is best displayed in the form of a provenance graph - -.. figure:: include/images/demo_calc.png - :width: 100% - - Provenance graph for a single Quantum ESPRESSO calculation. - -You can generate such a provenance graph for any calculation or data in AiiDA by running: - -.. code:: bash - - verdi node graph generate - -Try to reproduce the figure using the PK of your calculation. - -You might wonder what happened under the hood, e.g. where to find the actual input and output files of the calculation. -You will learn more about this later -- until then, here are a few useful commands: - -.. code:: bash - - verdi calcjob inputcat # shows the input file of the calculation - verdi calcjob outputcat # shows the output file of the calculation - verdi calcjob res # shows the parsed output - -A few questions you could answer using these commands (optional) - * How many atoms did the structure contain? - How many electrons? - * How many k-points were specified? How many k-points were actually computed? Why? - * How many SCF iterations were needed for convergence? - * How long did Quantum ESPRESSO actually run (wall time)? - -Moving to a different computer ------------------------------- - -Now, this Quantum ESPRESSO calculation ran on your (virtual) machine. -This is fine for tests, but for production calculations you'll typically want to run on a remote compute cluster. -In AiiDA, moving a calculation from one computer to another means changing one line of code. - -For the purposes of this tutorial, you'll run on your neighbor's computer. -Ask your neighbor for the IP address of their VM. -Then, download the :download:`neighbor.yml ` setup template, replace the placeholder by the IP address and let AiiDA know about this computer by running: - -.. .. literalinclude:: include/configuration/neighbor.yml - -.. code:: bash - - verdi computer setup --config neighbor.yml - -.. note:: - - If you're completing this tutorial at a later time and have no partner machine, - simply use "localhost" instead of the IP address. - -AiiDA is now aware of the existence of the computer but you'll still need to let AiiDA -know how to connect to it. -AiiDA does this via `SSH `_ keys. -Your tutorial VM already contains a private SSH key for connecting to the ``compute`` user of your neighbor's machine, -so all that is left is to configure it in AiiDA. - -Download the :download:`neighbor-config.yml ` configuration template and run: - -.. .. literalinclude:: include/configuration/neighbor-config.yml - -.. code:: bash - - verdi computer configure ssh neighbor --config neighbor-config.yml --non-interactive - -.. note:: Both ``verdi computer setup`` and ``verdi computer configure`` can be used interactively without - configuration files, which are provided here just to avoid typing errors. - -AiiDA should now have access to your neighbor's computer. Let's quickly test this: - -.. code:: bash - - verdi computer test neighbor - -Finally, let AiiDA know about the **code** we are going to use. -We've again prepared a template that looks as follows: - -.. Add template for code -.. literalinclude:: include/configuration/qe.yml - -Download the :download:`qe.yml ` code template and run: - -.. code:: bash - - verdi code setup --config qe.yml - verdi code list # note the label of the new code you just set up! - -Now modify the code label in your ``demo_calcjob.py`` script to the label of your new code and simply run another calculation using ``verdi run demo_calcjob.py``. - -To see what is going on, AiiDA provides a command that lets you jump to the folder of the directory of the calculation on the remote computer: - -.. code:: bash - - verdi process list --all # get PK of new calculation - verdi calcjob gotocomputer - -Have a look around. - * Do you recognize the different files? - * Have a look at the submission script ``_aiidasubmit.sh``. - Compare it to the submission script of your previous calculation. - What are the differences? - -From calculations to workflows ------------------------------- - -AiiDA can help you run individual calculations but it is really designed to help you run workflows that involve several calculations, while automatically keeping track of the provenance for full reproducibility. - -As the final step, we are going to launch the ``PwBandStructure`` workflow of the ``aiida-quantumespresso`` plugin. - -.. literalinclude:: include/snippets/demo_bands.py - -Download the :download:`demo_bands.py ` snippet and run it using - -.. code:: bash - - verdi run demo_bands.py - -This workflow will: - - #. Determine the primitive cell of the input structure - #. Run a calculation on the primitive cell to relax both the cell and the atomic positions (``vc-relax``) - #. Refine the symmetry of the relaxed structure, and find a standardised primitive cell using SeeK-path_ - #. Run a self-consistent field calculation on the refined structure - #. Run a band structure calculation at fixed Kohn-Sham potential along a standard path between high-symmetry k-points determined by SeeK-path_ - -The workflow uses the PBE exchange-correlation functional with suitable pseudopotentials and energy cutoffs from the `SSSP library version 1.1 `_. - - -.. _SeeK-path: https://www.materialscloud.org/work/tools/seekpath - -.. K-point mesh is selected to have a minimum k-point density of 0.2 Å-1 - A Marzari-Vanderbilt smearing of 0.02 Ry is used for the electronic occupations - -The workflow should take ~10 minutes on your virtual machine. -You may notice that ``verdi process list`` now shows more than one entry. -While you wait for the workflow to complete, -let's start exploring its provenance. - -The full provenance graph obtained from ``verdi node graph generate`` will already be rather complex (you can try!), -so let's try browsing the provenance interactively instead. - -Start the AiiDA REST API: - -.. code:: bash - - verdi restapi - -and open the |provenance browser|. - -.. |provenance browser| raw:: html - - Materials Cloud provenance browser - -.. note:: - - In order for the provenance browser to work, you need to configure SSH to tunnel port 5000 from your VM to your local laptop (see here :ref:`2019_xmn_connect`). - - -The provenance browser is a Javascript application that connects to the AiiDA REST API. -Your data never leaves your computer. - -.. some general comment on importance of the graph? -.. a sentence on how to continue from here - -Browse your AiiDA database. - * Start by finding your structure in Data => Structure - * Use the provenance browser to explore the steps of the ``PwBandStructureWorkChain`` - -.. note:: - - When perfoming calculations for a publication, you can export your provenance graph using ``verdi export create`` and upload it to the `Materials Cloud Archive `_, enabling your peers to explore the provenance of your calculations online. - -Once the workchain is finished, use ``verdi process show `` to inspect the ``PwBandStructureWorkChain`` and find the PK of its ``band_structure`` output. -Use this to produce a PDF of the band structure: - -.. code:: bash - - verdi data bands export --format mpl_pdf --output band_structure.pdf - - -.. figure:: include/images/si_bands.png - :width: 80% - - Band structure computed by the `PwBandStructure` workchain. - -.. note:: - The ``BandsData`` node does contain information about the Fermi energy, so the energy zero in your plot will be arbitrary. - You can produce a plot with the Fermi energy set to zero (as above) using the following code in a jupyter notebook: - - .. code:: ipython - - %aiida - %matplotlib inline - - scf_params = load_node() # REPLACE with PK of "scf_parameters" output - fermi_energy = scf_params.dict.fermi_energy - - bands = load_node() # REPLACE with PK of "band_structure" output - bands.show_mpl(y_origin=fermi_energy, plot_zero_axis=True) - - - -What next? ----------- - -You now have a first taste of the type of problems AiiDA tries to solve. -Here are some options for how to continue: - - * Continue with the in-depth tutorial and learn more about the ``verdi``, ``verdi shell`` and ``python`` interfaces to AiiDA. - There is more than enough material to keep you busy for a day. - * Download `Quantum Mobile`_ virtual machine and try running the tutorial on your laptop instead. This will let you take the materials home and continue in your own time. - * Try `setting up AiiDA directly on your laptop `_. - - .. note:: **For advanced Linux & python users only**. - AiiDA depends on a number of services and software that require some skill to set up. - Unfortunately, we don't have the human resources to help you solve - issues related to your setup in a tutorial context. - - * Continue your work from other parts of the workshop, chat with participants and enjoy yourself :-) - - - .. _Quantum Mobile: https://github.com/marvel-nccr/quantum-mobile/releases/tag/19.09.0 diff --git a/docs/pages/2019_Xiamen/sections/include/configuration/code.yml b/docs/pages/2019_Xiamen/sections/include/configuration/code.yml deleted file mode 100644 index e9bdaad2..00000000 --- a/docs/pages/2019_Xiamen/sections/include/configuration/code.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" -on_computer: true -computer: "localhost" -remote_abs_path: "/usr/local/bin/pw.x" -prepend_text: "ulimit -s unlimited" -append_text: " " diff --git a/docs/pages/2019_Xiamen/sections/include/configuration/computer.yml b/docs/pages/2019_Xiamen/sections/include/configuration/computer.yml deleted file mode 100644 index 01938930..00000000 --- a/docs/pages/2019_Xiamen/sections/include/configuration/computer.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -description: "localhost" -label: "localhost" -hostname: "localhost" -transport: local -scheduler: "direct" -work_dir: "/home/max/.aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_Xiamen/sections/include/configuration/neighbor-config.yml b/docs/pages/2019_Xiamen/sections/include/configuration/neighbor-config.yml deleted file mode 100644 index ae7a0c26..00000000 --- a/docs/pages/2019_Xiamen/sections/include/configuration/neighbor-config.yml +++ /dev/null @@ -1,6 +0,0 @@ ---- -username: compute -key_filename: /home/max/.ssh/aiida-tutorial-compute.pem -timeout: 30 # timout for SSH before giving up -key_policy: AutoAddPolicy # add to known hosts automatically -safe_interval: 5 # Minimum time (s) between consecutive connections diff --git a/docs/pages/2019_Xiamen/sections/include/configuration/neighbor.yml b/docs/pages/2019_Xiamen/sections/include/configuration/neighbor.yml deleted file mode 100644 index 9c311820..00000000 --- a/docs/pages/2019_Xiamen/sections/include/configuration/neighbor.yml +++ /dev/null @@ -1,12 +0,0 @@ ---- -label: neighbor -description: Your neighbor's VM. -hostname: IP_ADDRESS # << REPLACE -transport: ssh # connects via SSH -scheduler: slurm # use SLURM scheduler -work_dir: "/tmp/aiida_run" -mpirun_command: "mpirun -np {tot_num_mpiprocs}" -mpiprocs_per_machine: "2" -shebang: "#!/bin/bash" -prepend_text: " " -append_text: " " diff --git a/docs/pages/2019_Xiamen/sections/include/configuration/qe.yml b/docs/pages/2019_Xiamen/sections/include/configuration/qe.yml deleted file mode 100644 index 4f9fc76d..00000000 --- a/docs/pages/2019_Xiamen/sections/include/configuration/qe.yml +++ /dev/null @@ -1,9 +0,0 @@ ---- -label: "qe-6.4.1-pw" -description: "quantum_espresso v6.4.1" -input_plugin: "quantumespresso.pw" # input plugin for pw.x code -computer: "neighbor" -on_computer: true -remote_abs_path: "/usr/local/bin/pw.x" # path to executable -prepend_text: "ulimit -s unlimited" # set stacksize to unlimited -append_text: " " diff --git a/docs/pages/2019_Xiamen/sections/include/images/Al_bands.png b/docs/pages/2019_Xiamen/sections/include/images/Al_bands.png deleted file mode 100644 index 83a18e02..00000000 Binary files a/docs/pages/2019_Xiamen/sections/include/images/Al_bands.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sections/include/images/CaF2_bands.png 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - - - Float(0.98) - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - - CALL CALL - - - - RETURN - CREATE - - - - CREATE - - C1 CREATE_STRUCTURE - - - - C2 RESCALE - - - - - - Str('Si') - - - - - - - StructureDataSi alat=5.341 - - - - - - - StructureDataSi alat=5.322 - - - - - - - - - Float(0.98) - - - - - - - - - - - W1 - - - - - - CREATE RESCALED - - - - a) b) diff --git a/docs/pages/2019_Xiamen/sections/include/images/qb_example_2.png b/docs/pages/2019_Xiamen/sections/include/images/qb_example_2.png deleted file mode 100644 index 6a0c9117..00000000 Binary files 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a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/batio3/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/graph.pdf b/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/graph.pdf deleted file mode 100644 index 50597454..00000000 Binary files a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/graph.pdf and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/graph.svg b/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/graph.svg deleted file mode 100644 index aa4259db..00000000 --- a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/graph.svg +++ /dev/null @@ -1,2678 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - Sialat = 5.4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - - UpfData - Si-family.UPF - - PwCalculation - - - - - - - code - - structure - - parameters - - kpoints - - pseudos_Si - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - RemoteData - /scratch/run/path/id@cluster.uni.edu - - FolderData - aiida.outdata-file.xml... - - Dict - energy = -8 eVforces = [...]... - - - - - - - - - - Additional parsedoutput nodes - - remote_folder - - output parameters - - retrieved - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - - Code - /path/to/pw.x@cluster.uni.edu - - StructureData - BaTiO3alat = 4 Å - - Dict - cutoff = 50 Ry... - - KpointsData - 3x3x3 - UpfData - Ba-family.UPFTi-family.UPFO-family.UPF - - PwCalculation - - - - - - code - - structure - - parameters - - kpoints - - - - - - - pseudos_Ti - - pseudos_Ba - - - pseudos_O - - - diff --git a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/si/graph-full.png b/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/si/graph-full.png deleted file mode 100644 index 5cfe8472..00000000 Binary files a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/si/graph-full.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/si/graph-input.png b/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/si/graph-input.png deleted file mode 100644 index 306d45d9..00000000 Binary files a/docs/pages/2019_Xiamen/sections/include/images/verdi_graph/si/graph-input.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sections/include/snippets/__init__.py b/docs/pages/2019_Xiamen/sections/include/snippets/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/pages/2019_Xiamen/sections/include/snippets/demo_bands.py b/docs/pages/2019_Xiamen/sections/include/snippets/demo_bands.py deleted file mode 100644 index 57eaa03a..00000000 --- a/docs/pages/2019_Xiamen/sections/include/snippets/demo_bands.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Compute a band structure with Quantum ESPRESSO - -Uses the PwBandStructureWorkChain provided by aiida-quantumespresso. -""" -from aiida.engine import submit -PwBandStructureWorkChain = WorkflowFactory('quantumespresso.pw.band_structure') - -results = submit( - PwBandStructureWorkChain, - code=Code.get_from_string("qe-6.4.1-pw@localhost"), - structure=load_node(), # REPLACE -) diff --git a/docs/pages/2019_Xiamen/sections/include/snippets/demo_calcjob.py b/docs/pages/2019_Xiamen/sections/include/snippets/demo_calcjob.py deleted file mode 100644 index 278731e3..00000000 --- a/docs/pages/2019_Xiamen/sections/include/snippets/demo_calcjob.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Run SCF calculation with Quantum ESPRESSO""" -from aiida.orm.nodes.data.upf import get_pseudos_from_structure -from aiida.engine import submit - -code = Code.get_from_string("") # REPLACE -builder = code.get_builder() - -# Select structure -structure = load_node() # REPLACE -builder.structure = structure - -# Define calculation -parameters = { - 'CONTROL': { - 'calculation': 'scf', # self-consistent field - }, - 'SYSTEM': { - 'ecutwfc': 30., # wave function cutoff in Ry - 'ecutrho': 240., # density cutoff in Ry - }, -} -builder.parameters = Dict(dict=parameters) - -# Select pseudopotentials -builder.pseudos = get_pseudos_from_structure(structure, '') # REPLACE - -# Define K-point mesh in reciprocal space -KpointsData = DataFactory('array.kpoints') -kpoints = KpointsData() -kpoints.set_kpoints_mesh([4,4,4]) -builder.kpoints = kpoints - -# Set resources -builder.metadata.options.resources = {'num_machines': 1} - -# Submit the job -calcjob = submit(builder) -print('Submitted CalcJob with PK=' + str(calcjob.pk)) diff --git a/docs/pages/2019_Xiamen/sections/include/snippets/workflows_create_diamond_fcc.py b/docs/pages/2019_Xiamen/sections/include/snippets/workflows_create_diamond_fcc.py deleted file mode 100644 index 0ad3d483..00000000 --- a/docs/pages/2019_Xiamen/sections/include/snippets/workflows_create_diamond_fcc.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import absolute_import -def create_diamond_fcc(element): - """Calculation function to create the crystal structure of a given element. - - For simplicity, only Si and Ge are valid elements. - - :param element: The element to create the structure with. - :return: The structure. - """ - from aiida import orm - - import numpy as np - elem_alat = { - 'Si': 5.431, # Angstrom - 'Ge': 5.658, # Angstrom - } - - # Validate input element - try: - symbol = element.value - except AttributeError: - symbol = str(element) - - if symbol not in elem_alat: - raise ValueError('Valid elements are only Si and Ge') - - # Create cell starting having lattice parameter alat corresponding to the element - alat = elem_alat[symbol] - cell = np.array([[0., 0.5, 0.5], [0.5, 0., 0.5], [0.5, 0.5, 0.]]) * alat - - # Create a structure data object - structure = orm.StructureData(cell=cell) - structure.append_atom(position=(0., 0., 0.), symbols=symbol) - structure.append_atom(position=(0.25 * alat, 0.25 * alat, 0.25 * alat), - symbols=symbol) - - return structure diff --git a/docs/pages/2019_Xiamen/sections/include/snippets/workflows_create_rescaled.py b/docs/pages/2019_Xiamen/sections/include/snippets/workflows_create_rescaled.py deleted file mode 100644 index c7d0a617..00000000 --- a/docs/pages/2019_Xiamen/sections/include/snippets/workflows_create_rescaled.py +++ /dev/null @@ -1,11 +0,0 @@ -from __future__ import absolute_import -from aiida.engine import workfunction - - -@workfunction -def create_rescaled(element, scale): - """Workfunction to create and immediately rescale a crystal structure of a given element.""" - structure = create_diamond_fcc(element) - rescaled = rescale(structure, scale) - - return rescaled diff --git a/docs/pages/2019_Xiamen/sections/include/snippets/workflows_rescale.py b/docs/pages/2019_Xiamen/sections/include/snippets/workflows_rescale.py deleted file mode 100644 index 99761a77..00000000 --- a/docs/pages/2019_Xiamen/sections/include/snippets/workflows_rescale.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import absolute_import -def rescale(structure, scale): - """Calculation function to rescale a structure - - :param structure: An AiiDA structure to rescale - :param scale: The scale factor (for the lattice constant) - :return: The rescaled structure - """ - from aiida import orm - - ase = structure.get_ase() - ase.set_cell(ase.get_cell() * float(scale), scale_atoms=True) - - return orm.StructureData(ase=ase) diff --git a/docs/pages/2019_Xiamen/sections/querybuilder.rst b/docs/pages/2019_Xiamen/sections/querybuilder.rst deleted file mode 100644 index 5d79091c..00000000 --- a/docs/pages/2019_Xiamen/sections/querybuilder.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _2019_xmn_querybuilder: - -Queries in AiiDA: The Querybuilder -================================== - -This part of the tutorial is provided only in interactive mode through a -Jupyter notebook, which you will be able to run in your browser. -For instructions on starting the Jupyter server, please refer to the -:ref:`setup section`. Once the server is running, -:download:`download this tutorial notebook <../notebooks/querybuilder-tutorial.ipynb>` -and open it in Jupyter. For instructions on downloading these files on -a machine through which you are connected through SSH, refer to -:ref:`this section`. - -The notebook will show you how the ``QueryBuilder`` can be used to query your -database for specific data. It will demonstrate certain concepts and then ask -you to use those to perform certain queries on your own database. Some of these -question cells will have partial solutions that you will have to complete. - -Once you have finished the notebook, you can download a -:download:`notebook with the solutions <../notebooks/querybuilder-solutions.ipynb>` -but try not to use them at first! - -See below for a rendered version of the notebook: - -.. toctree:: - - QueryBuilder Notebook <../notebooks/querybuilder-tutorial.ipynb> diff --git a/docs/pages/2019_Xiamen/sections/setup.rst b/docs/pages/2019_Xiamen/sections/setup.rst deleted file mode 100644 index dbc00904..00000000 --- a/docs/pages/2019_Xiamen/sections/setup.rst +++ /dev/null @@ -1,183 +0,0 @@ -Getting set up -============== - -.. _2019_xmn_connect: - -Start your virtual machine ---------------------------- - -On your Huawei cloud account: - - * Add a new security group ``aiida-tutorial``, open ports 22, 5000, 8888 - * Start a new server: - * Flavor ``s6.large.2`` (2 vCPUs, 4 GB RAM) should be sufficient - * Use shared image ``aiida-tutorial`` - * Use security group ``aiida-tutorial`` - * Use name ``workhorse`` (**important**) - * No need to set a password - * Copy the IP address of your new server - -Please ask your tutors for the password for the ``max`` user, -and read the instructions below on how to connect using your operating system. - -Linux and MacOS -~~~~~~~~~~~~~~~ - -It's recommended for you to place the ssh key you received in a folder -dedicated to your ssh configuration, to do so: - -- If not already present, create a ``.ssh`` directory in your home - (``mkdir ~/.ssh``), and set its permissions: ``chmod 700 ~/.ssh`` - -Add the following block your -``~/.ssh/config`` file: - - .. code:: bash - - Host aiidatutorial - Hostname IP_ADDRESS - User max - ForwardX11 yes - ForwardX11Trusted yes - LocalForward 8888 localhost:8888 - LocalForward 5000 localhost:5000 - ServerAliveInterval 120 - -replacing the IP address (``IP_ADDRESS``) and the ``NUM`` by -the one you received. - -Afterwards you can connect to the server using this simple command: - -.. code:: console - - ssh aiidatutorial - -.. note:: - - Here's a copy-paste ready command for you to use directly with zero - configuration: - - .. code:: console - - ssh \ - -L 8888:localhost:8888 \ - -L 5000:localhost:5000 \ - -o ServerAliveInterval=120 \ - -X -C \ - max@IP_ADDRESS - -.. note:: - - On MacOS you need to install `XQuartz `_ - in order to use X-forwarding. - -Windows -~~~~~~~ - -If you're running Windows 10, you may want to consider `installing the Windows Subsystem for Linux `_ (and then follow the instructions above). Alternatively: - -- Install the `PuTTY SSH client `_. - -- Run PuTTY - - - Put the given IP address as hostname, type ``aiidatutorial`` in "Saved Sessions" - and click "Save". - - Go to Connection > Data and put ``max`` as autologin username. - - Go to Connection > SSH > Tunnels, type ``8888`` in the - "Source Port" box, type ``localhost:8888`` in "Destination" and click "Add". - - Repeat the previous step for port ``5000`` instead of ``8888``. - - Go back to the "Session" screen, select "aiidatutorial" and click "Save" - - Finally, click "Open" (and click "Yes" on the putty security alert - to add the VM to your known hosts). - You should be redirected to a bash terminal on the virtual machine. - -.. note:: - Next time you open PuTTY, select ``aiidatutorial`` and click "Load" - before clicking "Open". - - -In order to enable X-forwarding: - -- Install the `Xming X Server for Windows `_. - -- Configure PuTTy as described in the `Xming wiki `_. - -.. _2019_xmn_setup_jupyter: - -Start jupyter -------------- - -Once connected to your virtual machine, type in the remote terminal - -.. code:: bash - - workon aiida - -This will enable the virtual environment in which AiiDA is installed, -allowing you to use AiiDA. Now type in the same shell: - -.. code:: bash - - jupyter notebook --no-browser - -This will run a server with a web application called ``jupyter``, which -is used to create interactive python notebooks. -In order to connect to the jupyter notebook server: - - - copy the URL that has been printed to the terminal (similar to ``http://localhost:8888/?token=2a3ba37cd1...``) - - open a web browser **on your laptop** and paste the URL - - You will see a list of folders on your personal VM. - -While keeping the first ``ssh`` connection running, open another ``ssh`` -connection in a second terminal and type ``workon aiida`` here too. This -terminal is the one we will actually use in this tutorial. - -.. note:: - - Our SSH configuration assumes that ``jupyter`` will serve the notebooks on port 8888. - If you want to serve notebooks on different ports, you'll also need to adjust - the SSH configuration. - - -.. _2019_xmn_setup_downloading_files: - -Downloading files ------------------ - -Throughout this tutorial, you will encounter links to download python scripts, jupyter notebooks and more. -These files should be downloaded to the environment/working directory you use to run the tutorial. -In particular, when running the tutorial on a linux virtual machine, copy the link address and download the files to the machine using the ``wget`` utility on the terminal: - - wget - -where you replace ```` with the actual HTTPS link that you copied from the tutorial text in your browser. -This will download that file in your current directory. - - -Troubleshooting ---------------- - -- If you get errors ``ImportError: No module named aiida`` or - ``No command ’verdi’ found``, double check that you have loaded the - virtual environment with ``workon aiida`` before launching ``python``, - ``ipython`` or the ``jupyter`` notebook server. - -- If your browser cannot connect to the jupyter notebook server, check that - you have correctly configured SSH tunneling/forwarding as described - above. Keep in mind that you need to start the jupyter server from the - terminal connected to the VM, while the web browser should be opened locally - on your laptop. - -- See the `jupyter notebook documentation `_ for compatibility of jupyter with various web browsers. - -Getting help ------------- - -There are a number of helpful resources available to you for getting more information about AiiDA. -Please consider: - - * consulting the extensive `AiiDA documentation `_ - * asking in the `Slack channel of the tutorial `_ - * opening a new issue on the `tutorial issue tracker `_ - * asking your neighbor - * asking a tutor diff --git a/docs/pages/2019_Xiamen/sections/verdi_cmdline.rst b/docs/pages/2019_Xiamen/sections/verdi_cmdline.rst deleted file mode 100644 index b3b2f32a..00000000 --- a/docs/pages/2019_Xiamen/sections/verdi_cmdline.rst +++ /dev/null @@ -1,536 +0,0 @@ -Verdi command line -================== - -This part of the tutorial will familiarize you with the ``verdi`` command-line interface (CLI), -which lets you manage your AiiDA installation, inspect the contents of your database, control running calculations and more. - - * The ``verdi`` command supports **tab-completion**: - In the terminal, type ``verdi``, followed by a space and press the 'Tab' key twice to show a list of all the available sub commands. - * For help on ``verdi`` or any of its subcommands, simply append the ``--help/-h`` flag, e.g.: - - .. code:: bash - - verdi quicksetup -h - -More details on ``verdi`` can be found in the `online documentation `_. - - -.. _2019_xmn_setup_verdi_quicksetup: - -Setting up a profile --------------------- - -After installing AiiDA, the first step is to create a "profile". -Typically, you would be using one profile per independent research project. - -The easiest way of setting up a new profile is through ``verdi quicksetup``. -Let's set up a new profile that we will use throughout this tutorial: - -.. code:: bash - - verdi quicksetup - -This will prompt you for some information, such as the name of the profile, your name, etc. -The information about you as a user will be associated with all the data that you create in AiiDA -and is important for attribution, when you start sharing your data with others. -After you have answered all the questions, a new profile will be created, along -with the required database and repository. - -.. note:: - - ``verdi quicksetup`` is a user-friendly wrapper around the ``verdi setup`` command that provides more control over the profile setup. - As explained in `the documentation `_, ``verdi setup`` expects certain external resources, such as the database to already have been pre-configured. - ``verdi quicksetup`` will try to do this for you, but may not be successful in certain environments. - -To see this profile, and any others that may have been configured, run: - -.. code:: bash - - verdi profile list - - Info: configuration folder: /home/max/.aiida - * generic - quicksetup - -Each line, ``generic`` and ``quicksetup`` in this example, corresponds to a profile. -The one marked with an asterisk is the "default" profile, meaning that any ``verdi`` command that you execute will be applied to that profile. - -.. note:: - - The output you get may differ. - The ``generic`` profile is pre-configured on the virtual machine built for the tutorial (but we are not going to use it here). - -Let's change the default profile to the newly created ``quicksetup`` for the rest of the tutorial: - -.. code:: bash - - verdi profile setdefault quicksetup - -From now on, all ``verdi`` commands will apply to the ``quicksetup`` profile. - -.. note:: - - To quickly perform a single command on a profile that is not the default, use the ``-p/--profile`` option: - For example, ``verdi -p generic profile show`` will display the configuration of the ``generic`` profile, despite it not being the current default profile. - - -Importing data --------------- - -Before we start running calculations ourselves, we are going to look at an AiiDA database already created by someone else. -Let's import one from the web: - -.. code:: bash - - verdi import https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2019_05_perovskites_v0.3.aiida - -Contrary to most databases, AiiDA databases contain not only *results* of calculations but also their inputs and information on how a particular result was obtained. -This information, the *data provenance*, is stored in the form of a *directed acyclic graph* (DAG). -In the following, we are going to introduce you to different ways of browsing this graph and will ask you to find out some information regarding the database you just imported. - -.. _2019_xmn_aiidagraph: - -Your first AiiDA graph ----------------------- - -:numref:`2019_xmn_fig_graph_input_only` shows a typcial example of a calculation represented in an AiiDA graph. -Have a look to the figure and its caption before moving on. - -.. _2019_xmn_fig_graph_input_only: -.. figure:: include/images/verdi_graph/batio3/graph-input.png - :width: 100% - - Graph with all inputs (data, circles; and code, diamond) to the Quantum ESPRESSO calculation (square) that you will create in the :ref:`calculations` section of this tutorial. - -.. _2019_xmn_fig_graph: -.. figure:: include/images/verdi_graph/batio3/graph-full.png - :width: 100% - - Same as :numref:`2019_xmn_fig_graph_input_only`, but also with the outputs that the engine will create and connect automatically. - The ``RemoteData`` node is created during submission and can be thought as a symbolic link to the remote folder in which the calculation runs on the cluster. - The other nodes are created when the calculation has finished, after retrieval and parsing. - The node with linkname 'retrieved' contains the raw output files stored in the AiiDA repository; all other nodes are added by the parser. - Additional nodes (symbolized in gray) can be added by the parser (e.g. an output ``StructureData`` if you performed a relaxation calculation, a ``TrajectoryData`` for molecular dynamics etc.). - -:numref:`2019_xmn_fig_graph_input_only` was drawn by hand but you can generate a similar graph automatically by passing the **identifier** of a calculation node to ``verdi graph generate ``. -Identifiers in AiiDA come in three forms: - - * "Primary Key" (PK): An integer, e.g. ``723``, that identifies your entity within your database (automatically assigned) - * `Universally Unique Identifier `_ (UUID): A string, e.g. ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` that identifies your entity globally (automatically assigned) - * Label: A human-readable string, e.g. ``test_qe_calculation`` (manually assigned) - -Any ``verdi`` command that expects an identifier will accept a PK, a UUID or a label (although not all entities have a label by default). -While PKs are often shorter than UUIDs and can be easier to remember, they are only unique within your database. -**Whenever you intend to share your data with others, use UUIDs to refer to nodes.** - -.. note:: - For UUIDs, it is sufficient to specify a subset (starting at the beginning) as long as it can already be uniquely resolved. - For more information on identifiers in ``verdi`` and AiiDA in general, see the `documentation `_. - -For the remainder of this section, fields enclosed in angular brackets, such as ````, are placeholders that you should replace before executing the command. -With that in mind, let's generate a graph for the calculation node with UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``: - -.. code:: bash - - verdi graph generate - -This command will create the file ``.dot`` that can be rendered by means of the utility ``dot`` as follows: - -.. code:: bash - - dot -Tpdf -o .pdf .dot - -which will create a pdf file ``.pdf``. - - -You can open this file on the Amazon machine by using ``evince`` or, if the ssh connection is too slow, copy it via ``scp`` to your local machine. -To do so, if you are using Linux/Mac OS X, you can type in your *local* machine: - -.. code:: bash - - scp aiidatutorial: - -and then open the file. -Alternatively, you can use graphical software to achieve the same, for instance: WinSCP on Windows, Cyberduck on the Mac, or the 'Connect to server' option in the main menu after clicking on the desktop for Ubuntu. - - -The provenance browser ----------------------- - -While the ``verdi`` CLI provides full access to the data underlying the provenance graph, a more intuitive tool for browsing AiiDA graphs is the interactive provenance browser available on `Materials Cloud `__. - -In order to use it, we first need to start the `AiiDA REST API `_: - -.. code:: bash - - verdi restapi - * Serving Flask app "aiida.restapi.run_api" (lazy loading) - * Environment: production - WARNING: Do not use the development server in a production environment. - Use a production WSGI server instead. - * Debug mode: off - * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) - -Now you can connect the provenance browser to your local REST API: - -- Open the |provenance_browser| on your laptop -- In the form, paste the (local) URL ``http://127.0.0.1:5000/api/v3`` - of our REST API -- Click "GO!" - -.. |provenance_browser| raw:: html - - provenance explorer - -Once the provenance browser javascript application has been loaded by your browser, it is communicating directly with the REST API and your data never leaves your computer. - -.. note:: - In order for this to work on your laptop, while the REST API is running on the virtual machine, we've enabled SSH tunneling for port ``5000`` in :ref:`2019_xmn_connect`. - -Start by clicking on the Details of a ``CalcJobNode`` and use the graph explorer to complete the exercise below. -If you ever get lost, just go to the "Details" tab, enter ``ce81c420-7751-48f6-af8e-eb7c6a30cec3`` and click on the "GO" button. - -.. admonition:: Exercise - - Use the provenance browser in order to figure out: - - - When was the calculation run and who run it? - - Was it a serial or a parallel calculation? How many MPI processes were used? - - What inputs did the calculation take? - - What code was used and what was the name of the executable? - - How many calculations were performed using this code? - - -Processes ---------- - -Anything that 'runs' in AiiDA, be it calculations or workflows, is considered a ``Process``. -To get a list of currently running processes, use: - -.. code:: bash - - verdi process list - -.. note:: - - The first time you run this command, it might take a few seconds. - Subsequent calls will be faster. - -which should be empty: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - - Total results: 0 - - Info: last time an entry changed state: never - -Let's see whether there are any *finished* processes in the database by passing the ``-S/--process-state`` flag: - -.. code:: bash - - verdi process list -S finished - -This command will list all the processes that have a process state ``Finished`` and should look something like: - -.. code:: bash - - PK Created Process label Process State Process status - ---- --------- -------------- --------------- ---------------- - 1178 1653D ago PwCalculaton ⏹ Finished [0] - 1953 1653D ago PwCalculaton ⏹ Finished [0] - 1734 1653D ago PwCalculaton ⏹ Finished [0] - 336 1653D ago PwCalculaton ⏹ Finished [0] - 1056 1653D ago PwCalculaton ⏹ Finished [0] - 1369 1653D ago PwCalculaton ⏹ Finished [0] - - Total results: 6 - - Info: last time an entry changed state: never - -Processes can be in any of the following states: - - * ``Created`` - * ``Waiting`` - * ``Running`` - * ``Finished`` - * ``Excepted`` - * ``Killed`` - -The first three states are 'active' states, meaning the process is not done yet, and the last three are 'terminal' states. -Once a process is in a terminal state, it will never become active again. -The `official documentation `_ contains more details on process states. - -In order to list processes of *all* states, use the ``-a/--all`` flag: - -.. code:: bash - - verdi process list -a - -This command will list all the processes that have *ever* been launched. -As your database will grow, so will the output of this command. -To limit the number of results, you can use the ``-p/--past-days `` option, that will only show processes that were created ``NUM`` days ago. -For example, this lists all processes launched since yesterday: - -.. code:: bash - - verdi process list -a -p1 - -.. _2019-aiida-identifiers: - -Each row of the output identifies a process with some basic information about its status. -For a more detailed list of properties, you can use ``verdi process show``, but to address any specific process, you need an identifier for it. - -Let's revisit the process with the UUID ``ce81c420-7751-48f6-af8e-eb7c6a30cec3``, this time using the CLI: - -.. code:: bash - - verdi process show ce81c420-7751-48f6-af8e-eb7c6a30cec - -Producing the output: - -.. code:: bash - - Property Value - ------------- ------------------------------------ - type CalcJobNode - pk 828 - uuid ce81c420-7751-48f6-af8e-eb7c6a30cec3 - label - description - ctime 2014-10-27 17:51:21.781045+00:00 - mtime 2019-05-09 14:10:09.307986+00:00 - process state Finished - exit status 0 - computer [1] daint - - Inputs PK Type - ---------- ---- ------------- - pseudos - Ba 611 UpfData - O 661 UpfData - Ti 989 UpfData - code 825 Code - kpoints 811 KpointsData - parameters 829 Dict - settings 813 Dict - structure 27 StructureData - - Outputs PK Type - ----------------------- ---- ------------- - output_kpoints 1894 KpointsData - output_parameters 62 Dict - output_structure 61 StructureData - output_trajectory_array 63 ArrayData - remote_folder 357 RemoteData - retrieved 60 FolderData - -You can use the PKs shown for the inputs and outputs to get more information about those nodes. - -.. warning:: - - Since the inputs and outputs are ``Data`` nodes, not ``Process`` nodes, use ``verdi node show`` instead. - - -Dict and CalcJobNode -~~~~~~~~~~~~~~~~~~~~ - -Let's investigate some of the nodes appearing in the graph. -From the inputs of the process, let's choose the node of type ``Dict`` with input link name ``parameters`` and type in the terminal: - -.. code:: bash - - verdi data dict show - -where ```` is the PK of the node. - -A ``Dict`` contains a dictionary (i.e. key–value pairs), stored in the database in a format ready to be queried. -We will learn how to run queries later on in this tutorial. -The command above will print the content dictionary, containing the parameters used to define the input file for the calculation. -You can compare the dictionary with the content of the raw input file to Quantum ESPRESSO (that was generated by AiiDA) via the command: - -.. code:: bash - - verdi calcjob inputcat - -where you provide the identifier of the calculation node. -Check the consistency of the parameters written in the input file and those stored in the ``Dict`` node. -Even if you don't know the meaning of the input flags of a Quantum ESPRESSO calculation, you should be able to see how the input dictionary has been converted to Fortran namelists. - -The previous command just printed the content of the 'default' input file ``aiida.in``. -To see a list of all the files used to run a calculation (input file, submission script, etc.) instead type: - -.. code:: bash - - verdi calcjob inputls - -Adding a ``--color`` flag allows you to easily distinguish files from folders by a different coloring. -Once you know the name of the file you want to visualize, you can call the ``verdi calcjob inputcat [PATH]`` command specifying the path. -For instance, to see the submission script, you can do: - -.. code:: bash - - verdi calcjob inputcat _aiidasubmit.sh - -StructureData -~~~~~~~~~~~~~ - -Now let us focus on ``StructureData`` objects, which represent a crystal structure. -We can consider for instance the input structure to the calculation we were considering before (it should have the UUID ``3a4b1270``). -Such objects can be inspected interactively by means of an atomic viewer such as the one provided by ``ase``. -AiiDA however supports several other viewers such as ``xcrysden``, ``jmol``, and ``vmd``. -Type in the terminal: - -.. code:: bash - - verdi data structure show --format ase - -to show the selected structure, although it will take a few seconds to appear -You should be able to rotate the view with the right mouse button. - -.. note:: - - If you receive some errors, make sure you started your SSH connection with the ``-X`` or ``-Y`` flag. - -Alternatively, especially if showing them interactively is too slow over SSH, you can export the content of a structure node in various popular formats such as ``xyz`` or ``xsf``. -This is achieved by typing in the terminal: - -.. code:: bash - - # verdi data structure export --format xsf > .xsf - verdi data structure export --format xsf 254e5a86 > 254e5a86.xsf - -You can open the generated ``xsf`` file and observe the cell and the coordinates. -Then, you can then copy ``.xsf`` from the Amazon machine to your local one and then visualize it, e.g. with ``xcrysden`` (if you have it installed): - -.. code:: bash - - xcrysden --xsf .xsf - -Codes and computers -~~~~~~~~~~~~~~~~~~~ - -Let us focus now on the nodes of type ``Code``. -A code represents (in the database) the actual executable used to run the calculation. -Find the identifier of such a node in the graph and type: - -.. code:: bash - - verdi code show - -The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc. -To show a list of all available codes type: - -.. code:: bash - - verdi code list - -If you want to show all codes, including hidden ones and those created by other users, use ``verdi code list -a -A``. -Now, among the entries of the output you should also find the code just shown. - -Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command: - -.. code:: bash - - verdi computer list -a - -The ``-a`` flag shows all computers, also the one imported in your database but that you did not configure, i.e. to which you don't have access. -Details about each computer can be obtained by the command: - -.. code:: bash - - verdi computer show - -Now you have the tools to answer the question: what is the scheduler installed on the computer where the calculations of the graph have run? - -Calculation results -~~~~~~~~~~~~~~~~~~~ - -The results of a calculation can be accessed directly from the calculation node. -Type in the terminal: - -.. code:: bash - - verdi calcjob res - -which will print the output dictionary of the 'scalar' results parsed by AiiDA at the end of the calculation. -Note that this is actually a shortcut for: - -.. code:: bash - - verdi data dict show - -where ``IDENTIFIER`` refers to the ``Dict`` node attached as an output of the calculation node, with link name ``output_parameters``. -By looking at the output of the command, what is the Fermi energy of the calculation with UUID ``ce81c420``? - -Similarly to what you did for the calculation inputs, you can access the output files via the commands: - -.. code:: bash - - verdi calcjob outputls - -and - -.. code:: bash - - verdi calcjob outputcat - -Use the latter to verify that the Fermi energy that you have found in the last step has been extracted correctly from the output file - -.. note:: - - Hint: filter the lines containing the string 'Fermi', e.g. using ``grep``, to isolate the relevant lines - -The results of calculations are stored in two ways: ``Dict`` objects are stored in the database, which makes querying them very convenient, whereas ``ArrayData`` objects are stored on the disk. -Once more, use the command ``verdi data array show `` to determine the Fermi energy obtained from calculation with the UUID ``ce81c420``. -This time you will need to use the identifier of the output ``ArrayData`` of the calculation, with link name ``output_trajectory_array``. -As you might have realized the difference now is that the whole series of values of the Fermi energy calculated after each relax/vc-relax step are stored. -The choice of what to store in ``Dict`` and ``ArrayData`` nodes is made by the parser of ``pw.x`` implemented in the ``aiida-quantumespresso`` plugin. - -(Optional section) Comments -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -AiiDA offers the possibility to attach comments to a any node, in order to be able to remember more easily its details. -Node with UUID prefix ``ce81c420`` should have no comments, but you can add a very instructive one by typing in the terminal: - -.. code:: bash - - verdi comment add "vc-relax of a BaTiO3 done with QE pw.x" -N - -Now, if you ask for a list of all comments associated to that calculation by typing: - -.. code:: bash - - verdi comment show - -the comment that you just added will appear together with some useful information such as its creator and creation date. -We let you play with the other options of ``verdi comment`` command to learn how to update or remove comments. - -AiiDA groups of calculations ----------------------------- - -In AiiDA, calculations (and more generally nodes) can be organized in groups, which are particularly useful to assign a set of calculations or data to a common project. -This allows you to have quick access to a whole set of calculations with no need for tedious browsing of the database or writing complex scripts for retrieving the desired nodes. -Type in the terminal: - -.. code:: bash - - verdi group list -a -A - -to show a list of **all** groups that exist in the database. -Choose the PK of the group named ``tutorial_pbesol`` and look at the calculations that it contains by typing: - -.. code:: bash - - verdi group show - -In this case, we have used the name of the group to organize calculations according to the pseudopotential that has been used to perform them. -Among the rows printed by the last command you will be able to find the calculation we have been inspecting until now. - -If, instead, you want to know all the groups to which a specific node belongs, you can run: - -.. code:: bash - - verdi group list -N/--node diff --git a/docs/pages/2019_Xiamen/sections/verdi_shell.rst b/docs/pages/2019_Xiamen/sections/verdi_shell.rst deleted file mode 100644 index f4568064..00000000 --- a/docs/pages/2019_Xiamen/sections/verdi_shell.rst +++ /dev/null @@ -1,416 +0,0 @@ -Verdi shell and AiiDA objects -============================= - -In this section we will use an interactive IPython environment with all the -basic AiiDA classes already loaded. We propose two realizations of such a -tool. The first consists of a special IPython shell where all the AiiDA -classes, methods and functions are accessible. Type in the terminal - -.. code:: bash - - verdi shell - -For all the everyday AiiDA-based operations, i.e. creating, querying, and -using AiiDA objects, the ``verdi shell`` is probably the best tool. In this -case, we suggest that you use two terminals, one for the ``verdi shell`` and -one to execute bash commands. - -The second option is based on Jupyter notebooks and is probably most suitable -to the purposes of our tutorial. Go to the browser where you have opened -``jupyter`` and click ``New`` → ``Python 3`` (top right corner). This will -open an IPython-based Jupyter notebook, made of cells in which you can type -portions of python code. The code will not be executed until you press -``Shift+Enter`` from within a cell. Type in the first cell - -.. code:: ipython - - %aiida - -and execute it. This will set exactly the same environment as the -``verdi shell``. The notebook will be automatically saved upon any -modification and when you think you are done, you can export your notebook in -many formats by going to ``File`` → ``Download as``. We suggest you to have a -look at the drop-down menus ``Insert`` and ``Cell`` where you will find the -main commands to manage the cells of your notebook. - -.. note:: - - The ``verdi shell`` and Jupyter - notebook are completely equivalent. Use either according to your - personal preference. - -You will still sometimes need to type command-line instructions in ``bash`` in -the first terminal you opened. To differentiate these from the commands to be -typed in the ``verdi shell``, the latter will be marked in this document by a -green background, like: - -.. code:: python - - some verdi shell command - -while command-line instructions in ``bash`` to be typed into a terminal will -be written with a blue background: - -.. code:: bash - - some bash command - -Alternatively, to avoid changing terminal, you can execute ``bash`` commands -within the ``verdi shell`` or the notebook by adding an exclamation mark before -the command itself: - -.. code:: ipython - - !some bash command - -.. _2019_xmn_loadnode: - -Loading a node --------------- - -Most AiiDA objects are represented by nodes, identified in the database by its -``PK`` (an integer). You can access a node using the following command -in the shell: - -.. code:: python - - node = load_node(PK) - -Load a node using one of the calculation ``PK`` s visible in the graph you -displayed in the previous section of the tutorial. Then get the energy of the -calculation with the command - -.. code:: python - - node.res.energy - -You can also type - -.. code:: python - - node.res. - -and then press ``TAB`` to see all the available output results of the -calculation. - -Loading specific kinds of nodes -------------------------------- - -Pseudopotentials -~~~~~~~~~~~~~~~~ - -From the graph you generated in section :ref:`2019_xmn_aiidagraph`, -find the ``PK`` of the pseudopotential file (LDA). Load it and -show what elements it corresponds to by typing: - -.. code:: python - - upf = load_node(PK) - upf.element - -All methods of ``UpfData`` are accessible by typing ``upf.`` and then pressing ``TAB``. - -k-points -~~~~~~~~ - -A set of k-points in the Brillouin zone is represented by an instance of the -``KpointsData`` class. Choose one from the graph of -produced in section :ref:`2019_xmn_aiidagraph`, -load it as ``kpoints`` and inspect its content: - -.. code:: python - - kpoints.get_kpoints_mesh() - -Then get the full (explicit) list of k-points belonging to this mesh using - -.. code:: python - - kpoints.get_kpoints_mesh(print_list=True) - -If this throws an ``AttributeError``, it means that the kpoints instance does not represent a regular mesh but rather a list of k-points defined by their crystal coordinates (typically used when plotting a band structure). -In this case, get the list of k-points coordinates using - -.. code:: python - - kpoints.get_kpoints() - -Conversely, if the `KpointsData` node `does` actually represent a mesh, this method is the one, that when called, will throw an ``AttributeError``. - -If you prefer Cartesian (rather than crystal) coordinates, type - -.. code:: python - - kpoints.get_kpoints(cartesian=True) - -For later use in this tutorial, let us try now to create a kpoints instance, -to describe a regular (2 x 2 x 2) mesh of k-points, centered at the Gamma -point (i.e. without offset). This can be done with the following commands: - -.. code:: python - - KpointsData = DataFactory('array.kpoints') - kpoints = KpointsData() - kpoints_mesh = 2 - kpoints.set_kpoints_mesh([kpoints_mesh] * 3) - kpoints.store() - -This function loads the appropriate class defined in a string (here -``array.kpoints``). Therefore, ``KpointsData`` is not a class instance, but -the kpoints class itself! - -While it is also possible to import ``KpointsData`` directly, it is recommended -to use the ``DataFactory`` function instead, as this is more future-proof: -even if the import path of the class changes in the future, its entry point -string (``array.kpoints``) will remain stable. - -Parameters -~~~~~~~~~~ - -Dictionaries with various parameters are represented in AiiDA by ``Dict`` nodes. -Get the PK and load the input parameters of a calculation in the graph produced in section :ref:`2019_xmn_aiidagraph`. -Then display its content by typing - -.. code:: python - - params = load_node('') - YOUR_DICT = params.get_dict() - YOUR_DICT - -Modify the python dictionary ``YOUR_DICT`` so that the wave-function cutoff is now set to 20 -Ry. Note that you cannot modify an object already stored in the database. To -write the modified dictionary to the database, create a new object of class ``Dict``: - -.. code:: python - - Dict = DataFactory('dict') - new_params = Dict(dict=YOUR_DICT) - -where ``YOUR_DICT`` is the modified python dictionary. -Note that ``new_params`` is not yet stored in the database. -In fact, typing ``new_params`` in the verdi shell will print a string notifying you of its 'unstored' status. -Let's finish by storing the ``new_params`` dictionary node in the datbase: - -.. code:: python - - new_params.store() - -Structures -~~~~~~~~~~ - -Find a structure in the graph you generated in section :ref:`2019_xmn_aiidagraph` and load it. -Display its chemical formula, atomic positions and species using - -.. code:: python - - structure.get_formula() - structure.sites - -where ``structure`` is the structure you loaded. If you are familiar with ASE -and PYMATGEN, you can convert this structure to those formats by typing - -.. code:: python - - structure.get_ase() - structure.get_pymatgen() - -Let’s try now to define a new structure to study, specifically a silicon -crystal. In the ``verdi shell``, define a cubic unit cell as a 3 x 3 matrix, -with lattice parameter `a`\ :sub:`lat`\ `= 5.4` Å: - -.. code:: python - - alat = 5.4 - the_cell = [[alat/2, alat/2, 0.], [alat/2, 0., alat/2], [0., alat/2, alat/2]] - -.. note:: - - Default units for crystal structure cell and coordinates in AiiDA are Å (Ångström). - -Structures in AiiDA are instances of the class ``StructureData``: load it in the -verdi shell - -.. code:: python - - StructureData = DataFactory('structure') - -Now, initialize the class instance (i.e. the actual structure we want to study) by -the command - -.. code:: python - - structure = StructureData(cell=the_cell) - -which sets the cubic cell defined before. From now on, you can access the cell -with the command - -.. code:: python - - structure.cell - -Finally, append each of the 2 atoms of the cell command. You can do it using -commands like - -.. code:: python - - structure.append_atom(position=(alat/4., alat/4., alat/4.), symbols="Si") - -for the first ‘Si’ atom. Repeat it for the other atomic site (0, 0, 0). You -can access and inspect the structure sites with the command - -.. code:: python - - structure.sites - -If you make a mistake, start over from -``structure = StructureData(cell=the_cell)``, or equivalently use -``structure.clear_kinds()`` to remove all kinds (atomic species) and sites. -Alternatively, AiiDA structures can also be converted directly from ASE -structures [#f1]_ using - -.. code:: python - - from ase.spacegroup import crystal - ase_structure = crystal('Si', [(0, 0, 0)], spacegroup=227, - cellpar=[alat, alat, alat, 90, 90, 90], primitive_cell=True) - structure = StructureData(ase=ase_structure) - -Now you can store the new structure object in the database with the command: - -.. code:: python - - structure.store() - -Finally, we can also import the silicon structure from an external (online) -repository such as the Crystallography Open Database (COD): - -.. code:: python - - from aiida.tools.dbimporters.plugins.cod import CodDbImporter - importer = CodDbImporter() - for entry in importer.query(formula='Si', spacegroup='F d -3 m'): - structure = entry.get_aiida_structure() - print("Formula", structure.get_formula()) - print("Unit cell volume: ", structure.get_cell_volume()) - -In that case two duplicate structures are found for 'Si'. - -Accessing inputs and outputs ----------------------------- - -Load again the calculation node used in Section :ref:`2019_xmn_loadnode`: - -.. code:: python - - calc = load_node(PK) - -Then type - -.. code:: python - - calc.inputs. - -and press ``TAB``: you will see all the link names between the calculation and -its input nodes. You can use a specific linkname to access the corresponding -input node, e.g.: - -.. code:: python - - calc.inputs.structure - -Similarly, if you type: - -.. code:: python - - calc.outputs. - -and then ``TAB``, you will list all output link names of the calculation. One -of them leads to the structure that was the input of ``calc`` we loaded -previously: - -.. code:: python - - calc.outputs.output_structure - -Note that links have a single name, that was assigned by the calculation that -used the corresponding input or produced the corresponding output, as -illustrated in section :ref:`2019_xmn_aiidagraph`. - -For a more programmatic approach, you can get a represenation of the inputs and outputs of a node, say ``calc``, through the following methods: - -.. code:: python - - calc_incoming = calc.get_incoming() - calc_outgoing = calc.get_outgoing() - -These methods will return an instance of the ``LinkManager`` class. -You can iterate over the neighboring nodes by calling the ``.all()`` method: - -.. code:: python - - for entry in calc.get_outgoing(): - print(entry.link_label, entry.link_type, entry.node) - -each entry returned by ``.all()`` is a ``LinkTriple``, a named tuple, from which you can get the link label and type and the neighboring node itself. -If you print one, you will see something like: - -.. code:: python - - LinkTriple(node=, link_type=, link_label=u'output_parameters') - -There are many other convenience methods on the ``LinkManager``. -For example if you are only interested in the link labels you can use: - -.. code:: python - - calc.get_outgoing().all_link_labels() - -which will return a list of all the labels of the outgoing links. -Likewise, ``.all_nodes()`` will give you a list of all the nodes to which links are going out from the ``calc`` node. -If you are looking for the node with a specific label, you can use: - -.. code:: python - - calc.get_outgoing().get_node_by_label('output_parameters') - -The ``get_outgoing`` and ``get_incoming`` methods also support filtering on various properties, such as the link label. -For example, if you only want to get the outgoing links whose label starts with ``output``, you can do the following: - -.. code:: python - - calc.get_outgoing(link_label_filter='output%').all_nodes() - - -Pseudopotential families ------------------------- - -Pseudopotentials in AiiDA are grouped in 'families' that contain one single -pseudo per element. We will see how to work with UPF pseudopotentials (the -format used by Quantum ESPRESSO and some other codes). Download and untar the -SSSP pseudopotentials via the commands: - -.. code:: bash - - wget https://archive.materialscloud.org/file/2018.0001/v3/SSSP_efficiency_pseudos.tar.gz - tar -zxvf SSSP_efficiency_pseudos.tar.gz - -Then you can upload the whole set of pseudopotentials to AiiDA by using the -following ``verdi`` command: - -.. code:: bash - - verdi data upf uploadfamily SSSP_efficiency_pseudos 'SSSP' 'SSSP pseudopotential library' - -In the command above, ``SSSP_efficiency_pseudos`` is the folder containing the -pseudopotentials, ``'SSSP'`` is the name given to the family, and the last argument -is its description. Finally, you can list all the pseudo families present in -the database with - -.. code:: bash - - verdi data upf listfamilies - - -.. rubric:: Footnotes - -.. [#f1] We purposefully do not provide advanced commands for crystal structure manipulation in AiiDA, because python packages that accomplish such tasks already exist (such as ASE or pymatgen). diff --git a/docs/pages/2019_Xiamen/sections/workflows.rst b/docs/pages/2019_Xiamen/sections/workflows.rst deleted file mode 100644 index a85c593e..00000000 --- a/docs/pages/2019_Xiamen/sections/workflows.rst +++ /dev/null @@ -1,512 +0,0 @@ -Workflows -========= - -The aim of the last part of this tutorial is to introduce the concept of workflows in AiiDA. - -In this section, we will ask you to: - -1. Understand how to keep the provenance when running small python scripts to convert one data object into another (postprocessing, preparation of inputs, etc.) -2. Understand how to represent simple python functions in the AiiDA database -3. Learn how to write a simple workflow in AiiDA (without and with remote calculation submission) -4. Learn how to write a workflow with checkpoints: this means that, even if your workflow requires external calculations to start, they and their dependencies are managed through the daemon. - While you are waiting for the calculations to complete, you can stop and even shutdown the computer in which AiiDA is running. - When you restart, the workflow will continue from where it was. -5. (optional) Go a bit deeper in the syntax of workflows with checkpoints (``WorkChain``), e.g. implementing a convergence workflow using ``while`` loops. - -.. note:: - - This is probably the most 'complex' part of the tutorial. - We suggest that you try to understand the underlying logic behind the scripts, without focusing too much on the details of the workflows implementation or the syntax. - If you want, you can then focus more on the technicalities in a second reading. - -Introduction ------------- - -The ultimate aim of this section is to create a workflow to calculate the equation of state of silicon. -This is a very common task for an *ab initio* researcher. -An equation of state consists in calculating the total energy (E) as a function of the unit cell volume (V). -The minimal energy is reached at the equilibrium volume. -Equivalently, the equilibrium is defined by a vanishing pressure (p=-dE/dV). -In the vicinity of the minimum, the functional form of the equation of state can be approximated by a parabola. -Such an approximation greatly simplifies the calculation of the bulk modulus, that is proportional to the second derivative of the energy (a more advanced treatment requires fitting the curve with, e.g., the Birch–Murnaghan expression). - -The process of calculating an equation of state puts together several operations. -First, we need to define and store in the AiiDA database the basic structure of, e.g., bulk Si. -Next, one has to define several structures with different lattice parameters. -Those structures must be connected between them in the database, in order to ensure that their provenance is recorded. -In other words, we want to be sure that in the future we will know that if we find a bunch of rescaled structures in the database, they all descend from the same one. -How to link two nodes in the database in an easy way is the subject of :ref:`2019_xmn_provenancewf`. - -In the following sections, the newly created structures will then serve as an input for total energy calculations performed, in this tutorial, with Quantum ESPRESSO. -This task is very similar to what you have done in the previous part of the tutorial. -Finally, you will fit the resulting energies as a function of volume to get the bulk modulus. -As the EOS task is very common, we will show how to automate its computation with workflows, and how to deal with both serial and parallel (i.e., independent) execution of multiple tasks. -Finally, we will show how to introduce more complex logic in your workflows such as loops and conditional statements (:ref:`see this section<2019_xmn_convpressure>`), with an example on a convergence loop to find iteratively the minimum of an EOS. - -.. _2019_xmn_provenancewf: - -Process functions: a way to generalize provenance in AiiDA ----------------------------------------------------------- - -Imagine having a function that takes as input a string of the name of a chemical element and generates the corresponding bulk structure as a ``StructureData`` object. -The function might look like the following snippet: - -.. literalinclude:: include/snippets/workflows_create_diamond_fcc.py - :language: python - -For the equation of state you need another function that takes as input a ``StructureData`` object and a rescaling factor, and returns a ``StructureData`` object with the rescaled lattice parameter: - -.. literalinclude:: include/snippets/workflows_rescale.py - :language: python - -In order to generate the rescaled starting structures, say for five different lattice parameters you would combine the two functions. -Open a ``verdi shell``, define the two functions from the previous snippets and enter the following commands: - -.. code:: python - - initial_structure = create_diamond_fcc('Si') - rescaled_structures = [rescale(initial_structure, factor) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and store them in the database: - -.. code:: python - - initial_structure.store() - for structure in rescaled_structures: - structure.store() - -As expected, all the structures that you have created are not linked in any manner as you can verify via the ``get_incoming()``/``get_outgoing()`` methods of the ``StuctureData`` class. -Instead, you would like these objects to be connected as sketched in :numref:`2019_xmn_fig_provenance_process_functions`. - -.. _2019_xmn_fig_provenance_process_functions: -.. figure:: include/images/process_functions.png - - Typical graphs created by using calculation and work functions. - (a) The calculation function ``create_structure`` takes a ``Str`` object as input and returns a single ``StructureData`` object which is used as input for the calculation function ``rescale`` together with a ``Float`` object. - This latter calculation function returns another ``StructureData`` object, defining a crystal with a rescaled lattice constant. - (b) Graph generated by a work function that calls two calculation functions. - A wrapper work function ``create_rescaled`` calls serially the calculation functions ``create_structure`` and ``rescale``. - This relationship is stored via ``CALL`` links. - - -Now that you are familiar with AiiDA, you know that the way to connect two data nodes is through a calculation. -In order to 'wrap' python functions and automate the generation of the needed links, in AiiDA we provide you with what we call 'process functions'. -There are two variants of process functions: - - * calculation functions - * work functions - -These operate mostly the same, but they should be used for different purposes, which will become clear later. -A normal function can be converted to a calculation function by using the ``@calcfunction`` decorator [#f1]_ that takes care of storing the execution as a calculation and adding the links between the input and output data nodes. - -To turn the original functions ``create_diamond_fcc`` and ``rescale`` into calculation functions, simply change the definition as follows: - -.. code:: python - - # Add this import - from aiida.engine import calcfunction - - # Add decorators - @calcfunction - def create_diamond_fcc(element): - ... (same as above) - - @calcfunction - def rescale(structure, scale): - ... (same as above) - -Note that the only change is that the function definitions were 'decorated' with the ``@calcfunction`` line. -This is the only thing that is necessary to transform the plain python functions magically into fully-fledged AiiDA process functions. - -.. note:: - - The only additional change necessary, is that process function input and outputs need to be ``Data`` nodes, so that they can be stored in the database. - AiiDA objects such as ``StructureData``, ``Dict``, etc. carry around information about their provenance as stored in the database. - This is why we must use the special database-storable types ``Float``, ``Str``, etc. as shown in the snippet below. - -Try now to run the following script: - -.. code:: python - - from aiida.orm import Float, Str - initial_structure = create_diamond_fcc(Str('Si')) - rescaled_structures = [rescale(initial_structure, Float(factor)) for factor in (0.98, 0.99, 1.0, 1.1, 1.2)] - -and check now that the output of ``initial_structure`` as well as the input of the rescaled structures point to an intermediate node, representing the execution of the calculation function, see :numref:`2019_xmn_fig_provenance_process_functions`. -For instance, you can check that the output links of ``initial_structure`` are the five ``rescale`` calculations: - -.. code:: python - - initial_structure.get_outgoing().all_nodes() - -which outputs - -.. code:: bash - - [, - , - , - , - ] - -and the inputs of each ``CalcFunctionNode`` 'rescale' are obtained with: - -.. code:: python - - for structure in initial_structure.get_outgoing().all_nodes(): - print(structure.get_incoming().all_nodes()) - -that will return - -.. code:: bash - - [, ] - [, ] - [, ] - [, ] - [, ] - -Function nesting -~~~~~~~~~~~~~~~~ -Calculation functions can be 'chained' together by wrapping them together in a work function. -The work function works almost exactly the same as a calculation function, except that it cannot 'create' data, but rather is used to 'call' calculation functions that do the calculations for it. -The calculation functions that it calls will be automatically linked in the provenance graph through 'call' links. -As an example, let us combine the two previously defined calculation functions by means of a wrapper work function called 'create_rescaled' that takes as input the element and the rescale factor. - -Type in your shell (or modify the functions defined in ``create_rescale.py`` and then run): - -.. include:: include/snippets/workflows_create_rescaled.py - :code: python - -and create an already rescaled structure by typing - -.. code:: python - - rescaled = create_rescaled(element=Str('Si'), scale=Float(0.98)) - -Now inspect the input links of ``rescaled``: - -.. code:: ipython - - In [6]: rescaled.get_incoming().all_nodes() - Out[6]: - [, - ] - -The object ``rescaled`` has two incoming links, corresponding to *two* different calculations as input. -These correspond to the calculations 'create_rescaled' and 'rescale' as shown in :numref:`2019_xmn_fig_provenance_process_functions`. -To see the 'call' link, inspect now the outputs of the ``WorkFunctionNode`` which corresponds to the ``create_rescaled`` work function. -Write down its ```` (in general, it will be different from 441), then in the shell load the corresponding node and inspect the outputs: - -.. code:: ipython - - In [12]: node = load_node() - In [13]: node.get_outgoing().all() - -You should be able to identify the two children calculations as well as the final structure (you will see the process nodes linked via CALL links: these are process-to-process links representing the fact that ``create_rescaled`` called two calculation functions). -The graphical representation of what you have in the database should match :numref:`2019_xmn_fig_provenance_process_functions`. - -.. _2019_xmn_sync: - -Run a simple workflow ---------------------- -Let us now use the work and calculation functions that we have just created to build a simple workflow to calculate the equation of state of silicon. -We will consider five different values of the lattice parameter obtained rescaling the experimental minimum, ``a=5.431``, by a factor in ``[0.96, 0.98, 1.0, 1.02, 1.04]``. -We will write a simple script that runs a series of five calculations and at the end returns the volume and the total energy corresponding to each value of the lattice parameter. -For your convenience, besides the functions that you have written so far in the file ``create_rescale.py``, we provide you with some other utilities to get the correct pseudopotential and to generate a pw input file, in :download:`common_wf.py <../scripts/common_wf.py>`. - -We have already created the following script, which you can :download:`download <../scripts/simple_sync_workflow.py>`, but please go through the lines carefully and make sure you understand them. -We suggest that you first have a careful look at it before running it: - -.. include:: ../scripts/simple_sync_workflow.py - :code: python - -If you look into the previous piece of code, you will notice that the way we submit a QE calculation is slightly different from what you have seen in the first part of the tutorial. -The following: - -.. code:: python - - calculations[label] = run(PwCalculation, **inputs) - -runs in the current python session (without the daemon), waits for its completion and returns the output in the user-defined variable ``result``. -The latter is a dictionary whose values are the output nodes generated by the work function, with the link labels as keys. -For example once the function is finished, in order to access the total energy, we need to access the ``Dict`` node which is linked via the 'output_parameters' link (see again Fig. 1 of Day 1 Tutorial, to see inputs and outputs of a Quantum ESPRESSO calculation). -Once the right node is retrieved as ``result['output_parameters']``, we need to get the ``energy`` attribute. -The global operation is achieved by the command - -.. code:: python - - result['output_parameters'].dict.energy - -To collect these results from the various calculations into a single data node, we need one final calculation function to do so. -Since the ``run_eos_wf`` is a 'workflow'-like processes, which cannot create data, this operation **cannot** simply be done in the work function body itself. -To keep the provenance **we have to** do this through a calculation function. -This is done by the ``create_eos_dictionary`` calculation function, that receives as inputs, the output parameters nodes from the completed calculations: - -.. code:: python - - @calcfunction - def create_eos_dictionary(**kwargs): - """Create a single `Dict` node from the `Dict` output parameters of completed `PwCalculations`. - - The dictionary will contain a list of tuples, where each tuple contains the volume, total energy and its units - of the results of a calculation. - - :return: `Dict` node with the equation of state results - """ - eos = [(result.dict.volume, result.dict.energy, result.dict.energy_units) for label, result in kwargs.items()] - return Dict(dict={'eos': eos}) - -It constructs a new ``Dict`` node that contains a single value ``eos`` which is a list of tuples with the relevant data for each calculation. -If you were to inspect the returned data node, with for example ``verdi data dict show`` you would see something like: - -.. code:: bash - - { - "eos": [ - [ - 40.04786949775, - -308.193208771881, - "eV" - ], - [ - 37.6927343883263, - -307.990673492459, - "eV" - ], - [ - 35.4317918679613, - -307.666113525946, - "eV" - ], - [ - 45.048406674717, - -308.308206255253, - "eV" - ], - [ - 42.4991194939683, - -308.293522312459, - "eV" - ] - ] - } - -As you see, the function ``run_eos_wf`` has been decorated as a work function to keep track of the provenance. -To run the workflow it suffices to call the function ``run_eos_wf`` in a python script providing the required input parameters. -For simplicity, we have included few lines at the end of the script that invoke the function with a static choice of parameters: - -.. code:: python - - def run_eos(code=load_code('qe-6.3-pw@localhost'), pseudo_family='SSSP', element='Si'): - return run_eos_wf(code, Str(pseudo_family), Str(element)) - - if __name__ == '__main__': - run_eos() - -To get a reference to the node that represents the function execution, we can ask the ``run`` function to return the node, in addition to the results. -Instead of calling the work function to run it, we can use the method ``run_get_node``: - -.. code:: python - - results, node = run_eos_wf.run_get_node(code, Str(pseudo_family), Str(element)) - print('run_eos_wf<{}> completed'.format(node.pk)) - -Run the workflow: - -.. code:: bash - - verdi run simple_sync_workflow.py - -The command above locks the shell until the full workflow has completed (we will see in a moment how to avoid this). -While the function is running, you can use (in a different shell) the command ``verdi process list`` to show ongoing and finished processes. -You can 'grep' for the ```` you are interested in. -Additionally, you can use the command ``verdi process status `` to show the tree of the calculatino functions called by the root work function with a given ````. - -Wait for the work function to finish, then call the function ``plot_eos()`` that we provided in the file ``common_wf.py`` to plot the equation of state and fit it with a Birch–Murnaghan equation. - -.. _2019_xmn_wf_multiple_calcs: - -Run multiple calculations -------------------------- - -You should have noticed that the calculations for different lattice parameters are executed serially, although they might perfectly be executed in parallel because their inputs and outputs are not connected in any way. -In the language of workflows, these calculations are executed in a synchronous (or blocking) way, whereas we would like to have them running *asynchronously* (i.e., in a non-blocking way, to run them in parallel). -One way to achieve this, is to submit the calculation to the daemon using the ``submit`` function. -Make a copy of the script ``simple_sync_workflow.py`` that we worked on in the previous section and name it ``simple_submit_workflow.py``. -To make the new script work asynchronously, simply change the following subset of lines: - -.. code:: python - - from aiida.engine import run - - [...] - - for label, factor in zip(labels, scale_factors): - - [...] - - calculations[label] = run(PwCalculation, **inputs) - - [...] - - inputs = {label: result['output_parameters'] for label, result in calculations.items()} - eos = create_eos_dictionary(**inputs) - - [...] - -replacing them with - -.. code:: python - - from aiida.engine import submit - from time import sleep - - [...] - - for label, factor in zip(labels, scale_factors): - [...] - - # Replace `run` with `submit` - calculations[label] = submit(PwCalculation, **inputs) - - [...] - - # Wait for the calculations to finish - for calculation in calculations.values(): - while not calculation.is_finished: - sleep(1) - - inputs = {label: node.get_outgoing().get_node_by_label('output_parameters') for label, node in calculations.items()} - eos = create_eos_dictionary(**inputs) - -The main differences are: - -- ``run`` is replaced by ``submit`` -- The return value of ``submit`` is not a dictionary describing the outputs of the calculation, but it is the node that represents the execution of that calculation. -- Each calculation starts in the background and calculation nodes are added to the ``calculations`` dictionary. -- At the end of the loop, when all calculations have been launched with ``submit``, another loop is used to wait for all calculations to finish before gathering the results as the final step. - -In the next section we will show you another way to achieve this, which has the added bonus that it introduces checkpoints in the work function, from which the process can be resumed should it be interrupted. -After applying the modifications, run the script. -You will see that all calculations start at the same time, without waiting for the previous ones to finish. - -If in the meantime you run ``verdi process status ``, all five calculations are already shown as output. -Also, if you run ``verdi process list``, you will see how the calculations are submitted to the scheduler. - -.. _2019_xmn_workchainsimple: - -Workchains, or how not to get lost if your computer shuts down or crashes -------------------------------------------------------------------------- - -The simple workflows that we have used so far have been launched by a python script that needs to be running for the whole time of the execution, namely the time in which the calculations are submitted, and the actual time needed by Quantum ESPRESSO to perform the calculation and the time taken to retrieve the results. -If you had killed the main python process during this time, the workflow would not have terminated correctly. -Perhaps you killed the calculation and you experienced the unpleasant consequences: intermediate calculation results are potentially lost and it is extremely difficult to restart a workflow from the exact place where it stopped. - -In order to overcome this limitation, in AiiDA we have implemented a way to insert checkpoints, where the main code defining a workflow can be stopped (you can even shut down the machine on which AiiDA is running!). -We call these work functions with checkpoints 'work chains' because, as you will see, they basically amount to splitting a work function in a chain of steps. -Each step is then ran by the daemon, in a way similar to the remote calculations. - -Here below you can find the basic rules that allow you to convert your workfunction-based script to a workchain-based one and a snippet example focusing on the code used to perform the calculation of an equation of state. - -.. include:: ../scripts/equation_of_state.py - :code: python - -.. warning:: - - WorkChains need to be defined in a **separate file** from the script used to run them. - E.g. save your WorkChain in ``workchains.py`` and use ``from workchains import MyWorkChain`` to import it in your script. - - Furthermore, the directory containing the WorkChain definition needs to be in the ``PYTHONPATH`` in order for the AiiDA daemon to find it. - If your ``workchains.py`` sits in ``/home/max/workchains``, add a line ``export PYTHONPATH=$PYTHONPATH:/home/max`` to the ``/home/max/.virtualenvs/aiida/bin/activate`` script, followed by ``verdi daemon restart``. - -- Instead of using decorated functions you need to define a class, inheriting from a prototype class called ``WorkChain`` that is provided by AiiDA in the ``aiida.engine`` module. - -- Within your class you need to implement a ``define`` classmethod that always takes ``cls`` and ``spec`` as inputs. - Here you specify the main information on the workchain, in particular: - - - The *inputs* that the workchain expects. - This is obtained by means of the ``spec.input()`` method, which provides as the key feature the automatic validation of the input types via the ``valid_type`` argument. - The same holds true for outputs, as you can use the ``spec.output()`` method to state what output types are expected to be returned by the workchain. - - - The ``outline`` consisting in a list of 'steps' that you want to run, put in the right sequence. - This is obtained by means of the method ``spec.outline()`` which takes as input the steps. - *Note*: in this example we just split the main execution in two sequential steps, that is, first ``run_eos`` then ``results``. - However, more complex logic is allowed, as will be explained in :ref:`another section<2019_xmn_convpressure>`. - -- You need to split your main code into methods, with the names you specified before into the outline (``run_eos`` and ``results`` in this example). - Where exactly should you split the code? - Well, the splitting points should be put where you would normally block the execution of the script for collecting results in a standard work function, namely whenever you call the method ``.result()``. - Each method should accept only one parameter, ``self``, e.g. ``def step_name(self)``. - -- You will notice that the methods reference the attribute ``ctx`` through ``self.ctx``, which is called the *context* and is inherited from the base class ``WorkChain``. - A python function or process function normally just stores variables in the local scope of the function. - For instance, in the example of :ref:`this subsection<2019_xmn_sync>`, you stored the completed calculations in the ``calculations`` dictionary, that was a local variable. - - In work chains, instead, to preserve variables between different steps, you need to store them in a special dictionary called *context*. - As explained above, the context variable ``ctx`` is inherited from the base class ``WorkChain``, and at each step method you just need to update its content. - AiiDA will take care of saving the context somewhere between workflow steps (on disk, in the database, depending on how AiiDA was configured). - For your convenience, you can also access the value of a context variable as ``self.ctx.varname`` instead of ``self.ctx['varname']``. - -- Any submission within the workflow should not call the normal ``run`` or ``submit`` functions, but ``self.submit`` to which you have to pass the process class, and a dictionary of inputs. - -- The submission in ``run_eos`` returns a future and not the actual calculation, because at that point in time we have only just launched the calculation to the daemon and it is not yet completed. - Therefore it literally is a 'future' result. - Yet we still need to add these futures to the context, so that in the next step of the workchain, when the calculations are in fact completed, we can access them and continue the work. - To do this, we can use the ``ToContext`` class. - This class takes a dictionary, where the values are the futures and the keys will be the names under which the corresponding calculations will be made available in the context when they are done. - See how the ``ToContext`` object is created and returned in ``run_eos``. - By doing this, the workchain will implicitly wait for the results of all the futures you have specified, and then call the next step *only when all futures have completed*. - -- *Return values*: While in normal process functions you attach output nodes to the node by invoking the *return* statement, in a workchain you need to call ``self.out(link_name, node)`` for each node you want to return. - Of course, if you have already prepared a dictionary of outputs, you can just use the following syntax: - - .. code:: python - - self.out_many(retdict) # Keys are link names, value the nodes - - The advantage of this different syntax is that you can start emitting output nodes already in the middle of the execution, and not necessarily at the very end as it happens for normal functions (*return* is always the last instruction executed in a function or method). - Also, note that once you have called ``self.out(link_name, node)`` on a given ``link_name``, you can no longer call ``self.out()`` on the same ``link_name``: this will raise an exception. - -Finally, the workflow has to be run. -For this you have to use the function ``run`` passing as arguments the ``EquationOfState`` class and the inputs as key-value arguments. -For example, you can execute: - -.. code:: python - - from aiida.engine import run - run(EquationOfState, element=Str('Si'), code=load_code('qe-6.3-pw@localhost'), pseudo_family=Str('SSSP')) - -While the workflow is running, you can check (in a different terminal) what is happening to the calculations using ``verdi process list``. -You will see that after a few seconds the calculations are all submitted to the scheduler and can potentially run at the same time. - -.. warning:: - - The calculations launched by the work chain will now be submitted to the daemon, instead of run in the same interpreter - However, by using ``run`` on the work chain itself, it will still not be able to restart. - To make the work chain save its checkpoints, you should use the ``submit`` launcher instead. - -As an additional exercise (optional), instead of running the main workflow ``EquationOfState``, try to submit it. -Note that the file where the work chains is defined will need to be globally importable (so the daemon knows how to load it) and you need to launch it (with ``submit``) from a different python file. -The easiest way to achieve this is typically to embed the workflow inside a python package. - -.. note:: - - As good practice, you should try to keep the steps as short as possible in term of execution time. - The reason is that the daemon can be stopped and restarted only between execution steps and not if a step is in the middle of a long execution. - -Finally, as an optional exercise if you have time, you can jump to :ref:`this appendix<2019_xmn_convpressure>`, which shows how to introduce more complex logic into your work chains (if conditionals, while loops etc.). -The exercise will show how to realize a convergence loop to obtain the minimum-volume structure in a EOS using the Newton's algorithm. - -.. note:: - - You might see warnings that say ``Exception trying to save checkpoint, this means you will not be able to restart in case of a crash until the next successful checkpoint``. - These are generated by the ``PwCalculation`` which is unable to save a checkpoint because it is not in a so called 'importable path'. - Simply put, this means that if AiiDA were to try and reload the class it wouldn't know which file to find it in. - To get around this you could simply put the workchain in a different file that is in the 'PYTHONPATH' and then launch it by importing it in your launch file. - In this way AiiDA knows where to find it next time it loads the checkpoint. - - -.. rubric:: Footnotes - -.. [#f1] In simple words, a decorator is a function that modifies the behavior of another function. In python, a function can be decorated by adding a line of the form ``@decorating_function_name`` on the line just before the ``def`` line of the decorated function. If you want to know more, there are many online resources explaining python decorators. diff --git a/docs/pages/2019_Xiamen/sponsors/epfl.png b/docs/pages/2019_Xiamen/sponsors/epfl.png deleted file mode 100644 index f888916d..00000000 Binary files a/docs/pages/2019_Xiamen/sponsors/epfl.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sponsors/intersect.png b/docs/pages/2019_Xiamen/sponsors/intersect.png deleted file mode 100644 index 17074094..00000000 Binary files a/docs/pages/2019_Xiamen/sponsors/intersect.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sponsors/marvel.png b/docs/pages/2019_Xiamen/sponsors/marvel.png deleted file mode 100644 index 92686e61..00000000 Binary files a/docs/pages/2019_Xiamen/sponsors/marvel.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sponsors/max.png b/docs/pages/2019_Xiamen/sponsors/max.png deleted file mode 100644 index 2ebda134..00000000 Binary files a/docs/pages/2019_Xiamen/sponsors/max.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sponsors/psi-k-crystal_low.png b/docs/pages/2019_Xiamen/sponsors/psi-k-crystal_low.png deleted file mode 100644 index d2a3d637..00000000 Binary files a/docs/pages/2019_Xiamen/sponsors/psi-k-crystal_low.png and /dev/null differ diff --git a/docs/pages/2019_Xiamen/sponsors/swissuniversities.png b/docs/pages/2019_Xiamen/sponsors/swissuniversities.png deleted file mode 100644 index dfea0319..00000000 Binary files a/docs/pages/2019_Xiamen/sponsors/swissuniversities.png and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/deliverable_capacity.py b/docs/pages/2019_molsim_school_Amsterdam/assets/deliverable_capacity.py deleted file mode 100644 index 27f07200..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/assets/deliverable_capacity.py +++ /dev/null @@ -1,186 +0,0 @@ -"""Deliverable capacity WorkChain.""" -from __future__ import absolute_import -from aiida.orm import CalculationFactory, DataFactory, Code -from aiida.orm.data.base import Float -from aiida.work import WorkChain, workfunction, submit -from aiida.work.workchain import ToContext, Outputs - -CifData = DataFactory('cif') -ParameterData = DataFactory('parameter') -NetworkParameters = DataFactory('zeopp.parameters') -SingleFile = DataFactory('singlefile') -RaspaCalculation = CalculationFactory('raspa') -ZeoppCalculation = CalculationFactory('zeopp.network') - - -@workfunction -def update_raspa_parameters(parameters, pressure): - """Store input parameters of Raspa for given pressure. - - Note: In order to keep the provenance of both Raspa calculations, - changing the pressure force us to create a new ParameterData node. - "workfunctions" will take care of linking the user-provided ParameterData - node to the new one containing the pressure. - """ - param_dict = parameters.get_dict() - param_dict['GeneralSettings']['ExternalPressure'] = pressure.value - return ParameterData(dict=param_dict) - - -class DcMethane(WorkChain): - """Compute methane deliverable capacity for a given structure.""" - - @classmethod - def define(cls, spec): - """Define workflow specification. - - This is the most important method of a Workchain, which defines the - inputs it takes, the logic of the execution and the outputs - that are generated in the process. - """ - super(DcMethane, cls).define(spec) - - # First we define the inputs, specifying the type we expect - - spec.input("structure", valid_type=CifData, required=True) - spec.input("zeopp_parameters", - valid_type=NetworkParameters, - required=True) - spec.input("atomic_radii", valid_type=SingleFile, required=True) - spec.input("raspa_parameters", valid_type=ParameterData, required=True) - spec.input("zeopp_code", valid_type=Code, required=True) - spec.input("raspa_code", valid_type=Code, required=True) - - # The outline describes the basic logic that defines - # which steps are executed in what order and based on - # what conditions. Each `cls.method` is implemented below - spec.outline( - cls.init, - cls.run_block_zeopp, - cls.run_loading_raspa_low_p, - cls.parse_loading_raspa, - cls.run_loading_raspa_high_p, - cls.parse_loading_raspa, - cls.extract_results, - ) - - # Here we define the output the Workchain will generate and - # return. Dynamic output allows a variety of AiiDA data nodes - # to be returned - spec.dynamic_output() - - def init(self): - """Initialize variables.""" - - self.ctx.loading_average = {} - self.ctx.loading_dev = {} - - self.ctx.options = { - "resources": { - "num_machines": 1, # run on 1 node - "tot_num_mpiprocs": 1, # use 1 process - "num_mpiprocs_per_machine": 1, - }, - "max_wallclock_seconds": 4 * 60 * 60, # 1h walltime - "max_memory_kb": 2000000, # 2GB memory - "queue_name": "molsim", # slurm partition to use - "withmpi": False, # we run in serial mode - } - - def run_block_zeopp(self): - """This function will perform a zeo++ calculation to block inaccessible pockets.""" - - # Create the input dictionary - inputs = { - 'code': self.inputs.zeopp_code, - 'structure': self.inputs.structure, - 'parameters': self.inputs.zeopp_parameters, - 'atomic_radii': self.inputs.atomic_radii, - '_options': self.ctx.options, - } - - # Create the calculation process and launch it - process = ZeoppCalculation.process() - future = submit(process, **inputs) - self.report("pk: {} | Running Zeo++ to obtain blocked pockets".format( - future.pid)) - - return ToContext(zeopp=Outputs(future)) - - def run_loading_raspa_low_p(self): - """Run raspa loading calculation at low pressure.""" - self.ctx.current_pressure = 5.8e5 - self.ctx.current_pressure_label = "low" - return self._run_loading_raspa() - - def run_loading_raspa_high_p(self): - """Run raspa loading calculation at high pressure.""" - self.ctx.current_pressure = 65e5 - self.ctx.current_pressure_label = "high" - return self._run_loading_raspa() - - def _run_loading_raspa(self): - """Perform raspa calculation at given pressure. - - Most of the runtime will be spent in this function. - """ - # Create the input dictionary - inputs = { - 'code': - self.inputs.raspa_code, - 'structure': - self.inputs.structure, - 'parameters': - update_raspa_parameters(self.inputs.raspa_parameters, - Float(self.ctx.current_pressure)), - 'block_component_0': - self.ctx.zeopp['block'], - '_options': - self.ctx.options, - } - - # Create the calculation process and launch it - process = RaspaCalculation.process() - future = submit(process, **inputs) - self.report("pk: {} | Running raspa for the pressure {} [bar]".format( - future.pid, self.ctx.current_pressure / 1e5)) - - return ToContext(raspa=Outputs(future)) - - def parse_loading_raspa(self): - """Extract pressure and loading average of last completed raspa calculation.""" - loading_average = self.ctx.raspa[ - "component_0"].dict.loading_absolute_average - loading_dev = self.ctx.raspa["component_0"].dict.loading_absolute_dev - self.ctx.loading_average[ - self.ctx.current_pressure_label] = loading_average - self.ctx.loading_dev[self.ctx.current_pressure_label] = loading_dev - - def extract_results(self): - """Extract results of the workflow. - - Attaches the results of the raspa calculation and the initial structure to the outputs. - """ - from math import sqrt - cf = self.ctx.raspa[ - "component_0"].dict.conversion_factor_molec_uc_to_cm3stp_cm3 - dc = self.ctx.loading_average["high"] - self.ctx.loading_average["low"] - dc_dev = sqrt(self.ctx.loading_dev["high"]**2 + - self.ctx.loading_dev["low"]**2) - - result = { - "deliverable_capacity": dc * cf, - "deliverable_capacity_units": "cm^3_STP/cm^3", - "deliverable_capacity_dev": dc_dev * cf, - "loading_absolute_average_low_p": - self.ctx.loading_average["low"] * cf, - "loading_absolute_dev_low_p": self.ctx.loading_dev["low"] * cf, - "loading_units": "cm^3_STP/cm^3", - "loading_absolute_average_high_p": - self.ctx.loading_average["high"] * cf, - "loading_absolute_dev_high_p": self.ctx.loading_dev["high"] * cf, - } - self.out("result", ParameterData(dict=result)) - - self.report("Workchain <{}> completed successfully".format( - self.calc.pk)) diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/gcmc.png b/docs/pages/2019_molsim_school_Amsterdam/assets/gcmc.png deleted file mode 100644 index 9b04daa7..00000000 Binary files a/docs/pages/2019_molsim_school_Amsterdam/assets/gcmc.png and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/multiply_unitcell.py b/docs/pages/2019_molsim_school_Amsterdam/assets/multiply_unitcell.py deleted file mode 100644 index 66cc067b..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/assets/multiply_unitcell.py +++ /dev/null @@ -1,36 +0,0 @@ -def multiply_unit_cell(cif, cutoff): - """Returns the multiplication factors (tuple of 3 int) for the cell vectors - that are needed to respect: min(perpendicular_width) > threshold - """ - from math import cos, sin, sqrt, pi - import numpy as np - deg2rad = pi / 180. - - struct = six.itervalues(cif.values.dictionary) - - a = float(struct['_cell_length_a']) - b = float(struct['_cell_length_b']) - c = float(struct['_cell_length_c']) - - alpha = float(struct['_cell_angle_alpha']) * deg2rad - beta = float(struct['_cell_angle_beta']) * deg2rad - gamma = float(struct['_cell_angle_gamma']) * deg2rad - - # first step is computing cell parameters according to https://en.wikipedia.org/wiki/Fractional_coordinates - # Note: this is the algorithm implemented in Raspa (framework.c/UnitCellBox). - # There also is a simpler one but it is less robust. - v = sqrt(1 - cos(alpha)**2 - cos(beta)**2 - cos(gamma)**2 + - 2 * cos(alpha) * cos(beta) * cos(gamma)) - cell = np.zeros((3, 3)) - cell[0, :] = [a, 0, 0] - cell[1, :] = [b * cos(gamma), b * sin(gamma), 0] - cell[2, :] = [ - c * cos(beta), - c * (cos(alpha) - cos(beta) * cos(gamma)) / (sin(gamma)), - c * v / sin(gamma) - ] - cell = np.array(cell) - - # diagonalizing the cell matrix: note that the diagonal elements are the perpendicolar widths because ay=az=bz=0 - diag = np.diag(cell) - return tuple(int(i) for i in np.ceil(cutoff / diag * 2.)) diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/perp_width.png b/docs/pages/2019_molsim_school_Amsterdam/assets/perp_width.png deleted file mode 100644 index f5af35f4..00000000 Binary files a/docs/pages/2019_molsim_school_Amsterdam/assets/perp_width.png and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/pore_volume.png b/docs/pages/2019_molsim_school_Amsterdam/assets/pore_volume.png deleted file mode 100644 index 8afaf8ed..00000000 Binary files a/docs/pages/2019_molsim_school_Amsterdam/assets/pore_volume.png and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/raspa_loading.py b/docs/pages/2019_molsim_school_Amsterdam/assets/raspa_loading.py deleted file mode 100644 index 4aafafed..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/assets/raspa_loading.py +++ /dev/null @@ -1,62 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import print_function -from __future__ import absolute_import - -from aiida.common.example_helpers import test_and_get_code -from aiida.orm import DataFactory, CalculationFactory -from aiida.work.run import submit -from aiida.orm import load_node - -# Data classes -CifData = DataFactory('cif') -ParameterData = DataFactory('parameter') -RaspaCalculation = CalculationFactory('raspa') - -# Raspa input parameters -parameters = ParameterData(dict={ - "GeneralSettings": { - "SimulationType" : "MonteCarlo", - "NumberOfCycles" : 1000, - "NumberOfInitializationCycles" : 1000, - "PrintEvery" : 100, - - "CutOff" : 12.0, # (Angstroms) - - "Forcefield" : "UFF-TraPPE", - "ChargeMethod" : "None", - "UnitCells" : " ", - - "ExternalTemperature" : , - "ExternalPressure" : , - }, - "Component": [{ - "MoleculeName" : "methane", - "MoleculeDefinition" : "TraPPE", - "MolFraction" : 1.0, - "TranslationProbability" : 1.0, - "RotationProbability" : 1.0, - "ReinsertionProbability" : 1.0, - "SwapProbability" : 1.0, - "CreateNumberOfMolecules" : 0, - }], -}) - -# Calculation resources -options = { - "resources": { - "num_machines": 1, # run on 1 node - "tot_num_mpiprocs": 1, # use 1 process - "num_mpiprocs_per_machine": 1, - }, - "max_wallclock_seconds": 1 * 60 * 60, # 1h walltime - "max_memory_kb": 2000000, # 2GB memory - "queue_name": "molsim", # slurm partition to use - "withmpi": False, # we run in serial mode -} - -submit(RaspaCalculation.process(), - code=test_and_get_code("raspa@bazis", expected_code_type='raspa'), - structure=load_node(""), - parameters=parameters, - _options=options -) diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/spheres.png b/docs/pages/2019_molsim_school_Amsterdam/assets/spheres.png deleted file mode 100644 index 8522010c..00000000 Binary files a/docs/pages/2019_molsim_school_Amsterdam/assets/spheres.png and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/zeopp_sample_graph.pdf b/docs/pages/2019_molsim_school_Amsterdam/assets/zeopp_sample_graph.pdf deleted file mode 100644 index a4f7859c..00000000 Binary files a/docs/pages/2019_molsim_school_Amsterdam/assets/zeopp_sample_graph.pdf and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/assets/zeopp_sample_graph.png b/docs/pages/2019_molsim_school_Amsterdam/assets/zeopp_sample_graph.png deleted file mode 100644 index 284ec79a..00000000 Binary files a/docs/pages/2019_molsim_school_Amsterdam/assets/zeopp_sample_graph.png and /dev/null differ diff --git a/docs/pages/2019_molsim_school_Amsterdam/index.rst b/docs/pages/2019_molsim_school_Amsterdam/index.rst deleted file mode 100644 index 9c90032d..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/index.rst +++ /dev/null @@ -1,81 +0,0 @@ -+-----------------+-------------------------------------------+ -| Related resources | -+=================+===========================================+ -| Virtual Machine | `Quantum Mobile 18.10.0RC1`_ | -+-----------------+-------------------------------------------+ -| python packages | `aiida-core 0.12.2`_, `aiidalab 19.01.2`_ | -+-----------------+-------------------------------------------+ -| codes | `zeo++ 0.3`_, `raspa 2.0.36`_ | -+-----------------+-------------------------------------------+ - -.. _Quantum Mobile 18.10.0RC1: https://github.com/marvel-nccr/quantum-mobile/releases/tag/18.10.0RC1 -.. _aiida-core 0.12.2: https://pypi.org/project/aiida-core/0.12.2 -.. _aiidalab 19.01.2: https://pypi.org/project/aiidalab/19.1.2 -.. _zeo++ 0.3: http://www.zeoplusplus.org/download.html -.. _raspa 2.0.36: https://github.com/iRASPA/RASPA2/releases/tag/v2.0.36 - -This tutorial is part of the `Understanding molecular -simulation `__ school held at the -University of Amsterdam from January 7-18 2019. - - -Metal-organic frameworks for methane storage applications -========================================================= - -In this tutorial, we will screen metal-organic frameworks (MOFs) for -their possible application as methane adsorbents, allowing to store -natural gas at increased density and lower storage pressure. We will use -the `AiiDA framework `__ in order to automate the -screening workflow and to record the full provenance of the calculations -for reproducibility. - -The tutorial is meant to be run inside the `Quantum -Mobile `__ virtual -machine, using a compute resource with -`zeo++ `__ and -`RASPA2 `__ installed. - -.. note:: - - This tutorial requires a basic knowledge of - `python `__. If you are - not familiar with python, we suggest you partner with someone who is. - -Analyzing the database ----------------------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - Getting set up <./tutorial/setup> - Browsing the provenance graph <./tutorial/provenance-graph> - The verdi command line <./tutorial/verdi-commands> - The AiiDA python interface <./tutorial/python-interface> - Querying the AiiDA database <./tutorial/queries> - Selecting candidate materials <./tutorial/candidate-selection> - -Computing properties of candidate materials -------------------------------------------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - Setting up remote computers and codes <./screening/calculations> - Compute methane loading <./screening/methane-loading> - Screening <./screening/screening> - Upload your results <./screening/export> - -Theoretical background ----------------------- - -.. toctree:: - :maxdepth: 1 - :numbered: - - Origin of the MOF database <./theoretical/502-mofs> - Geometric properties <./theoretical/geometric-properties> - Multiply the unit cell <./theoretical/multiply-uc> - Settings for Raspa <./theoretical/settings-raspa> - Extra challenge: MOFs for CO2 capture <./theoretical/charged-adsorbates> diff --git a/docs/pages/2019_molsim_school_Amsterdam/screening/calculations.md b/docs/pages/2019_molsim_school_Amsterdam/screening/calculations.md deleted file mode 100644 index be04dad7..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/screening/calculations.md +++ /dev/null @@ -1,274 +0,0 @@ -# Setting up remote computers and codes - -AiiDA manages calculations from start to finish, submitting input files to -remote (super)computers, monitoring the job queue and getting results back -to your computer. - -In order for AiiDA to take care of this for you, you'll need to tell AiiDA -once how to connect to the remote computer and how to run the simulation codes. - -## Computer setup and configuration - -For the tutorial, we have access to a compute cluster from the University of Amsterdam. -Let's set up passwordless access using the secure shell (SSH) protocol. - -First, generate a new public/private SSH key pair. - -> **Note** -> When running this outside Quantum Mobile, be careful not to overwrite your -> own key pair. In this case, just change the proposed file name. - -```console -$ ssh-keygen -t rsa -Generating public/private rsa key pair. -Enter file in which to save the key (/home/max/.ssh/id_rsa): -Enter passphrase (empty for no passphrase): -Enter same passphrase again: -Your identification has been savedin /home/max/.ssh/id_rsa. -Your public key has been saved in /home/max/.ssh/id_rsa.pub. -The key fingerprint is: ... -The key's randomart image is: ... -``` - -Then, copy the public key to the compute cluster: - -```console -$ ssh-copy-id molsim@bazis-h1.science.uva.nl - -/usr/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed -/usr/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys -********************************************************************** - Welcome to the Amsterdam/UvA FNWI research cluster. - Faculty of Science - (http://www.science.uva.nl) - University of Amsterdam, The Netherlands - - - Only AUTHORIZED USERS may access this site. - -All actions are logged. If you don't like this policy, disconnect now. - - Note: The bazis-h1 defq has access to 32 cores per node - -********************************************************************** - Faculty Expert-IT Support Group (mailto:feiog-tech-science@uva.nl) -********************************************************************** -molsim@bazis-h1.science.uva.nl's password: - -Number of key(s) added: 1 -``` - -Now try logging in to the cluster with `ssh molsim@bazis-h1.science.uva.nl`, -which should work without password. -Once it does, let's set up the `bazis` computer in AiiDA as follows: - -```console -$ verdi computer setup -At any prompt, type ? to get some help. -————————————— -=> Computer name: bazis -Creating new computer with name 'bazis' -=> Fully-qualified hostname: bazis-h1.science.uva.nl -=> Description: bazis computer for the molsim course -=> Enabled: True -=> Transport type: ssh -=> Scheduler type: slurm -=> shebang line at the beginning of the submission script: #!/bin/bash -=> AiiDA work directory: /home/{username}/aiida_run/ -=> mpirun command: srun -n {tot_num_mpiprocs} -=> Default number of CPUs per machine: 1 -=> Text to prepend to each command execution: -# This is a multiline input, press CTRL+D on a -# empty line when you finish -# —————————————— -# End of old input. You can keep adding -# lines, or press CTRL+D to store this value -# —————————————— -=> Text to append to each command execution: -# This is a multiline input, press CTRL+D on a -# empty line when you finish -# —————————————— -# End of old input. You can keep adding -# lines, or press CTRL+D to store this value -# —————————————— -Computer 'bazis' successfully stored in DB. -``` - -> **Note** -> Please be careful to enter the data as shown, in particular: -> -> * AiiDA work directory: /**home**/{username}/aiida_run/ -> * mpirun command: **srun -n** {tot_num_mpiprocs} - -At this point, the computer node has been created in the database - -```console -$ verdi computer list -a -``` - -but access hasn't been configured yet. - -```console -$ verdi computer configure bazis -Configuring computer 'bazis' for the AiiDA user 'aiida@localhost' -Computer bazis has transport of type ssh - -Note: to leave a field unconfigured, leave it empty and press [Enter] - -=> username = molsim -=> port = 22 -=> look for keys = -=> key filename = -=> timeout = 60 -=> allow agent = -=> proxy command = -=> compress = True -=> gssauth = False -=> gsskex = False -=> gssdelegcreds = False -=> gsshost = bazis-h1.science.uva.nl -=> load system hostkeys = True -=> key policy = AutoAddPolicy -Configuration stored for your user on computer 'bazis'. -``` - -Finally, let AiiDA test the computer setup we just created: - -```console -$ verdi computer test bazis -Testing computer 'bazis' for user leopold.talirz@epfl.ch... -> Testing connection... -> Getting job list... - `-> OK, 1 jobs found in the queue. -> Creating a temporary file in the work directory... - `-> Getting the remote user name... - [remote username: molsim20] - [Checking/creating work directory: /home/molsim20/aiida_run/] - `-> Creating the file tmpjzXXrC... - `-> Checking if the file has been created... - [OK] - `-> Retrieving the file and checking its content... - [Retrieved] - [Content OK] - `-> Removing the file... - [Deleted successfully] -Test completed (all 3 tests succeeded) -``` - -## Code setup and configuration - -Next, we need to let AiiDA know about the computer codes available on `bazis`. - -We've already installed [RASPA2](https://github.com/numat/RASPA2) there, -so you can set up the code as follows: - -```console -$ verdi code setup -At any prompt, type ? to get some help. -————————————— -=> Label: raspa -=> Description: Raspa code for the molsim course -=> Local: False -=> Default input plugin: raspa -=> Remote computer name: bazis -=> Remote absolute path: /home/molsim20/raspa/bin/simulate -=> Text to prepend to each command execution -FOR INSTANCE, MODULES TO BE LOADED FOR THIS CODE: -# This is a multiline input, press CTRL+D on a -# empty line when you finish -# —————————————— -# End of old input. You can keep adding -# lines, or press CTRL+D to store this value -# —————————————— -export RASPA_DIR=/home/molsim20/raspa/ -=> Text to append to each command execution: -# This is a multiline input, press CTRL+D on a -# empty line when you finish -# —————————————— -# End of old input. You can keep adding -# lines, or press CTRL+D to store this value -# —————————————— -Code 'raspa' successfully stored in DB. -``` - -> **Note** -> Remember to include: -> -> * => Remote absolute path: /home/molsim20/raspa/bin/simulate -> * export RASPA_DIR=/home/molsim20/raspa/ -> -> as "Text to prepend to each command execution" -> -> otherwise RASPA won't run and you might get a parsing error. -> -> If you get an error about the input plugin not being found, -> try running `reeentry scan` to refresh AiiDA's plugin entry point cache -> and repeat the code setup. - -The list of codes should now include your new code `raspa@bazis` - -```console -$ verdi code list -``` - -## The AiiDA daemon - -The AiiDA daemon is a program running all the time in the background, checking -for new calculations that need to be submitted. The daemon also takes care of -all the necessary operations before the calculation submission, and after the -calculation has completed on the cluster. - -Let's check whether the AiiDA daemon is already running. - -```console -$ verdi daemon status -# Most recent daemon timestamp:0h:00m:26s ago -## Found 1 process running: - * aiida-daemon[aiida-daemon] RUNNING pid 15044, uptime 3 days, 15:38:41 -``` - -If the daemon is not running, please start it - -```console -$ verdi daemon start -``` - -> **Note** -> AiiDA supports multiple profiles but for reasons of consistency -> only one profile can communicate with the daemon at a time. -> When starting the daemon, you may therefore see an error message like -> -> ```console -> You are not the daemon user! I will not start the daemon. -> (The daemon user is 'aiida@localhost', you are 'some.body@xyz.com') -> -> ** FOR ADVANCED USERS ONLY: ** -> To change the current default user, use 'verdi install --only-config' -> To change the daemon user, use 'verdi daemon configureuser' -> -> ``` -> -> Just follow the instructions by running -> -> ```console -> $ verdi daemon configureuser -> ``` -> -> and provide the email address of your profile in order to allow your user to run the daemon -> -> ```console -> > Current default user: some.body@xyz.com -> > Currently configured user who can run the daemon: aiida@localhost -> (therefore, you cannot run the daemon, at the moment) -> **************************************************************************** -> * WARNING! Change this setting only if you are sure of what you are doing. * -> * Moreover, make sure that the daemon is stopped. * -> **************************************************************************** -> Are you really sure that you want to change the daemon user? [y/N] y -> -> Enter below the email of the new user who can run the daemon. -> New daemon user: some.body@xyz.com -> The new user that can run the daemon is now Some Body. -> ``` -> -> Now start the daemon! diff --git a/docs/pages/2019_molsim_school_Amsterdam/screening/export.md b/docs/pages/2019_molsim_school_Amsterdam/screening/export.md deleted file mode 100644 index 89eae82a..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/screening/export.md +++ /dev/null @@ -1,28 +0,0 @@ -# Upload your results - -Once your calculations are finished, it's time to export and upload -them to the tutorial server. - -```console -$ verdi group list -t autogroup.run # note the PK of the latest group -$ verdi export create -G database.aiida -``` - -Finally, it's time to upload your results to the [tutorial server](http://34.244.178.26) -and compare your result to those of the others. - -* What is the highest deliverable capacity you got? -* Did you pick the same material as some other participants? -* Since the upload includes the provenance of your calculations, - you'll be able to check which parameters the others used - by clicking on the points in the scatter plot. - -**That's it** -- you've completed the AiiDA molsim tutorial, well done! -Keep posted for AiiDA news on the [AiiDA mailing list](http://groups.google.com/group/aiidausers/subscribe), in particular the upcoming AiiDA 1.0 release with many improvements and new features. - -If you're interested in continuing along these lines, you may want to (optional): - -* screen a few more materials to get a higher deliverable capacity - (**please do not run more than 5 at a time** in order not to block the queue for the other participants). -* learn about [how to change force fields in RASPA](../theoretical/settings-raspa) and play with the parameters to see how they impact the deliverable capacity -* have a look at the [extra challenge on CO2 capture](../theoretical/charged-adsorbates.md) (advanced, with less detailed instructions). diff --git a/docs/pages/2019_molsim_school_Amsterdam/screening/methane-loading.rst b/docs/pages/2019_molsim_school_Amsterdam/screening/methane-loading.rst deleted file mode 100644 index d69f6469..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/screening/methane-loading.rst +++ /dev/null @@ -1,187 +0,0 @@ -Compute methane loading for one MOF -=================================== - -With the codes set up and the daemon running, we are ready to do our -first calculation using AiiDA -- the methane loading of a MOF at 65 bar. - -We will use the `RASPA <../theoretical/settings-raspa.md>`__ code to -perform a grand-canonical Monte Carlo (GCMC) calculation, trying to -insert methane molecules into the nanoporous framework at the given -pressure. - -In principle, we could continue to use the ``verdi shell`` or -``jupyter notebooks`` but in order to speed things up, we've already -prepared a template python script that you can -:download:`download here <../assets/raspa_loading.py>`. - -In the following, you will adapt it to your needs. - -RASPA input parameters ----------------------- - -The `aiida-raspa `__ plugin -uses ``ParameterData`` nodes to specify the input parameters of RASPA: - -.. code:: python - - parameters = ParameterData(dict={ - "GeneralSettings": { - "SimulationType" : "MonteCarlo", - "NumberOfCycles" : 1000, - "NumberOfInitializationCycles" : 1000, - "PrintEvery" : 100, - - "CutOff" : 12.0, # (Angstroms) - - "Forcefield" : "UFF-TraPPE", - "ChargeMethod" : "None", - "UnitCells" : " ", - - "ExternalTemperature" : , - "ExternalPressure" : , - }, - "Component": [{ - "MoleculeName" : "methane", - "MoleculeDefinition" : "TraPPE", - "MolFraction" : 1.0, - "TranslationProbability" : 1.0, - "RotationProbability" : 1.0, - "ReinsertionProbability" : 1.0, - "SwapProbability" : 1.0, - "CreateNumberOfMolecules" : 0, - }], - }) - -Exercise -~~~~~~~~ - -The ``ParameterData`` dictionary is missing a number of parameters for -which you'll need to figure out reasonable values. - -- ``UnitCells`` Our simulations are performed under periodic boundary - conditions. This means, we need to make our simulation cell large - enough that a molecule will never interact with two periodic copies - of any of its neighbors. Given the cutoff radius of 12 Angstroms, how - often do you need to replicate the unit cell of the material? - - *Hint:* The CIF files include information on the size of the unit - cell. - -- For ``ExternalTemperature``/``ExternalPressure`` Use ambient - temperature and standard methane desorption pressure for natural gas - vehicles. - -- Currently, the probabilies for translation, rotation, reinsertion and - swap Monte Carlo moves are all equal, which is suboptimal. How would - optimize them? - -Another input of the calculation is the atomic structure of the MOF we -want to use. Find the structure labelled "ABUWOJ" (hint: filter by the -``label``) and note down its PK or UUID. - -.. note:: - Once you know the PK or UUID of a node, you can always load it - using - - .. code:: python - - structure = load_node() # load using PK (specific to your database) - structure = load_node('') # load using UUID (same for everyone) - - -Creating the calculation ------------------------- - -Every calculation sent to a cluster is linked to a code, which describes -the executable file to be used. We need to load the ``raspa@bazis`` code -that we set up before: - -.. code:: python - - from aiida.common.example_helpers import test_and_get_code - raspa_code = test_and_get_code("raspa@bazis", expected_code_type='raspa') - -Now we'll specify a few pieces of information to pass on to the -`slurm `__ scheduler that manages -calculations on the cluster, such as how many compute nodes to use or -the maximum time allowed for the calculation: - -.. code:: python - - options = { - "resources": { - "num_machines": 1, # run on 1 node - "tot_num_mpiprocs": 1, # use 1 process - "num_mpiprocs_per_machine": 1, - }, - "max_wallclock_seconds": 1 * 60 * 60, # 1h walltime - "max_memory_kb": 2000000, # 2GB memory - "queue_name": "molsim", # slurm partition to use - "withmpi": False, # we run in serial mode - } - - | **Note** - | AiiDA supports different types of schedulers via plugins, - including slurm, pbspro and sge. - -Submitting the calculation --------------------------- - -In principle, you can now simply submit the calculation from the -``verdi shell`` using - -.. code:: python - - RaspaCalculation = CalculationFactory('raspa') - submit(RaspaCalculation.process(), - code=raspa_code, - structure=structure, - parameters=parameters, - _options=options, - ) - -When the submission script is ready, submit it to the AiiDA daemon: - -.. code:: console - - $ verdi run raspa_loading.py - -.. note:: - By default, the daemon polls for new calculations every 30 - seconds, i.e. you may need to wait up to 30 seconds before your - calculation starts running. - If your calculation is stuck in the ``NEW`` state, it may indicate - that your daemon is not running or AiiDA is unable to find the - calculation plugin. Try:: - - reentry scan # make sure the raspa plugin is available to aiida - verdi daemon restart - - -Use ``verdi calculation list -a`` to monitor the state of the -calculation. Once running, the calculation should finish within 5 -minutes. - -Analyzing your results ----------------------- - -Once the calculation shows up as ``FINISHED``, have a look at the -result. - -Raspa produces two types of output: Outputs related to the whole system -(e.g. total energy, temperature) and outputs related to the component -- -in our case, there is only one component: methane. - -Find the ``component_0`` output of the calculation using -``verdi calculation show `` and use the PK of ``component_0`` in -``verdi data parameter show `` to extract the average and standard -deviation of the absolute methane loading. - -Exercise -~~~~~~~~ - -What is the relative standard deviation of the loading? How could you -decrease it? - -Re-run the calculation with adapted settings in order to decrease the -relative standard deviation below 5% diff --git a/docs/pages/2019_molsim_school_Amsterdam/screening/screening.rst b/docs/pages/2019_molsim_school_Amsterdam/screening/screening.rst deleted file mode 100644 index eb742fbb..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/screening/screening.rst +++ /dev/null @@ -1,409 +0,0 @@ -Screening -========= - -In order to compute the deliverable capacity of a material, you need to -compute the methane loading both at the loading and at the discharge -pressure, and then do some simple math - in other words, a simple -*workflow*. AiiDA provides -`WorkChains `__ -to orchestrate the running of calculations. - -We've prepared a WorkChain to compute the deliverable methane capacity. - -Download it :download:`from here <../assets/deliverable_capacity.py>` -and place the file in some directory, then add the path to this -directory to ``PYTHONPATH`` and restart the daemon: - -.. code:: console - - $ export PYTHONPATH=/path/to/your/directory/:$PYTHONPATH - $ verdi daemon restart - -Now, we analyze step by step the WorkChain, and we will see later how to -run it. - -Step 0: Defining inputs, outputs and execution steps ----------------------------------------------------- - -AiiDA WorkChains are python *classes*. The ``define`` method specifies -the inputs, the steps of execution and the outputs of the WorkChain: - -.. code:: python - - class DcMethane(WorkChain): - """Compute methane deliverable capacity for a given structure.""" - - @classmethod - def define(cls, spec): - """Define workflow specification. - - This is the most important method of a Workchain, which defines the - inputs it takes, the logic of the execution and the outputs - that are generated in the process. - """ - super(DcMethane, cls).define(spec) - - # First we define the inputs, specifying the type we expect - - spec.input("structure", valid_type=CifData, required=True) - spec.input( - "zeopp_parameters", valid_type=NetworkParameters, required=True) - spec.input("raspa_parameters", valid_type=ParameterData, required=True) - spec.input("atomic_radii", valid_type=SingleFile, required=True) - spec.input("zeopp_code", valid_type=Code, required=True) - spec.input("raspa_code", valid_type=Code, required=True) - - # The outline describes the basic logic that defines - # which steps are executed in what order and based on - # what conditions. Each `cls.method` is implemented below - spec.outline( - cls.init, - cls.run_block_zeopp, - cls.run_loading_raspa_low_p, - cls.parse_loading_raspa, - cls.run_loading_raspa_high_p, - cls.parse_loading_raspa, - cls.extract_results, - ) - - # Here we define the output the Workchain will generate and - # return. Dynamic output allows a variety of AiiDA data nodes - # to be returned - spec.dynamic_output() - -The ``DcMethane`` uses ``spec.input()`` to specify the following inputs: -1. The structure in ``CifData`` format 1. Raspa parameters of type -``ParameterData`` 1. Zeo++ parameters of type ``NetworkParameters`` 1. A -file containing specification of atomic radii (of type ``SingleFile``) -1. Zeo++ and Raspa codes (of type ``Code``). - -It then uses ``spec.outline()`` to specify the steps of the workchain -(using functions defined below). All **7 steps** will be executed in -order. - -Step 1: Prepare input parameters and variables ----------------------------------------------- - -.. code:: python - - def init(self): - """Initialize variables.""" - - self.ctx.loading_average = {} - self.ctx.loading_dev = {} - - self.ctx.options = { - "resources": { - "num_machines": 1, - "tot_num_mpiprocs": 1, - "num_mpiprocs_per_machine": 1, - }, - "max_wallclock_seconds": 4 * 60 * 60, - "max_memory_kb": 2000000, - "queue_name": "molsim", - "withmpi": False, - } - - | **Note** - | The **context** (``self.ctx``) variable is a container that is - accessible by every function in the ``DcMethane`` workchain. In this - particular case we are creating two empty dictionaries to store the - loading average and its deviation at different pressures. - -Step 2: Compute the geometric parameters of the MOFs. ------------------------------------------------------ - -As described in `Setting for Raspa -<../theoretical/settings-raspa>`__: - -- ``BlockPockets`` and ``BlockPocketsFileName`` will be filled by - AiiDA: if Zeo++ finds some non accessible pore volume, it can - generate a .block file with the positions and the radii of blocking - spheres. These spheres are inserted in the framework to prevent Raspa - from inserting molecules in the non accessible pore. - -Here we will compute blocked pockets of a particular material employing -the Zeo++ code. - -.. code:: python - - def run_block_zeopp(self): - """This function will perform a zeo++ calculation to obtain the blocked pockets.""" - - # Create the input dictionary - inputs = { - 'code': self.inputs.zeopp_code, - 'structure': self.inputs.structure, - 'parameters': self.inputs.zeopp_parameters, - 'atomic_radii': self.inputs.atomic_radii, - '_options': self.ctx.options, - } - - # Create the calculation process and launch it - process = ZeoppCalculation.process() - future = submit(process, **inputs) - self.report("pk: {} | Running Zeo++ to obtain blocked pockets".format( - future.pid)) - - return ToContext(zeopp=Outputs(future)) - -As you can see: in order to run the calculation one just needs to -provide code, structure, parameters and atomic\_radii file that are all -directly taken from the workflow inputs. The job submission happens in -exactly the same way as it was for the `single raspa -calculation `__ -that we tried previously. - -.. note:: - The ``self.report()`` functions provides a convenient way to - report the status of a workflow that can be access from the verdi - command line via ``verdi work report `` - -Step 3, 5: Compute the methane loading --------------------------------------- - -Steps 3 (``run_loading_raspa_low_p``) and 5 -(``run_loading_raspa_high_p``) compute the methane loading at 5.8 and 65 -bars respectively in [molecules/cell] units. The functions are defined -as follows: - -.. code:: python - - def run_loading_raspa_low_p(self): - self.ctx.current_pressure = 5.8e5 - self.ctx.current_pressure_label = "low" - return self._run_loading_raspa() - - def run_loading_raspa_high_p(self): - self.ctx.current_pressure = 65e5 - self.ctx.current_pressure_label = "high" - return self._run_loading_raspa() - -and finally execute the same ``_run_loading_raspa`` function. - -.. code:: python - - def _run_loading_raspa(self): - """Perform raspa calculation at given pressure. - - Most of the runtime will be spent in this function. - """ - # Create the input dictionary - inputs = { - 'code': self.inputs.raspa_code, - 'structure': self.inputs.structure, - 'parameters': update_raspa_parameters(self.inputs.raspa_parameters, Float(self.ctx.current_pressure)), - 'block_component_0': self.ctx.zeopp['block'], - '_options': self.ctx.options, - } - - # Create the calculation process and launch it - process = RaspaCalculation.process() - future = submit(process, **inputs) - self.report("pk: {} | Running raspa for the pressure {} [bar]".format(future.pid, self.ctx.current_pressure / 1e5)) - - return ToContext(raspa=Outputs(future)) - -``ToContext()`` will create a variable ``self.ctx.raspa`` that will -contain the results of the calculation. - -``Outputs()`` function will wait for the calculation to be completed. - -Step 4, 6: Extract pressure and methane loading ------------------------------------------------ - -Steps 4 and 6 extract pressure and methane loading (with deviation) and -puts them into ``loading_average`` and ``loading_dev`` dictionaries -stored in the **context**. - -.. code:: python - - """Extract pressure and loading average of last completed raspa calculation.""" - loading_average = self.ctx.raspa["component_0"].dict.loading_absolute_average - loading_dev = self.ctx.raspa["component_0"].dict.loading_absolute_dev - self.ctx.loading_average[self.ctx.current_pressure_label] = loading_average - self.ctx.loading_dev[self.ctx.current_pressure_label] = loading_dev - -Step 7: Store the selected computed parameters as the output node ------------------------------------------------------------------ - -This final step is to prepare the results of the ``DcMethane`` workchain -extracting the most relevant information and putting it in a -``ParameterData`` object. - -In particular, we extract the deliverable capacities at low and high -pressures that are computed in previous steps. We transform data from -[molecule/unit cell] units to [cm:sup:`3\_STP/cm`\ 3] using the -conversion factor provided by raspa -(``conversion_factor_molec_uc_to_cm3stp_cm3``). We also [compute] -(https://en.wikipedia.org/wiki/Sum\_of\_normally\_distributed\_random\_variables) -the standard deviation of the difference. - -.. code:: python - - def extract_results(self): - """Extract results of the workflow. - - Attaches the results of the raspa calculation and the initial structure to the outputs. - """ - from math import sqrt - cf = self.ctx.raspa["component_0"].dict.conversion_factor_molec_uc_to_cm3stp_cm3 - dc = self.ctx.loading_average["high"] - self.ctx.loading_average["low"] - dc_dev = sqrt(self.ctx.loading_dev["high"]**2 + self.ctx.loading_dev["low"]**2) - - result = { - "deliverable_capacity": dc * cf, - "deliverable_capacity_units": "cm^3_STP/cm^3", - "deliverable_capacity_dev": dc_dev * cf, - "loading_absolute_average_low_p" : self.ctx.loading_average["low"] * cf, - "loading_absolute_dev_low_p" : self.ctx.loading_dev["low"] * cf, - "loading_units" : "cm^3_STP/cm^3", - "loading_absolute_average_high_p" : self.ctx.loading_average["high"] * cf, - "loading_absolute_dev_high_p" : self.ctx.loading_dev["high"] * cf, - } - self.out("result", ParameterData(dict=result)) - - self.report("Workchain <{}> completed successfully".format( - self.calc.pk)) - return - -Exercises ---------- - -1. Before you actually start doing the calculations please setup the - zeo++ code as shown here:: - - * PK: 60109 - * UUID: 8a37224a-1247-4484-b3c8-ce3e8f37cee7 - * Label: zeopp - * Description: zeo++ code for the molsim course - * Default plugin: zeopp.network - * Used by: 1 calculations - * Type: remote - * Remote machine: bazis - * Remote absolute path: - /home/molsim20/network - * prepend text: - # No prepend text. - * append text: - # No append text. - - Should you have any doubts, just consult the `Computer setup and configuration `__ part of our tutorial. - -2. The following script is necessary to run the ``DcMethane`` workchain. - You need to save it as ``run_DcMethane.py``, edit it with your - settings and run it with ``verdi run run_DcMethane.py``. - - .. code:: python - - from aiida.backends.utils import load_dbenv, is_dbenv_loaded - if not is_dbenv_loaded(): - load_dbenv() - - import os - import sys - import time - from deliverable_capacity import DcMethane - - from aiida.orm import DataFactory - from aiida.orm.data.cif import CifData - from aiida.orm.data.base import Float - from aiida.work.run import run, submit - - NetworkParameters = DataFactory('zeopp.parameters') - ParameterData = DataFactory('parameter') - - def multiply_unit_cell(cif, cutoff): - """Returns the multiplication factors (tuple of 3 int) for the cell vectors - that are needed to respect: min(perpendicular_width) > threshold - """ - from math import cos, sin, sqrt, pi - import numpy as np - deg2rad=pi/180. - struct=cif.values.dictionary.itervalues().next() - a = float(struct['_cell_length_a']) - b = float(struct['_cell_length_b']) - c = float(struct['_cell_length_c']) - alpha = float(struct['_cell_angle_alpha'])*deg2rad - beta = float(struct['_cell_angle_beta'])*deg2rad - gamma = float(struct['_cell_angle_gamma'])*deg2rad - v = sqrt(1-cos(alpha)**2-cos(beta)**2-cos(gamma)**2+2*cos(alpha)*cos(beta)*cos(gamma)) - cell=np.zeros((3,3)) - cell[0,:] = [a, 0, 0] - cell[1,:] = [b*cos(gamma), b*sin(gamma),0] - cell[2,:] = [c*cos(beta), c*(cos(alpha)-cos(beta)*cos(gamma))/(sin(gamma)),c*v/sin(gamma)] - cell=np.array(cell) - diag = np.diag(cell) - return tuple(int(i) for i in np.ceil(cutoff/diag*2.)) - - cutoff = 8.8 #TO EDIT - probe_radius = 1.865 #Why this value? - - zeopp_params = NetworkParameters(dict={ - 'ha': True, - 'block': [probe_radius, 100], - }) - - # Search for the structures to evaluate and submit them - q = QueryBuilder() - q.append(CifData, filters={'label': { 'in': [ ...] }}) #TO EDIT: provide labels of the structures you want to submit - - for item in q.all(): - cif = item[0] - print (cif) - nx, ny, nz = multiply_unit_cell(cif, cutoff) - unitscells="{} {} {}".format(nx,ny,nz) - - raspa_params = ParameterData(dict={ - "GeneralSettings": - { - "SimulationType" : "MonteCarlo", - "NumberOfCycles" : 888, #TO EDIT - "NumberOfInitializationCycles" : 888, #TO EDIT - "PrintEvery" : 100, - - "CutOff" : cutoff, - - "Forcefield" : "UFF-TraPPE", - "ChargeMethod" : "None", - "UnitCells" : " ", - "ExternalTemperature" : 298, - - }, - "Component": - [{ - "MoleculeName" : "methane", - "MoleculeDefinition" : "TraPPE", - "MolFraction" : 1.0, - "TranslationProbability" : 8.8, #TO EDIT - "RotationProbability" : 8.8, #TO EDIT - "ReinsertionProbability" : 8.8, #TO EDIT - "SwapProbability" : 8.8, #TO EDIT - "CreateNumberOfMolecules" : 8, #TO EDIT - }], - }) - - - outputs = submit( - DcMethane, - structure=cif, - zeopp_parameters = zeopp_params, - raspa_parameters = raspa_params, - atomic_radii = load_node('27d2af72-3af0-48a6-a563-24d1d6d6eb60'), - zeopp_code=Code.get_from_string('zeopp@bazis1'), - raspa_code=Code.get_from_string('raspa@bazis1'), - ) - time.sleep(40) - - -.. note:: - - The function ``multiply_unit_cell`` is automatically - computing the number of ``UnitCells`` needed, ``nx ny nz``. This - function contains the math to `deal also with non-orthogonal unit - cells <../theoretical/multiply-uc>`__. - - Consult the `Querying the AiiDA database <../tutorial/queries>`__ - part of the tutorial in order to find out which filter you should - put in ``q.append(CifData, filters={})`` to select the appropriate - structures. diff --git a/docs/pages/2019_molsim_school_Amsterdam/theoretical/502-mofs.md b/docs/pages/2019_molsim_school_Amsterdam/theoretical/502-mofs.md deleted file mode 100644 index 628369bd..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/theoretical/502-mofs.md +++ /dev/null @@ -1,32 +0,0 @@ -# Origin of the MOF database - -The structures of the 502 Metal Organic Frameworks (MOFs) you are asked to analyze -come mainly from powder or single crystal X-Ray diffraction. - -These structures have been uploaded to the -[Cambridge Structural Database (CSD)](https://www.ccdc.cam.ac.uk/solutions/csd-system/components/csd/), -and have been parsed to reject messy structures and remove the solvent molecules inside the pores. -This database was named [Computational Ready Experimental (CoRE) MOF database](http://gregchung.github.io/CoRE-MOFs/). - -* In 2014 the CSD reported more than 600k structures -* About 60k structures were identified as MOFs -* About 20k MOFs structures were identified having a 3D network topology -* 5109 were identified as "not messy" and porous (i.e., having pore limiting diameter > 2.4 Angstrom) [(Chung2014)](https://pubs.acs.org/doi/abs/10.1021/cm502594j) -* From 5109 3D MOFs, 2932 survived a DFT calculation, using Quantum Espresso, to compute DDEC point charges [(Nazarian2016a)](https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.5b03836) -* From 5109 3D MOFs, 838 survived a DFT geometry optimization in CP2K [(Nazarian2016b)](https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.6b04226) -* 502 MOFs are the intersection of the two sets, having optimized geometry from CP2K and charges from Quantum Espresso - -Don't pay much attention of the names of these structures. They are just strings -with random combinations of six letters. - -In case you you wonder if there are duplicates in the 502, -see [(Barthel2018)](https://pubs.acs.org/doi/abs/10.1021/acs.cgd.7b01663) - ---- - -## Exercise - -The 502 structures are provided with atomic partial charges. - -Do you need them to evaluate the methane deliverable capacity? -How are the interactions between molecule and framework described? diff --git a/docs/pages/2019_molsim_school_Amsterdam/theoretical/charged-adsorbates.md b/docs/pages/2019_molsim_school_Amsterdam/theoretical/charged-adsorbates.md deleted file mode 100644 index 0a30c6df..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/theoretical/charged-adsorbates.md +++ /dev/null @@ -1,18 +0,0 @@ -# MOFs for CO2 capture - -The sequestration of carbon dioxide from flue gasses is an active field of research, -where MOFs appear to be very promising. - ---- - -## Challenge - -Can you find the best MOF for [carbon capture](https://en.wikipedia.org/wiki/Carbon_capture_and_storage)? - -* You can use the CO2 molecule model from the TraPPE force field -* The 502 structures already have charges, so in Raspa remember to enable - the calculation of Coulombic interactions using the Ewald method. -* A typical reference for indicating the material performance for carbon capture is the - CO2 uptake that you can achieve at 298K and 0.2bar. - -What do you think are the characteristics of materials with high affinity to carbon dioxide? diff --git a/docs/pages/2019_molsim_school_Amsterdam/theoretical/geometric-properties.rst b/docs/pages/2019_molsim_school_Amsterdam/theoretical/geometric-properties.rst deleted file mode 100644 index 6432c235..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/theoretical/geometric-properties.rst +++ /dev/null @@ -1,64 +0,0 @@ -Geometric properties -==================== - -There are several metrics we want you to use to pre-evaluate the -performance of a material for methane storage. - -We used `Zeo++ `__ to compute this -properties. - -- *largest included sphere (Di)*, the largest sphere that can fit the - largest pore of the structure - -- *largest free sphere (Df)*, the largest spherical probe that can - percolate through the material - -- *largest included sphere along the free sphere path (Dif)*, the - largest sphere that can fit along the channel where the largest free - sphere was evaluated. Therefore Di = Dif if there is only one channel - or if the largest pore is along the largest channel. - -.. figure:: ../assets/spheres.png - :width: 400px - - Graphical illustration of Di, Df and Dif. - -- *internal surface area*, considering the atoms of the frameworks as - spheres, and rolling a spherical probe on their surface. By - convention the probe has a radius of 1.86 Angstrom, half the kinetic - diameter of the nitrogen molecule. This is a purely geometrical - quantity and may differ a lot with the measured BET surface - `(Rouquerol2007) `__. - -- *geometric pore volume*, considering the atoms of the frameworks as - spheres, all the volume outside the spheres. - -- *probe-occupiable pore volume* (or Connolly volume ), considering the - atoms of the frameworks as spheres, all the pore volume where a - spherical probe could fit. The pore volume can also be distinguished - into accessible or not-accessible, if the probe can actually diffuse - or not inside that space: in the second case you are expected to use - blocking spheres to avoid the insertion of gas molecule in Monte - Carlo algorithms. - -- *void fraction*, fraction of pore volume over the total volume of the - unit cell. If you wonder why sometimes it is called "helium void - fraction", it was a convention to use the helium atom as probe to - compute (BC: NOT measure experimentally!) this quantity. However, - using helium to compute the void fraction is not very physically - meaningful - `(Ongari2017) `__. - -.. figure:: ../assets/pore_volume.png - :width: 420px - - Explanation of the different kind of pore volumes. - -- *number of channels*, the number of unconnected channels of the - porous material where the probe can diffuse through - -It is very interesting to compare these metrics to the performance of -your materials for a certain application, to be able to find any -correlation that could help to discover the best framework for that -application. See for example -`Wilmer2012 `__. diff --git a/docs/pages/2019_molsim_school_Amsterdam/theoretical/multiply-uc.rst b/docs/pages/2019_molsim_school_Amsterdam/theoretical/multiply-uc.rst deleted file mode 100644 index b2bbb7ef..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/theoretical/multiply-uc.rst +++ /dev/null @@ -1,33 +0,0 @@ -Multiplying the unit cell -========================= - -You learned that the lengths of your simulation box should bigger than -twice the cutoff value.\\ Therefore, for an orthogonal cell you should -multiply you cell until its length meets this criterion in every -direction. - -But what if the cell is not orthogonal? - -You should not speak in terms of "lengths" but in terms of -"perpendicular lengths", as shown in the figure for the two-dimensional -case. While in the orthogonal case one can simplify pwa = b and pwb = a, -in a tilted unit cell we have to compute pwa and pwb and then evaluate -if the cell needs to be expanded, and the multiplication coefficients. - -.. figure:: ../assets/perp_width.png - :width: 100% - - Perpendicular widths in orthogonal and tilted 2D cells. - -This explains why we need so much math in the function -``multiply_unit_cell(cif)``, to compute the Raspa input "UnitCells". - -Note that if you do not multiply correctly the unit cell, Raspa will -complain in the output: - -.. code:: console - - WARNING: INAPPROPRIATE NUMBER OF UNIT CELLS USED - -and you will (usually) get an uptake that is less then the correct one. -Why? diff --git a/docs/pages/2019_molsim_school_Amsterdam/theoretical/settings-raspa.rst b/docs/pages/2019_molsim_school_Amsterdam/theoretical/settings-raspa.rst deleted file mode 100644 index 204ee304..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/theoretical/settings-raspa.rst +++ /dev/null @@ -1,139 +0,0 @@ -Settings for RASPA -================== - -The RASPA package can perform MC and MD calculations, for a variety of -systems. - -- `paper `__ -- `GitHub `__ -- `Manual `__ - -In this tutorial we use this program to perform Grand Canonical (GCMC) -calculations in nanoporous structures. Here it is basic input: - -:: - - SimulationType MonteCarlo - NumberOfInitializationCycles *** - NumberOfCycles *** - PrintEvery *** - - RestartFile no - RestartStyle Raspa - - CutOff *** - - Forcefield UFF-TraPPE - - ChargeMethod *** - - Framework 0 - FrameworkName *** - UnitCells * * * - - ExternalTemperature *** - ExternalPressure *** - - Component 0 MoleculeName methane - MoleculeDefinition TraPPE - BlockPockets yes/no - BlockPocketsFileName ***** - TranslationProbability *** - RotationProbability *** - ReinsertionProbability *** - SwapProbability *** - CreateNumberOfMolecules *** - -These settings are imported in AiiDA as a ParameterData object, with the -"shape" of a python dictionary. - -You should fill the input settings, replacing asterisks with meaningful -values. - -Consider that the forcefield parameters that you will be using to model -the framework-molecule interactions are on your remote computer: - -- ``$RASPA_DIR/share/raspa/forcefield/UFF-TraPPE/`` contains the - Lennard-Jones parameters for the framework atoms, from - `UFF `__, and for - methane, from - `TraPPE `__ -- ``$RASPA_DIR/share/raspa/molecules/TraPPE/`` contains the file - ``methane.def`` with the critical T and P and acentric factor of - methane (why?) and the geometry of this gas molecule. Note that we - are using a forcefield where methane is modelled as a single point! - -As an extra exercise you may decide to use another popular forcefield -for the framework atoms: -`DREIDING `__. Note -that, since it does not contain all the atoms of the periodic table, it -is common practice to use UFF parameters for the missing ones.\\ You can -find this forcefield as -``$RASPA_DIR/share/raspa/forcefield/DREIDING-TraPPE/``, and remember to -modify ``Forcefield DREIDING-TraPPE`` in the input. - -You can even modify, create or import your own forcefield: just create -the folder ``$RASPA_DIR/share/raspa/forcefield/{ffname}/``, copy the -file formatting of the other forcefield .def files and specify -``Forcefield {ffname}`` in the input. - -A few hints for the settings: -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -- ``BlockPockets`` and ``BlockPocketsFileName`` will be filled by - AiiDA: if Zeo++ finds some non accessible pore volume, it can - generate a .block file with the positions and the radii of blocking - spheres. These spheres are inserted in the framework to prevent Raspa - from inserting molecules in the non accessible pore. - -- You need to choose ``NumberOfInitializationCycles`` and - ``NumberOfCycles``. Consider that for every cycle every molecule of - your system is selected to perform a MC movement that may be accepted - or rejected. If your system is empty or there are a few particles, - Raspa tries to perform GCMC insertions ("swaps") to fill your system - (see UMS, Algorithm 13). The number of particles continues to - fluctuate according to the Grand Canonical ensemble, and consider - that after ``NumberOfInitializationCycles`` cycles, Raspa start to - compute the average number of particles for ``NumberOfCycles`` - cycles. Therefore, the number of cycles should be large enough to - compute statistics correctly but not too large to waist computational - times after your uncertainty is already low. Standard deviation is - computed using Block Averages (see UMS pag. 529). - -.. figure:: ../assets/gcmc.png - :width: 98% - - Here an example of the value fluctuation and average, with the averaging starting after 10k cycles. - -- You have to chose the probability for the different swap movements. - The total will be normalized: for example if you assign 2.0, 1.0, - 1.0, 1.0, this will be normalized as 40%, 20%, 20%, 20% probability - for each move. - - - ``TranslationProbability``: rigid displacement by a random - distance. The maximum displacement is scaled during the simulation - to achieve an acceptance ratio of 50%. - - ``RotationProbability``: rotation around its starting bead - - ``ReinsertionProbability``: reinsertion of the particle in a - random position of the unit cell. - - ``SwapProbability``: insertion or deletion move. Whether to insert - or delete is decided randomly with a probability of 50% for each. - The swap move imposes a chemical equilibrium between the system - and an imaginary particle reservoir for the current component. - -- Choose a reasonable ``cutoff`` (Angstrom) to exclude negligible - Lennard-Jones interaction between far particles. Consider that the - higher the cutoff the more you will have to expand your structures! - -- You can choose ``ChargeMethod`` to ``None`` or ``Ewald``, depending - if you want or not to compute Coulombic interactions. - -- ``FrameworkName`` will be filled by AiiDA - -- ``UnitCells`` will be filled by the multiply\_unit\_cell(cif) - function: it gives the cell expansion that you need to perform - according to the cutoff you chose. - -- With ``CreateNumberOfMolecules`` you can specify the number of - particles that will be inserted (randomly) in your system at the - start of your simulation. diff --git a/docs/pages/2019_molsim_school_Amsterdam/tutorial/candidate-selection.md b/docs/pages/2019_molsim_school_Amsterdam/tutorial/candidate-selection.md deleted file mode 100644 index 3accfc30..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/tutorial/candidate-selection.md +++ /dev/null @@ -1,116 +0,0 @@ -# Selecting candidate materials - -If we had unlimited computational resources, we would simply screen the whole -database. -From a tutorial perspective, however, there is not much difference between -screening 5 or screening 500 structures, except for the longer wait time. - -Instead, you will now use what you've learned about extracting information from -the AiiDA database in order to select 3 good candidate materials, whose deliverable -capacities you are going to compute. - -## What makes a good material for methane storage - -You've already learned this morning about some key descriptors that can be used to guess whether a nanoporous material can be suitable for methane storage. -You may want to consult the -[SLIDES](https://docs.google.com/presentation/d/1F_bczGaH8n3CSR6rFoP3z8d6rPbRY1B7t_YuiaO0qgw/edit?usp=sharing) -in order to refresh your memory, -and have a look at a [brief description](../theoretical/geometric-properties) of some important geometrical properties computed by zeo++. - ---- - -### Exercise 1 - -Pick two geometric descriptors to use for selecting your candidate materials. -Load a `NetworkCalculation` node and identify the corresponding keys for these two descriptors (and their units). - ---- - -## Finding good candidates - -Let's use the QueryBuilder in order get the full range of the two descriptors in the database: - -```python -qb=QueryBuilder() -qb.append(CifData, tag='cif') -qb.append(ParameterData, descendant_of='cif', - project=['attributes.Density', 'attributes.Number_of_channels'] -) -result = qb.all() -``` - -In order to figure out which properties zeo++ computed, have a look at the attributes -of one of the zeo++ output nodes, for example: - -```python -pm=load_node('f1834bb1-3279-40ca-94dc-a02832143d0d') -pm.get_attrs() -``` - -> **Note** -> We are using `Density` and `Number_of_channels` here but this combination -> is just an example (and not an ideal choice). -> If you are wondering how to set up `ParameterData` and `CifData`, see -> [the previous section](queries#the-aiida-querybuilder). - -Plot the result using the plotting library of your choice. -Using `matplotlib` you would do something like - -```python -import matplotlib.pyplot as plt -x,y = zip(*result) -plt.plot(x,y,'o') -plt.show() -``` - ---- - -### Exercise 2 - -Use the information from the plots to identify a suitable target range for your -descriptors and filter the structures within this range. - -Once you've identified the range of your two parameters, -get the `label`s of the structure in this range. -Note that you can combine filters like so: - -```python - filters = { "and": [ - { 'attributes.Density': {'and': [{'>': 1.0}, {'<': 1.5}] } }, - { 'attributes.Number_of_channels': {'>': 1}}, - ]}, -``` - -For explanations on filters, see [the previous section](queries#filters). - -> **Note** -> The `label` can be used to give human-readable identifiers to any AiiDA node. -> By default it is empty, but we have added labels for all `CifData` nodes -> in the database. - ---- - -Finally, put the structures you've identified into a group `candidates` -so that you can refer to them easily from now on. -In addition to your three structures, also add 'HKUST1' in order to compare -to the reference calculation provided later on. - -```python -candidate_labels = ['HKUST1'] # add your labels! -qb=QueryBuilder() -qb.append(CifData, filters={ 'label': {'in': candidate_labels}}) -cifs = qb.all() -candidates, created = Group.get_or_create(name='candidates') # create & store new group -candidates.add_nodes([ cif[0] for cif in cifs]) -``` - -After this, your group should show up in `verdi group list` -and you can use `verdi group show candidates` to inspect its content. - -In the python interface you can retrieve them back like so: - -```python -candidates = Group.get_from_string('candidates') -for cif in candidates.nodes: - print(cif) -``` diff --git a/docs/pages/2019_molsim_school_Amsterdam/tutorial/provenance-graph.md b/docs/pages/2019_molsim_school_Amsterdam/tutorial/provenance-graph.md deleted file mode 100644 index fde11064..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/tutorial/provenance-graph.md +++ /dev/null @@ -1,61 +0,0 @@ -Browsing the provenance graph -============================= - -Before we start creating data ourselves, we are going to look at an -AiiDA database already created by someone else. Let’s import one from the web: - -```console -$ verdi import https://www.dropbox.com/s/xz7h6m7dd91v3da/mof_database.aiida?dl=1 -``` - -Contrary to most databases, AiiDA databases contain not only results of -calculations but also their inputs and information on how a particular -result was obtained. -This information (provenance) is stored in the form of a directed acyclic graph (DAG). - -In the following, we are going to introduce you to different ways of browsing this graph -and ask you find out some information regarding the database you just imported. - -The EXPLORE interface ---------------------- - -The most intuitive way to browse AiiDA graphs is to use the interactive provenance -explorer available on [Materials Cloud](https://www.materialscloud.org). - -For it to work, we first need to start the AiiDA REST API: - -```console -$ verdi restapi - * Serving Flask app "aiida.restapi.run_api" (lazy loading) - * Environment: production - WARNING: Do not use the development server in a production environment. - Use a production WSGI server instead. - * Debug mode: off - * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) -``` - -Now - -* Open a browser **inside** Quantum Mobile -* Inside this browser, open the [link to the provenance browser](http://34.244.178.26:5001/explore/connect){:target="_blank"} -* In the form, paste the (local) URL `http://127.0.0.1:5000/api/v2` of our REST API and click "GO!" - -> **Note** -> The provenance browser is also [available on Materials Cloud](https://www.materialscloud.org/explore/connect) but we are using a more recent development version. - -Once the provenance explorer has been loaded by your browser, it is communicating directly with the -REST API and your data never leaves your computer. - -Start by clicking on the Details of a `NetworkCalculation` node -(if you ever get lost, just go to the Details subpage, enter `bba8402d-6559-4fd8-ad32-84625a6221f0` and click on the GO button). - ---- -### Exercise - -Use the graph explorer in order to figure out: - -* What code was used and what was the name of the executable? -* When was the calculation run and who run it? -* How much memory was requested for the calculation? -* What inputs did the calculation take? -* What other types of calculation was the `Cif` input structure used in? diff --git a/docs/pages/2019_molsim_school_Amsterdam/tutorial/python-interface.md b/docs/pages/2019_molsim_school_Amsterdam/tutorial/python-interface.md deleted file mode 100644 index 6e4acbfc..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/tutorial/python-interface.md +++ /dev/null @@ -1,94 +0,0 @@ -# The AiiDA python interface - -AiiDA is written in python, and while the `verdi` command line interface -provides handy shortcuts for everyday operations, AiiDA provides its -full power only via the python interface. - -## Using the python interface - -There are three practical ways of using the python interface: - - 1. python scripts that `import` the `aiida` python package - 2. the interactive `verdi shell` - 3. `Jupyter` notebooks - -For this tutorial we recommend using the `verdi shell` (explained below) -but if you're already familiar with `Jupyter` notebooks you may use those as well. -We will get back to option 1 (python scripts) towards the end of the tutorial. - -### The verdi shell - -The `verdi shell` is a customized ipython shell, where all the AiiDA classes, -methods and functions are already accessible. Type in the terminal - -```console -$ verdi shell -``` - -The `verdi shell` is handy for everyday AiiDA-based operations, e.g. creating, -querying and using AiiDA objects. -You would typically use two terminals, one for the -`verdi shell` and one to execute bash commands. - -> **Note** -> Press `Ctrl+Shift+T` in order to open a new terminal tab. -> Don't forget to `workon aiida` in the new tab before using the shell. - -### Jupyter notebooks - -Start a jupyter notebook server: - -```console -$ jupyter notebook -``` - -In the new browser window, select `New -> Python 2` (top right corner). -You are now inside a jupyter notebook, consisting of cells where you can type -portions of python code. The code will not be executed until you press -`Shift+Enter` from within a cell. - -In order to load the same environment as in the `verdi shell`, type `%aiida` -in the first cell and execute it. - -> **Note** -> The `verdi shell` and the `jupyter notebook` are completely equivalent. -> Use either according to your personal preference. - -You will still need sometimes to type command-line instructions in -the terminal. -Either keep a terminal open on the side or use execute terminal commands -directly from the `verdi shell` or `jupyter notebook` by prefixing the -command by an exclamation mark: - -```python -!verdi profile list -``` - -## Loading a node - -Most AiiDA objects are represented by nodes, identified in the database -by their PK number (an integer). You can access a node using the following -command in the shell: - -```python -node = load_node() -``` - -> **Note** -> In the following, we'll use a dark gray border to indicate python code -> and a light gray border to indicate the terminal. - -Load one of the calculation nodes you played around with before. -Then get the density computed with the command - -```python -node.out.output_parameters.get_dict() -``` - -You can also type - -```python -node.res. -``` - -and then press `TAB` to directly access the keys of the output dictionary. diff --git a/docs/pages/2019_molsim_school_Amsterdam/tutorial/queries.md b/docs/pages/2019_molsim_school_Amsterdam/tutorial/queries.md deleted file mode 100644 index 3e119bb0..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/tutorial/queries.md +++ /dev/null @@ -1,159 +0,0 @@ -# Querying the AiiDA database {#sec:querybuilder} - -We will now learn how to *query* the AiiDA database using the python interface. -Queries are, in essence, questions to your database and you will use the -answers you obtain in order to identify good candidate materials for methane -storage. - -## The AiiDA QueryBuilder - -AiiDA provides a querying tool called the [QueryBuilder](http://aiida-core.readthedocs.io/en/latest/querying/querybuilder/usage.html). -When working in the `verdi shell` or a jupyter notebook with `%aiida` enabled, -the `QueryBuilder` class has already been imported. - -Let's create a new querybuilder for our query: - -```python -qb = QueryBuilder() -``` - -Now we need to say which nodes of the AiiDA graph we are interested in. -All node types of the AiiDA graph derive from the `Node` class, so in order to select the most -generic type of node, simply append `Node`: - -```python -qb.append(Node) -``` - -At this point, we can finish our query by asking for all nodes matching these criteria: - -```python -qb.all() # Returns all nodes in the database -``` - -> **Note** -> We could also have used `qb.first()` to get the first node. -> Always use tab-completion when in doubt which functions can be used on a python object. - -This command will return us all the Nodes directly, which may -not be the most wise thing to do considering that is the biggest family -of AiiDA stored objects that we can query. -To understand the size of the result, we can type the following command: - -```python -qb.count() # Returns an integer, the number of nodes in the database -``` - -If you are interested to retrieve a subclass of a node, append that -specific subclass instead of Node: - -```python -CifData = DataFactory('cif') -qb = QueryBuilder() # Creating a new QueryBuilder instance -qb.append(CifData) # Telling the QueryBuilder instance that I want a cif data type -qb.all() # Asking for all the results! -``` - -> **Note** -> Remember to create a new `qb = QueryBuilder()` instance for each new query. - -Other data types we will need in the tutorial are: - -```python -ParameterData = DataFactory('parameter') -NetworkCalculation = CalculationFactory('zeopp.network') -RaspaCalculation = CalculationFactory('raspa') -``` - ---- -### Exercise - -Find the number of `CifData` and `NetworkCalculation` nodes in the database. -Is there one calculation for every structure? - -You may also be interested to learn [where the structures are coming from](../theoretical/502-mofs). - ---- - -| Node & subclasses | Number in DB -|-------------------| -------------- -| Node | 2519 -| StructureData | 0 -| CifData | 503 -| ParameterData | 503 -| SinglefileData | 1 -| Code | 1 -| JobCalculation | 503 - -*List of some Node subclasses and how many times they occur in our test database.* - -> **Note** -> Advanced users who are familiar with SQL -> (Structured Query Language) syntax, can inspect the generated SQL query via: -> ```python -> str(qb) -> ``` - -## Projections - -So far, we've retrieved node objects but we are interested in the information they contain. -In database language, performing a projection means to extract one or -more specific columns from a table. In AiiDA language it means to -specify which properties of the selected nodes our query should return. - -|---------| -------------------------| -|Entity | Properties | -|---------| -------------------------| -|Node | id, uuid, type, label, description, ctime, mtime| -|Computer | id, uuid, name, hostname, description, enabled, transport_type, scheduler_type| -|User | id, email, first_name, last_name, institution| -|Group | id, uuid, name, type, time, description| -|---------| -------------------------| - -### A selection of entities and some of their properties - -For example, we might be interested only in type and universally unique -identifier (UUID) of each node: - -```python -qb = QueryBuilder() -qb.append(Node, project=["type", "uuid"]) -qb.all() -``` - -> **Note** -> Please use the `id` keyword in QueryBuilder queries to access the PK of a node. - -## Filters - -We already know how to filter by the type of node, -but often we would like to filter the results by other node properties. - -For example, we might want to select all -the calculations that were launched on a specific date. In database -language, this is called "adding a filter" to a query. A filter is a -boolean operator that returns `True` or `False`. - -| Operator | Datatype | Example -|-------------| -------------------------| ------------------------------------ -| == | All | `{'==':12}` -| in | All | `{'in':['FINISHED', 'PARSING']}` -| >,<,<=,>= | floats, integers, dates | `{'>':5.2}` -| like | Chars | `{'like':'calculation%'}` -| ilike | Chars | `{'ilike':'caLculAtioN%'}` -| or | | `{'or':[{'<':5.3}, {'>':6.3}]}` -| and | | `{'and':[{'>':5.3}, {'<':6.3}]}` - -### Selection of filter operators - -If you want to add filters to your query, use the `filters` -and provide a dictionary with the filters you would like to apply. -Let's filter out the structure with label "HKUST1": - -```python -qb = QueryBuilder() # Instantiating a new QueryBuilder -qb.append(CifData, # I want structures - project=["id"], # I want to know the id - filters={"label": {"==":"HKUST1"}}) # Only structures with this label -qb.all() -``` diff --git a/docs/pages/2019_molsim_school_Amsterdam/tutorial/setup.md b/docs/pages/2019_molsim_school_Amsterdam/tutorial/setup.md deleted file mode 100644 index c8dd00ec..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/tutorial/setup.md +++ /dev/null @@ -1,91 +0,0 @@ -Getting set up -=============== - -Download and install the [Quantum Mobile virtual machine](https://github.com/marvel-nccr/quantum-mobile/releases/tag/18.06.0), -which comes both with AiiDA already preconfigured and with a number of useful -codes and tools for computational materials science. - -Start the virtual machine, open a terminal window and -type - -```console -$ workon aiida -``` - -This activates the virtual environment, in which AiiDA is installed. - -Since Quantum Mobile focuses on *ab initio* calculations, it is missing -some aiida plugins we are going to need. Let’s install them (this can -take 3 minutes): - -```console -$ pip install aiidalab==19.01.2 -``` - -> **Note** -> You may encounter warnings about version conflicts. -> As long as the command finishes with `Successfully installed ...` don't worry and proceed. -> -> **Note** -> To make sure that AiiDA noticed all the plugin updates please run -> -> ```console -> $ reentry scan -> ``` - -Furthermore, you are currently using the `default` AiiDA profile. - -```console -$ verdi profile list -``` - -At the end of the tutorial, we'll ask you to submit the data you computed, -so let's create a new profile in order to associate this data with you: - -```console -$ verdi quicksetup -Profile name [quicksetup]: molsim2019 -Email Address (identifies your data when sharing): leopold.talirz@epfl.ch -First Name: Leopold -Last Name: Talirz -Institution: EPFL -Executing now a migrate command... -...for Django backend -Operations to perform: - Apply all migrations: contenttypes, db, sites, auth, sessions -Running migrations: - Applying contenttypes.0001_initial... OK - Applying auth.0001_initial... OK - Applying db.0001_initial... OK - Applying db.0002_db_state_change... OK - Applying db.0003_add_link_type... OK - Applying db.0004_add_daemon_and_uuid_indices... OK - Applying db.0005_add_cmtime_indices... OK - Applying db.0006_delete_dbpath... OK - Applying db.0007_update_linktypes... OK - Applying db.0008_code_hidden_to_extra... OK - Applying sessions.0001_initial... OK - Applying sites.0001_initial... OK -Database was created successfully -Loading new environment... -Installing default AiiDA user... -Starting user configuration for leopold.talirz@epfl.ch... -Configuring a new user with email 'leopold.talirz@epfl.ch' ->> User Leopold Talirz saved. << -** NOTE: no password set for this user, - so he/she will not be able to login - via the REST API and the Web Interface. -Setup finished. -The default profile for the 'verdi' process is set to 'default': do you want to set the newly created 'molsim2019' as the new default? (can be reverted later) [y/N]: y -The default profile for the 'daemon' process is set to 'default': do you want to set the newly created 'molsim2019' as the new default? (can be reverted later) [y/N]: y -``` - -Use `verdi profile list` to verify that you've switched to your new personal AiiDA profile. - -If you encounter any issues throughout the tutorial, please consider - -* consulting the extensive [AiiDA documentation](https://aiida-core.readthedocs.io/en/stable/) -* asking in the [Slack channel of the tutorial](http://bit.ly/molsim2019-slack) of the tutorial -* opening a new issue on the [tutorial issue tracker](https://github.com/aiidateam/aiida-tutorials/issues) -* asking your neighbor -* asking a tutor diff --git a/docs/pages/2019_molsim_school_Amsterdam/tutorial/verdi-commands.rst b/docs/pages/2019_molsim_school_Amsterdam/tutorial/verdi-commands.rst deleted file mode 100644 index 6e4a9889..00000000 --- a/docs/pages/2019_molsim_school_Amsterdam/tutorial/verdi-commands.rst +++ /dev/null @@ -1,149 +0,0 @@ -The verdi command line -====================== - -The ``verdi`` command line utility is another common way of interacting -with AiiDA, be it to investigate the provenance, to submit or monitor -calculations. - -.. code:: console - - $ verdi - Usage: verdi [--profile=PROFILENAME|-p PROFILENAME] COMMAND [] - - List of the most relevant available commands: - * calculation Query and interact with calculations - * code Setup and manage codes to be used - * comment Manage general properties of nodes in the database - * computer Setup and manage computers to be used - * daemon Manage the AiiDA daemon - * data Setup and manage data specific types - * devel AiiDA commands for developers - * export Create and manage AiiDA export archives - * graph Utility to explore the nodes in the database graph - * group Setup and manage groups - * help Describe a specific command - * import Import nodes and group of nodes - * install Install/setup aiida for the current user - * node Manage operations on AiiDA nodes - * profile List AiiDA profiles, and set the default profile. - * quicksetup Quick setup for the most common usecase (1 user, 1 machine). - * rehash Re-hash all nodes. - * restapi verdi command used to hook up the AiIDA REST API. - * run Execute an AiiDA script - * setup Setup aiida for the current user - * shell Run the interactive shell with the AiiDA environment loaded. - * user List and configure new AiiDA users. - * work Manage the AiiDA worflow manager - * workflow Manage the AiiDA legacy worflow manager - See 'verdi help' for more help. - - | **Note** - | ``verdi`` has tab completion. Just type a command and press Tab - twice to list the availble subcommands. - | Whenever you need help with a command, add the ``-h`` flag. - Fields enclosed in angular brackets, such as ````, are - placeholders to be replaced by the actual value of that field (an - integer, a string, etc...). - -Let's try to get a list of calculations using ``verdi``: - -.. code:: console - - $ verdi calculation list - -It turns out the list of calculations is empty because you currently -don't have any calculations running. - -.. admonition:: Exercise - - Use the ``-h`` flag to figure out how to list calculations of any state - (both running and finished ones) and from all users (both your current - AiiDA profile and others). - - How do you list all calculations that ran in the past week? - -Let's have a closer look at a zeo++ calculation node. Pick a primary key -(PK) number and run: - -.. code:: console - - $ verdi calculation show - ----------- ------------------------------------ - type NetworkCalculation - pk 224 - uuid 5a92d1e2-e243-4386-8ba1-e6c540e93b20 - label - description - ctime 2019-01-14 19:11:30.678462+00:00 - mtime 2019-01-14 21:17:44.639323+00:00 - computer [1] fidis - code zeopp - ----------- ------------------------------------ - ##### INPUTS: - Link label PK Type - ------------ ---- ----------------- - atomic_radii 1257 SinglefileData - parameters 2461 NetworkParameters - structure 1830 CifData - ##### OUTPUTS: - Link label PK Type - ----------------- ---- ------------- - remote_folder 2331 RemoteData - retrieved 582 FolderData - output_parameters 583 ParameterData - -This provides a textual representation of the provenance of a single -node, similar to what we've already seen in the interactive provenance -browser before. - -Another way to produce a visual representation of the provenance is -``verdi graph``. - -.. admonition:: Excercise - - Pick a PK number from the list of calculations and generate a provenance - graph for a ``NetworkCalculation``: - - .. code:: console - - $ verdi graph generate - $ dot -Tpdf -o .pdf .dot # convert .dot to .pdf - $ evince .pdf # open pdf - -.. figure:: ../assets/zeopp_sample_graph.png - :width: 620px - - Dependency graph of a zeo++ calculation. - -Contrary to the interactive graph browser, ``verdi graph generate`` can -show not only the direct inputs and outputs of the given node (shown in -blue) but the full graph the node is connected to. We will make use of -this functionality later. - -.. admonition:: Exercise - - Famliarize yourself with the other options of the ``calculation`` and - ``data`` subcommands, such as: - - .. code:: console - - verdi data parameter show # for ParameterData PKs - verdi data cif show --format jmol # for CifData PKs - verdi calculation res # for Calculation PKs - verdi calculation show # for Calculation PKs, shows code & computer labels - verdi code show