diff --git a/.github/actions/create-dev-database/action.yml b/.github/actions/create-dev-database/action.yml index d7ffc002a..799ab3846 100644 --- a/.github/actions/create-dev-database/action.yml +++ b/.github/actions/create-dev-database/action.yml @@ -14,7 +14,7 @@ inputs: runs: using: "composite" steps: - - run: docker-compose up -d + - run: docker compose up -d working-directory: ${{ github.action_path }} shell: bash env: diff --git a/.github/workflows/ci-docs.yml b/.github/workflows/ci-docs-dev.yml similarity index 56% rename from .github/workflows/ci-docs.yml rename to .github/workflows/ci-docs-dev.yml index 421d9b59f..9cbd86a50 100644 --- a/.github/workflows/ci-docs.yml +++ b/.github/workflows/ci-docs-dev.yml @@ -1,23 +1,23 @@ -name: ci docs +name: ci docs dev -# a merged PR will trigger a push on master so merging to master is +# a merged PR will trigger a push on dev so merging to dev is # included in the event below on: push: branches: - - master + - dev + jobs: deploy: runs-on: ubuntu-latest - if: github.event.pull_request.head.repo.fork == false steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 with: fetch-depth: 0 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v5 with: - python-version: 3.x - - uses: Gr1N/setup-poetry@v4 + python-version: 3.9 + - uses: Gr1N/setup-poetry@v9 - name: Install system dependencies run: sudo apt-get update && sudo apt-get install -y build-essential libpq-dev libgraphviz-dev - name: Cache Python dependencies @@ -27,5 +27,9 @@ jobs: key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} - name: Install python dependencies run: poetry install + - name: Configure Git user + run: | + git config --local user.email "github-actions[bot]@users.noreply.github.com" + git config --local user.name "github-actions[bot]" - name: Build and deploy docs - run: poetry run mkdocs gh-deploy -m 'github action deployment' --force + run: poetry run mike deploy --push dev diff --git a/.github/workflows/ci-docs-release.yml b/.github/workflows/ci-docs-release.yml new file mode 100644 index 000000000..9e64464f6 --- /dev/null +++ b/.github/workflows/ci-docs-release.yml @@ -0,0 +1,36 @@ +name: ci docs release + +on: + release: + types: [published] + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: 3.9 + - uses: Gr1N/setup-poetry@v9 + - name: Set release notes tag + run: | + export RELEASE_TAG_VERSION=${{ github.event.release.tag_name }} + echo "RELEASE_TAG_VERSION=${RELEASE_TAG_VERSION:1}" >> $GITHUB_ENV + - name: Install system dependencies + run: sudo apt-get update && sudo apt-get install -y build-essential libpq-dev libgraphviz-dev + - name: Cache Python dependencies + uses: actions/cache@v2 + with: + path: ~/.cache/pypoetry/virtualenvs + key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} + - name: Install python dependencies + run: poetry install + - name: Configure Git user + run: | + git config --local user.email "github-actions[bot]@users.noreply.github.com" + git config --local user.name "github-actions[bot]" + - name: Build and deploy docs + run: poetry run mike deploy --push --update-aliases ${RELEASE_TAG_VERSION} latest diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 000000000..70751d2ac --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,28 @@ +name: lint + +on: [pull_request] + +jobs: + lint: + runs-on: ubuntu-20.04 + + steps: + - uses: actions/checkout@v2 + - uses: ricardochaves/python-lint@v1.4.0 + with: + use-pylint: false + use-pycodestyle: false + use-flake8: true + use-black: false + use-mypy: false + use-isort: false + extra-pylint-options: "" + extra-pycodestyle-options: "" + extra-flake8-options: "--select=W291,W292,F401" + # flake8 options being used: + # - W291 trailing whitespace + # - W292 no newline at end of file + # - F401 module imported but unused + extra-black-options: "" + extra-mypy-options: "" + extra-isort-options: "" diff --git a/.github/workflows/test-suite.yml b/.github/workflows/test-suite.yml index 5a0390dc4..95c41980c 100644 --- a/.github/workflows/test-suite.yml +++ b/.github/workflows/test-suite.yml @@ -3,67 +3,88 @@ name: test-suite on: [pull_request] env: - db_password: 'postgres' - db_port: '5432' + db_password: "postgres" + db_port: "5432" jobs: test: - runs-on: ubuntu-18.04 + runs-on: ubuntu-20.04 strategy: fail-fast: false matrix: - python_version: ['3.7.5', '3.8.7', '3.9.1'] + python_version: ["3.8.12", "3.9.10", "3.10"] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v2 - - name: Create dev database - uses: ./.github/actions/create-dev-database - with: - db_password: ${{ env.db_password }} - db_port: ${{ env.db_port }} + - name: Create dev database + uses: ./.github/actions/create-dev-database + with: + db_password: ${{ env.db_password }} + db_port: ${{ env.db_port }} - - name: Install dependencies - run: sudo apt-get install -y libpq-dev libgraphviz-dev + - name: Install dependencies + run: sudo apt-get install -y libpq-dev libgraphviz-dev - - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python_version }} + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python_version }} - - uses: actions/setup-node@v2 - with: - node-version: '12' + - name: Install LLVM and Clang + uses: KyleMayes/install-llvm-action@v1 + with: + version: "10.0" + + - uses: snok/install-poetry@v1 + with: + version: 1.5.1 + virtualenvs-create: true + virtualenvs-in-project: true + - name: cache deps + id: cache-deps + uses: actions/cache@v2 + with: + path: .venv + key: pydeps-${{ hashFiles('**/poetry.lock') }} - - name: Set up frontend assets and install python dependencies - run: | - npm ci - npm start - pip install . + - run: poetry install --no-interaction --no-root + if: steps.cache-deps.outputs.cache-hit != 'true' + + - run: poetry install --no-interaction + + - uses: actions/setup-node@v4 + with: + node-version: "12" - - name: Set DATABASE_URL (using ansible) - run: > - cp webinterface/.env.template webinterface/.env && - ansible localhost - -m lineinfile - -a " - path=webinterface/.env - regex=^DATABASE_URL - line='DATABASE_URL=psql://vast:vastpsw@localhost:${{ env.db_port }}/vastdb' - " + - name: Set up frontend assets + run: | + npm ci + npm start - - name: Create database user and database name - run: > - python ./init-tools/init-db.py - localhost ${{ env.db_port }} - postgres ${{ env.db_password }} - vast vastpsw vastdb + - name: Set DATABASE_URL (using ansible) + run: > + cp webinterface/.env.template webinterface/.env && + ansible localhost + -m lineinfile + -a " + path=webinterface/.env + regex=^DATABASE_URL + line='DATABASE_URL=psql://vast:vastpsw@localhost:${{ env.db_port }}/vastdb' + " - - name: Make relevant directories - run: mkdir pipeline-runs + - name: Create database user and database name + run: > + poetry run python ./init-tools/init-db.py + localhost ${{ env.db_port }} + postgres ${{ env.db_password }} + vast vastpsw vastdb - - name: Download regression test data - run: bash -e ./download.sh - working-directory: ./vast_pipeline/tests/regression-data + - name: Make relevant directories + run: mkdir pipeline-runs - - name: Run tests - run: ./manage.py test + - name: Download regression test data + run: bash -e ./download.sh + working-directory: ./vast_pipeline/tests/regression-data + + - name: Run tests + run: poetry run python manage.py test diff --git a/.gitignore b/.gitignore index aa7139680..fc0174c80 100644 --- a/.gitignore +++ b/.gitignore @@ -109,3 +109,6 @@ docker/ .dockerignore .idea/ mypy.ini + +# mac specific +.DS_store diff --git a/CHANGELOG.md b/CHANGELOG.md index 7cb633e90..87640f654 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,9 +2,9 @@ All notable changes to this project will be documented in this file. -The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), with an added `List of PRs` section and links to the relevant PRs on the individal updates. This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), with an added `List of PRs` section and links to the relevant PRs on the individual updates. This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [Unreleased](https://github.com/askap-vast/vast-pipeline/compare/v1.0.0...HEAD) +## [Unreleased](https://github.com/askap-vast/vast-pipeline/compare/v1.1.0...HEAD) #### Added @@ -16,6 +16,199 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), #### List of PRs +## [1.1.0](https://github.com/askap-vast/vast-pipeline/releases/v1.1.0) (2024-10-14) + +#### Added + +- Added further memory usage and timing debug logging [#725](https://github.com/askap-vast/vast-pipeline/pull/725) +- Add support for python 3.10 [#740](https://github.com/askap-vast/vast-pipeline/pull/740) +- Added support calculate_n_partitions for sensible dask dataframe partitioning [#724](https://github.com/askap-vast/vast-pipeline/pull/724) +- Added support for compressed FITS files [#694](https://github.com/askap-vast/vast-pipeline/pull/694) +- Added links to Data Central DAS and the Fink Broker to the source page [#697](https://github.com/askap-vast/vast-pipeline/pull/697/) +- Added `n_new_sources` column to run model to store the number of new sources in a pipeline run [#676](https://github.com/askap-vast/vast-pipeline/pull/676). +- Added `MAX_CUTOUT_IMAGES` to the pipeline settings to limit the number of postage stamps displayed on the source detail page [#658](https://github.com/askap-vast/vast-pipeline/pull/658). +- Added run config option to skip calculating measurement pair metrics [#655](https://github.com/askap-vast/vast-pipeline/pull/655). +- Added support for Python 3.10 [#641](https://github.com/askap-vast/vast-pipeline/pull/641). +- Added documentation versioning [#627](https://github.com/askap-vast/vast-pipeline/pull/627). +- Added a ZTF cone search button on the source detail page [#626](https://github.com/askap-vast/vast-pipeline/pull/626). +- Added the 0-based index of each measurement to the image cutout card headers [#625](https://github.com/askap-vast/vast-pipeline/pull/625). +- Added Bokeh hover tooltip to measurement pair graph to display pair metrics [#625](https://github.com/askap-vast/vast-pipeline/pull/625). +- Added new VAST surveys (13-21) to the Aladin Lite panel [#622](https://github.com/askap-vast/vast-pipeline/pull/622). +- Added eta-V plot analysis page along with documentation [#586](https://github.com/askap-vast/vast-pipeline/pull/586). +- Added thumbnails to light curve tooltips [#586](https://github.com/askap-vast/vast-pipeline/pull/586). +- Added logfile dropdown selection to run detail page [#595](https://github.com/askap-vast/vast-pipeline/pull/595). +- Added datetime stamps to all log files [#595](https://github.com/askap-vast/vast-pipeline/pull/595). +- Added new log files for arrow file creation and restore run and added to run detail page [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Added restore run test [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Added new run status of `DEL`, `Deleting` [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Added documentation pages on new action buttons [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Added UI action buttons to run-detail page to allow arrow file generation, deletion and restoration [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Added try-except error capture on pre-run checks to correctly assign pipeline run as failed if an error occurs [#576](https://github.com/askap-vast/vast-pipeline/pull/576). +- Added support for ingesting Selavy catalogues in VOTable (XML) and CSV format [#565](https://github.com/askap-vast/vast-pipeline/pull/565) +- Added new commands: `initingest` and `ingestimages` [#544](https://github.com/askap-vast/vast-pipeline/pull/544) +- Added documentation on the data required to run the pipeline [#572](https://github.com/askap-vast/vast-pipeline/pull/572). +- Added support for ingesting Selavy catalogues in VOTable (XML) and CSV format [#565](https://github.com/askap-vast/vast-pipeline/pull/565). +- Added new commands: `initingest` and `ingestimages` [#544](https://github.com/askap-vast/vast-pipeline/pull/544). +- Added TNS cone search to the external search results on the source detail page [#557](https://github.com/askap-vast/vast-pipeline/pull/557). +- Added `HOME_DATA_ROOT` to the pipeline settings to override the OS default home directory location [#559](https://github.com/askap-vast/vast-pipeline/pull/559). +- Added processing spinner to source query table [#551](https://github.com/askap-vast/vast-pipeline/pull/551). +- Added `site_url` to the mkdocs config so static asset URLs have the correct base URL [#543](https://github.com/askap-vast/vast-pipeline/pull/543). +- Added basic linter to CI/CD [#546](https://github.com/askap-vast/vast-pipeline/pull/546) + +#### Changed + +- Force dask<2022.4.2, numpy<1.23, param<2.0 [#728](https://github.com/askap-vast/vast-pipeline/pull/728) +- Bumped versions for github actions packages [#728](https://github.com/askap-vast/vast-pipeline/pull/728) +- Changed pipeline.new_sources.parallel_get_rms_measurements to drop all but one RMS measurmeents [#730](https://github.com/askap-vast/vast-pipeline/pull/730) +- Use chunks for associations upload [#726](https://github.com/askap-vast/vast-pipeline/pull/726) +- Updated all FITS loading to use a wrapper that can handle compressed FITS files [#694](https://github.com/askap-vast/vast-pipeline/pull/694) +- Downgrade ci-docs to python 3.8 [#702](https://github.com/askap-vast/vast-pipeline/pull/702) +- Update Gr1N poetry to v8, force python 3.8.10 [#701](https://github.com/askap-vast/vast-pipeline/pull/701) +- Updated path to test data in github actions and docs [#699](https://github.com/askap-vast/vast-pipeline/pull/699) +- Changed GitHub actions test suite to install pipeline via poetry [#699](https://github.com/askap-vast/vast-pipeline/pull/699). +- Updated GitHub actions ubuntu version to 20.04 [#699](https://github.com/askap-vast/vast-pipeline/pull/699). +- Removed python 3.10 testing from GitHub actions [#699](https://github.com/askap-vast/vast-pipeline/pull/699). +- Updated GitHub actions Gr1N/setup-poetry to v7 [#665](https://github.com/askap-vast/vast-pipeline/pull/665). +- Changed lightcurve plot cursor hit-test mode from "vline" to "mouse" to avoid a regression in Bokeh [#652](https://github.com/askap-vast/vast-pipeline/pull/652). +- Updated Bokeh from v2.3.3 to v2.4.2 [#652](https://github.com/askap-vast/vast-pipeline/pull/652). +- Source query results table is no longer populated by default, a query must be submitted first [#638](https://github.com/askap-vast/vast-pipeline/pull/638). +- Bumped major versions of astropy (5.0) and pyarrow (7.0) [#641](https://github.com/askap-vast/vast-pipeline/pull/641). +- Addressed future pandas append deprecation, migrated all uses to pd.concat [#643](https://github.com/askap-vast/vast-pipeline/pull/643). +- Bumped all documentation dependancies to latest versions (incl. mkdocs-material minimum 8.2.4) [#627](https://github.com/askap-vast/vast-pipeline/pull/627). +- Changed GitHub workflows for new documentation versioning [#627](https://github.com/askap-vast/vast-pipeline/pull/627). +- Bumped Jinja2 to 3.0.3 to fix a Markupsafe error caused by a removed function [#634](https://github.com/askap-vast/vast-pipeline/pull/634). +- Dependancies updated using npm audit fix (non-breaking) [#620](https://github.com/askap-vast/vast-pipeline/pull/620). +- Refactored adding source to favourites button to use ajax to avoid page reload [#614](https://github.com/askap-vast/vast-pipeline/pull/614). +- Bumped test python versions to 3.7.12, 3.8.12 and 3.9.10 [#586](https://github.com/askap-vast/vast-pipeline/pull/586). +- Bumped various dependencies using a fresh poetry.lock file [#586](https://github.com/askap-vast/vast-pipeline/pull/586). +- Bumped bokeh packages to 2.3.3 [#586](https://github.com/askap-vast/vast-pipeline/pull/586). +- Django-Q config variable `max_attempts` is configurable in the .env file [#595](https://github.com/askap-vast/vast-pipeline/pull/595). +- Replaced `models.MeasurementPair` model with a dataclass [#590](https://github.com/askap-vast/vast-pipeline/pull/590). +- Django-Q config variables `timeout` and `retry` are configurable in the .env file [#589](https://github.com/askap-vast/vast-pipeline/pull/589). +- Changed restore run command to only allow one run as input [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Changed existing documentation pages to reflect new buttons [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Moved creation of output backup files to occur before the config check [#576](https://github.com/askap-vast/vast-pipeline/pull/576). +- Association test data updated with d2d fix [#574](https://github.com/askap-vast/vast-pipeline/pull/574). +- Removed the timezone from the Timestamps being written to the the arrow file as this causes problems with vaex [#571](https://github.com/askap-vast/vast-pipeline/pull/571). +- Reduced the memory footprint for computing the ideal source coverages by sky regions [#555](https://github.com/askap-vast/vast-pipeline/pull/555). +- Gulp will only read `webinterface/.env` if the required vars are undefined in the current environment [#548](https://github.com/askap-vast/vast-pipeline/pull/548). + +#### Fixed + +- Fixed handling of NaNs and negatives in noise image statistics [#755](https://github.com/askap-vast/vast-pipeline/pull/755) +- Optimise YAML config parsing [#754](https://github.com/askap-vast/vast-pipeline/pull/754) +- Fixed mkdocs serving issues [#728](https://github.com/askap-vast/vast-pipeline/pull/728) +- Fixed python 3.9 testing failing on github actions [#728](https://github.com/askap-vast/vast-pipeline/pull/728) +- Updated github action syntax to correctly call docker compose [#736](https://github.com/askap-vast/vast-pipeline/pull/736) +- Fix memory leak in new_sources merge [#730](https://github.com/askap-vast/vast-pipeline/pull/730) +- Fix memory leak in model upload [#726](https://github.com/askap-vast/vast-pipeline/pull/726) +- Implemented sensible dask dataframe partitioning [#724](https://github.com/askap-vast/vast-pipeline/pull/724) +- Fixed outdated `runpipeline` section on CLI docs page [#685](https://github.com/askap-vast/vast-pipeline/pull/685). +- Fixed link to JupyterHub [#676](https://github.com/askap-vast/vast-pipeline/pull/676). +- Ensure Image models are not created if the catalogue ingest fails [#648](https://github.com/askap-vast/vast-pipeline/pull/648). +- Fixed run failures caused by attempting to force fit images with empty catalogues [#653](https://github.com/askap-vast/vast-pipeline/pull/653). +- Fixed a Bokeh regression that requires LabelSet values to be strings [#652](https://github.com/askap-vast/vast-pipeline/pull/652). +- Fixed deprecation warning on astroquery Ned import [#644](https://github.com/askap-vast/vast-pipeline/pull/644). +- Fixed a regression from pandas==1.4.0 that caused empty groups to be passed to an apply function in parallel association [#642](https://github.com/askap-vast/vast-pipeline/pull/642). +- Fixed docs issue that stopped serializers and views being shown in the code reference [#627](https://github.com/askap-vast/vast-pipeline/pull/627). +- Fixed broken links to external search results from NED by URI encoding source names [#633](https://github.com/askap-vast/vast-pipeline/pull/633). +- Fixed a regression from pandas==1.4.0 that caused empty groups to be passed to an apply function [#632](https://github.com/askap-vast/vast-pipeline/pull/632). +- Fixed source names to be IAU compliant [#618](https://github.com/askap-vast/vast-pipeline/pull/618). +- Fixed broken NED links for coordinates with many decimal places [#623](https://github.com/askap-vast/vast-pipeline/pull/623). +- Added an error handler for the external source queries (e.g. SIMBAD) [#616](https://github.com/askap-vast/vast-pipeline/pull/616). +- Stopped JS9 from changing the page titles [#597](https://github.com/askap-vast/vast-pipeline/pull/597). +- Fixed regression issues with pandas 1.4 [#586](https://github.com/askap-vast/vast-pipeline/pull/586). +- Fixed config being copied before run was confirmed to actually go ahead for existing runs [#595](https://github.com/askap-vast/vast-pipeline/pull/595). +- Fixed forced measurements being removed from associations during the restore run process [#600](https://github.com/askap-vast/vast-pipeline/pull/600). +- Fixed measurement FITS cutout bug [#588](https://github.com/askap-vast/vast-pipeline/pull/588). +- Fixed removal of image and sky region objects when a run is deleted [#585](https://github.com/askap-vast/vast-pipeline/pull/585). +- Fixed testing pandas equal deprecation warning [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Fixed restore run relations issue [#580](https://github.com/askap-vast/vast-pipeline/pull/580). +- Fixed logic for full re-run requirement when UI run is being re-run from an error status [#576](https://github.com/askap-vast/vast-pipeline/pull/576). +- Fixed d2d not being carried through the advanced association process [#574](https://github.com/askap-vast/vast-pipeline/pull/574). +- Fixed old dictionary references in the documentation run config page [#572](https://github.com/askap-vast/vast-pipeline/pull/572). +- Fixed a regression from pandas=1.3.0 that caused non-numeric columns to be dropped after a groupby sum operation [#567](https://github.com/askap-vast/vast-pipeline/pull/567). +- Fixed permission error for regular users when trying to launch an initialised run [#563](https://github.com/askap-vast/vast-pipeline/pull/563). +- Fixed outdated installation link in README [#543](https://github.com/askap-vast/vast-pipeline/pull/543). + +#### Removed + +- Removed sky coverage plot from homepage [#676](https://github.com/askap-vast/vast-pipeline/pull/676). +- Removed various counts from homepage [#676](https://github.com/askap-vast/vast-pipeline/pull/676). +- Removed support for Python 3.7 [#641](https://github.com/askap-vast/vast-pipeline/pull/641). +- Removed Measurements table page and measurements table from run detail page [#636](https://github.com/askap-vast/vast-pipeline/pull/636). +- Removed the unique constraint on `models.Measurement.name` [#583](https://github.com/askap-vast/vast-pipeline/pull/583). + +#### List of PRs + +- [#755](https://github.com/askap-vast/vast-pipeline/pull/755): fix: Fixed handling of NaNs and negatives in noise image statistics +- [#754](https://github.com/askap-vast/vast-pipeline/pull/754): fix: Optimise YAML config parsing +- [#725](https://github.com/askap-vast/vast-pipeline/pull/725): feat: Added further memory usage and timing debug logging +- [#740](https://github.com/askap-vast/vast-pipeline/pull/740): feat: Add support for python 3.10 +- [#728](https://github.com/askap-vast/vast-pipeline/pull/728): fix: Adjust package versions and fix mkdocs serve issues +- [#728](https://github.com/askap-vast/vast-pipeline/pull/728): fix: Adjust package versions and fix python 3.9 tests breaking on github actions +- [#736](https://github.com/askap-vast/vast-pipeline/pull/736): fix: Updated github action syntax to correctly call docker compose +- [#730](https://github.com/askap-vast/vast-pipeline/pull/730): fix: Fix memory leak in new_sources merge +- [#726](https://github.com/askap-vast/vast-pipeline/pull/726): fix: Fix memory leak in model upload and use chunks for associations upload +- [#724](https://github.com/askap-vast/vast-pipeline/pull/724): fix, feat: Implemented sensible dask dataframe partitioning +- [#694](https://github.com/askap-vast/vast-pipeline/pull/694): feat: Handle compressed fits files. +- [#702](https://github.com/askap-vast/vast-pipeline/pull/702): fix: Downgrade ci-docs to python 3.8. +- [#701](https://github.com/askap-vast/vast-pipeline/pull/701): fix: Update Gr1N poetry to v8, force python 3.8.10. +- [#699](https://github.com/askap-vast/vast-pipeline/pull/699): docs, feat: Add new regression data download URL and updates to Github Actions. +- [#697](https://github.com/askap-vast/vast-pipeline/pull/697/): feat: Added links to Data Central DAS and the Fink Broker to the source page. +- [#685](https://github.com/askap-vast/vast-pipeline/pull/685): docs: Updated `runpipeline` section on CLI docs. +- [#676](https://github.com/askap-vast/vast-pipeline/pull/676): Removed home counts and new source count. +- [#665](https://github.com/askap-vast/vast-pipeline/pull/665): Update Gr1N/setup-poetry to v7. +- [#658](https://github.com/askap-vast/vast-pipeline/pull/658): feat: Add MAX_CUTOUT_IMAGES setting. +- [#655](https://github.com/askap-vast/vast-pipeline/pull/655): feat: Add run config option to disable measurement pairs. +- [#648](https://github.com/askap-vast/vast-pipeline/pull/648): fix: make Image and Measurement creation atomic together. +- [#653](https://github.com/askap-vast/vast-pipeline/pull/653): fix: Allow forced fitting on images with empty catalogues. +- [#652](https://github.com/askap-vast/vast-pipeline/pull/652): dep, fix: Bump bokeh 2.4.2. +- [#644](https://github.com/askap-vast/vast-pipeline/pull/644): fix: Fix astroquery Ned import deprecation. +- [#638](https://github.com/askap-vast/vast-pipeline/pull/638): feat: Support defer loading of dataTables data. +- [#641](https://github.com/askap-vast/vast-pipeline/pull/641): dep: Drop Python 3.7 and dependency refresh. +- [#643](https://github.com/askap-vast/vast-pipeline/pull/643): fix: Addressed pandas DataFrame.append deprecation. +- [#642](https://github.com/askap-vast/vast-pipeline/pull/642): fix: Fix empty groups in parallel association. +- [#636](https://github.com/askap-vast/vast-pipeline/pull/636): fix, doc: Remove excessive measurement tables. +- [#627](https://github.com/askap-vast/vast-pipeline/pull/627): dep, docs: Documentation update and versioning. +- [#633](https://github.com/askap-vast/vast-pipeline/pull/633): fix: URI encode NED object names. +- [#632](https://github.com/askap-vast/vast-pipeline/pull/632): fix: skip empty groups in new sources groupby-apply. +- [#634](https://github.com/askap-vast/vast-pipeline/pull/634): dep: bump Jinja2 to v3. +- [#626](https://github.com/askap-vast/vast-pipeline/pull/626): feat: Add a ZTF cone search link. +- [#618](https://github.com/askap-vast/vast-pipeline/pull/618): fix: Produce IAU compliant source names. +- [#625](https://github.com/askap-vast/vast-pipeline/pull/625): feat: Pair metrics hover tooltip. +- [#620](https://github.com/askap-vast/vast-pipeline/pull/620): dep: Non-breaking npm audit fix update. +- [#622](https://github.com/askap-vast/vast-pipeline/pull/622): feat: Updated Aladin VAST surveys. +- [#623](https://github.com/askap-vast/vast-pipeline/pull/623): fix: fixed NED links. +- [#616](https://github.com/askap-vast/vast-pipeline/pull/616): fix: added error handling to external queries. +- [#614](https://github.com/askap-vast/vast-pipeline/pull/614): feat: Refactored add to favourites button to avoid refresh. +- [#597](https://github.com/askap-vast/vast-pipeline/pull/597): fix: Update detail page titles. +- [#586](https://github.com/askap-vast/vast-pipeline/pull/586): feat, dep, doc: Add an eta-v analysis page for the source query. +- [#595](https://github.com/askap-vast/vast-pipeline/pull/595): fix: Add date and time stamp to log files. +- [#600](https://github.com/askap-vast/vast-pipeline/pull/600): fix: Fixed restore run forced measurements associations. +- [#590](https://github.com/askap-vast/vast-pipeline/pull/590): fix: Remove MeasurementPair model. +- [#589](https://github.com/askap-vast/vast-pipeline/pull/589): fix: expose django-q timeout and retry to env vars. +- [#588](https://github.com/askap-vast/vast-pipeline/pull/588): fix: change cutout endpoint to use measurement ID. +- [#585](https://github.com/askap-vast/vast-pipeline/pull/585): fix: Clean up m2m related objects when deleting a run. +- [#583](https://github.com/askap-vast/vast-pipeline/pull/583): fix: remove unique constraint from Measurement.name. +- [#580](https://github.com/askap-vast/vast-pipeline/pull/580): feat, fix, doc: Added UI run action buttons. +- [#576](https://github.com/askap-vast/vast-pipeline/pull/576): fix: Fixed UI re-run from errored status. +- [#574](https://github.com/askap-vast/vast-pipeline/pull/574): fix: Fixed d2d assignment in advanced association. +- [#572](https://github.com/askap-vast/vast-pipeline/pull/572): doc: Added required data page to documentation. +- [#571](https://github.com/askap-vast/vast-pipeline/pull/571): fix: Removed timezone from measurements arrow file time column +- [#565](https://github.com/askap-vast/vast-pipeline/pull/565): feat: added support for reading selavy VOTables and CSVs. +- [#567](https://github.com/askap-vast/vast-pipeline/pull/567): fix: fixed pandas=1.3.0 groupby sum regression. +- [#563](https://github.com/askap-vast/vast-pipeline/pull/563): fix: fixed launch run user permission bug. +- [#544](https://github.com/askap-vast/vast-pipeline/pull/544): feat: new command to ingest images without running the full pipeline. +- [#557](https://github.com/askap-vast/vast-pipeline/pull/557): feat: Add TNS external search for sources. +- [#559](https://github.com/askap-vast/vast-pipeline/pull/559): feat: added HOME_DATA_ROOT setting. +- [#555](https://github.com/askap-vast/vast-pipeline/pull/555): fix: compute ideal source coverage with astropy xmatch. +- [#551](https://github.com/askap-vast/vast-pipeline/pull/551): feat: added processing spinner to source query table. +- [#550](https://github.com/askap-vast/vast-pipeline/pull/550): fix: missing changelog entry +- [#548](https://github.com/askap-vast/vast-pipeline/pull/548): fix: only read .env if required vars are undefined. +- [#546](https://github.com/askap-vast/vast-pipeline/pull/546): feat, fix: remove unused imports, and added basic linter during CI/CD. +- [#543](https://github.com/askap-vast/vast-pipeline/pull/543): fix, doc: Fix README link and documentation 404 assets. + ## [1.0.0](https://github.com/askap-vast/vast-pipeline/releases/v1.0.0) (2021-05-21) #### Added diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5f001af50..33c78bb99 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,3 +1,3 @@ # VAST Pipeline - Developing Notes -Please refer to the site documentation [contributing and developing guidelines](https://vast-survey.org/vast-pipeline/developing/intro/). +Please refer to the site documentation [contributing and developing guidelines](https://vast-survey.org/vast-pipeline/v1.0.0/developing/intro/). diff --git a/README.md b/README.md index 8e366ef63..1a6019fa9 100644 --- a/README.md +++ b/README.md @@ -2,20 +2,21 @@ [![astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org/) -[![Nimbus](https://img.shields.io/website?down_color=red&down_message=offline&label=Nimbus&up_color=green&up_message=online&url=https%3A%2F%2Fdata.vast-survey.org%2Fpipeline%2F)](https://data.vast-survey.org/pipeline/) +[![Nimbus](https://img.shields.io/website?down_color=red&down_message=offline&label=Nimbus&up_color=green&up_message=online&url=https%3A%2F%2Fdev.pipeline.vast-survey.org)](https://dev.pipeline.vast-survey.org) ![Tests](https://github.com/askap-vast/vast-pipeline/workflows/test-suite/badge.svg) +[![documentation](https://img.shields.io/badge/docs-mkdocs%20material-blue.svg?style=flat)](https://vast-survey.org/vast-pipeline/) This repository holds the code of the VAST Pipeline, a radio transient detection pipeline for the ASKAP survey science project, VAST. -Please read the [Installation Instructions](https://vast-survey.org/vast-pipeline/quickstart/installation/). If you have any questions or feedback, we welcome you to open an [issue](https://github.com/askap-vast/vast-pipeline/issues). If you are interested in contributing to the code, please read and follow the [Contributing and Developing Guidelines](https://vast-survey.org/vast-pipeline/developing/intro/). +Please read the [Installation Instructions](https://vast-survey.org/vast-pipeline/v1.0.0/gettingstarted/installation/). If you have any questions or feedback, we welcome you to open an [issue](https://github.com/askap-vast/vast-pipeline/issues). If you are interested in contributing to the code, please read and follow the [Contributing and Developing Guidelines](https://vast-survey.org/vast-pipeline/v1.0.0/developing/intro/). If using this tool in your research, please cite Murphy, et al. (2021). ## Features -* Code base in `Python 3.7+` +* Code base in `Python 3.8+` * Source association/manipulations using `Astropy4+` and `Pandas1+` dataframe * Association methods: basic (`Astropy` crossmatch), advanced (search with a fixed distance), De Ruiter * Flagging of "New Source" and "Related Source" @@ -50,4 +51,6 @@ The VAST Pipeline development was supported by: * Software support resources awarded under the Astronomy Data and Computing Services (ADACS) Merit Allocation Program. ADACS is funded from the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government and managed by Astronomy Australia Limited (AAL). * NSF grant AST-1816492. +We also acknowledge the [LOFAR Transients Pipeline (TraP)](https://ascl.net/1412.011) ([Swinbank, et al. 2015](https://ui.adsabs.harvard.edu/abs/2015A%26C....11...25S/abstract)) from which various concepts and design choices have been implemented in the VAST Pipeline. + The developers thank the creators of [SB Admin 2](https://github.com/StartBootstrap/startbootstrap-sb-admin-2) to make the dashboard template freely available. diff --git a/docs/adminusage/cli.md b/docs/adminusage/cli.md index 14d7143dc..cda67ca2c 100644 --- a/docs/adminusage/cli.md +++ b/docs/adminusage/cli.md @@ -19,6 +19,8 @@ Output: clearpiperun createmeasarrow debugrun + ingestimages + initingest initpiperun restorepiperun runpipeline @@ -26,7 +28,7 @@ Output: ... ``` -There are 6 commands, described in detail below. +There are 8 commands, described in detail below. ### clearpiperun @@ -85,6 +87,9 @@ Example usage: This command allows for the creation of the `measurements.arrow` and `measurement_pairs.arrow` files after a run has been successfully completed. See [Arrow Files](../outputs/outputs.md#arrow-files) for more information. +!!!info + The `measurement_pairs.arrow` file will only be created if the run was configured to calculate pair metrics. + ```terminal ./manage.py createmeasarrow --help usage: manage.py createmeasarrow [-h] [--overwrite] [--version] [-v {0,1,2,3}] @@ -180,6 +185,127 @@ Nr of association: 3276 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ``` +### ingestimages + +This command runs the first part of the pipeline only. +It ingests/adds a set of images, and their measurements, to the database. +It requires an image ingestion configuration file as input. +A template ingest configuration file can be generated with the [`initingest`](#initingest) command (below). + +```terminal +(pipeline_env)$ ./manage.py ingestimages --help +``` + +Output: + +```terminal +usage: manage.py ingestimages [-h] [--version] [-v {0,1,2,3}] + [--settings SETTINGS] + [--pythonpath PYTHONPATH] [--traceback] + [--no-color] [--force-color] + [--skip-checks] + image_ingest_config + +Ingest/add a set of images to the database + +positional arguments: + image_ingest_config Image ingestion configuration filename/path. + +optional arguments: + -h, --help show this help message and exit + --version show program's version number and exit + -v {0,1,2,3}, --verbosity {0,1,2,3} + Verbosity level; 0=minimal output, 1=normal + output, 2=verbose output, 3=very verbose output + --settings SETTINGS The Python path to a settings module, e.g. + "myproject.settings.main". If this isn't + provided, the DJANGO_SETTINGS_MODULE environment + variable will be used. + --pythonpath PYTHONPATH + A directory to add to the Python path, e.g. + "/home/djangoprojects/myproject". + --traceback Raise on CommandError exceptions + --no-color Don't colorize the command output. + --force-color Force colorization of the command output. + --skip-checks Skip system checks. +``` + +General usage: + +```terminal +(pipeline_env)$ ./manage.py ingestimages ./ingest_config.yml +``` + +Output: +```terminal +2021-06-25 03:08:44,313 loading INFO Reading image epoch01.fits ... +2021-06-25 03:08:44,348 utils INFO Adding new frequency band: 888 +2021-06-25 03:08:44,390 utils INFO Created sky region 150.001, -30.001 +2021-06-25 03:08:44,441 loading INFO Processed measurements dataframe of shape: (4, 40) +2021-06-25 03:08:44,452 loading INFO Bulk created #4 Measurement +2021-06-25 03:08:44,504 loading INFO Reading image epoch02.fits ... +... +2021-06-25 03:08:44,731 loading INFO Reading image epoch04.fits ... +2021-06-25 03:08:44,771 utils INFO Created sky region 150.021, -30.017 +2021-06-25 03:08:44,805 loading INFO Processed measurements dataframe of shape: (5, 40) +2021-06-25 03:08:44,810 loading INFO Bulk created #5 Measurement +2021-06-25 03:08:44,819 loading INFO Total images upload/loading time: 0.97 seconds +``` + +### initingest + +This command generates a template configuration file for use with the [`ingestimages`](#ingestimages) command. + +```terminal +(pipeline_env)$ ./manage.py initingest --help +``` + +Output: + +```terminal +usage: manage.py initingest [-h] [--version] [-v {0,1,2,3}] + [--settings SETTINGS] + [--pythonpath PYTHONPATH] [--traceback] + [--no-color] [--force-color] [--skip-checks] + config_file_name + +Create a template image ingestion configuration file + +positional arguments: + config_file_name Filename to write template ingest configuration to. + +optional arguments: + -h, --help show this help message and exit + --version show program's version number and exit + -v {0,1,2,3}, --verbosity {0,1,2,3} + Verbosity level; 0=minimal output, 1=normal + output, 2=verbose output, 3=very verbose output + --settings SETTINGS The Python path to a settings module, e.g. + "myproject.settings.main". If this isn't + provided, the DJANGO_SETTINGS_MODULE environment + variable will be used. + --pythonpath PYTHONPATH + A directory to add to the Python path, e.g. + "/home/djangoprojects/myproject". + --traceback Raise on CommandError exceptions + --no-color Don't colorize the command output. + --force-color Force colorization of the command output. + --skip-checks Skip system checks. +``` + +General usage: + +```terminal +(pipeline_env)$ ./manage.py initingest ingest_config.yml +``` + +Output: +```terminal +Writing template to: ingest_config.yml +``` + +Then modify `ingest_config.yml` to suit your needs. + ### initpiperun In order to process the images in the pipeline, you must create/initialise a pipeline run first. @@ -378,6 +504,14 @@ Would you like to restore the run ? (y/n): y The pipeline is run using `runpipeline` django command. +The `--full-rerun` option allows for the requested pipeline run to be cleared prior to processing +so a fresh run is performed. + +!!!warning + Using `--full-rerun` cannot be undone and all prior results will be deleted, including any source comments + associated with the pipeline run. + Use with caution. + ```terminal (pipeline_env)$ ./manage.py runpipeline --help ``` @@ -385,27 +519,27 @@ The pipeline is run using `runpipeline` django command. Output: ```terminal -usage: manage.py runpipeline [-h] [--version] [-v {0,1,2,3}] +usage: manage.py runpipeline [-h] [--full-rerun] [--version] [-v {0,1,2,3}] [--settings SETTINGS] [--pythonpath PYTHONPATH] - [--traceback] [--no-color] [--force-color] - [--skip-checks] + [--traceback] [--no-color] [--force-color] [--skip-checks] piperun Process the pipeline for a list of images and Selavy catalogs positional arguments: - piperun Path or name of the pipeline run. + piperun Path or name of the pipeline run. optional arguments: -h, --help show this help message and exit + --full-rerun Flag to signify that a full re-run is requested. Old data is + completely removed and replaced. --version show program's version number and exit -v {0,1,2,3}, --verbosity {0,1,2,3} - Verbosity level; 0=minimal output, 1=normal output, - 2=verbose output, 3=very verbose output + Verbosity level; 0=minimal output, 1=normal output, 2=verbose + output, 3=very verbose output --settings SETTINGS The Python path to a settings module, e.g. "myproject.settings.main". If this isn't provided, the - DJANGO_SETTINGS_MODULE environment variable will be - used. + DJANGO_SETTINGS_MODULE environment variable will be used. --pythonpath PYTHONPATH A directory to add to the Python path, e.g. "/home/djangoprojects/myproject". diff --git a/docs/adminusage/ingestimages.md b/docs/adminusage/ingestimages.md new file mode 100644 index 000000000..6273b5535 --- /dev/null +++ b/docs/adminusage/ingestimages.md @@ -0,0 +1,66 @@ +# Standalone Image Ingestion + +Administrators have the added option of ingesting new images via the command line, without running the full pipeline. + +!!! note + Currently, when non-admin users run the pipeline, any images listed in the configuration that do not exist in the database will be processed and uploaded to the database. Hence, it is not a requirement to ingest images using this process before a run is processed that uses new images. + +The relevant command is [`ingestimages`](../cli/#ingestimages), which requires and ingest configuration file as input. +The configuration file must contain the images to be listed, as well as a subset of options from the main [run configuration](../../using/runconfig) file. + +In particular, along with the images to be ingested, the configuration requires the following options: + +**`measurements.condon_errors`** +Boolean. Calculate the Condon errors of the extractions when read in from the source extraction file. If `False` then the errors directly from the source finder output are used. Recommended to set to `True` for selavy extractions. Defaults to `True`. + +**`measurements.selavy_local_rms_fill_value`** +Float. Value to substitute for the `local_rms` parameter in selavy extractions if a `0.0` value is found. Unit is mJy. Defaults to `0.2`. + +**`measurements.ra_uncertainty`** +Float. Defines an uncertainty error to the RA that will be added in quadrature to the existing source extraction error. Used to represent a systematic positional error. Unit is arcseconds. Defaults to 1.0. + +**`measurements.dec_uncertainty`** +Float. Defines an uncertainty error to the Dec that will be added in quadrature to the existing source extraction error. Used to represent systematic positional error. Unit is arcseconds. Defaults to 1.0. + +!!! example "ingest_config.yaml" + ```yaml + # This file is used for ingest only mode + # It specifies the images to be processed and the relevant + # settings to be used. + + inputs: + # NOTE: all the inputs must match with each other, i.e. the catalogue for the first + # input image (inputs.image[0]) must be the first input catalogue (inputs.selavy[0]) + # and so on. + image: + # list input images here, e.g. (note the leading hyphens) + glob: ./data/epoch0?.fits + + selavy: + # list input selavy catalogues here, as above with the images + glob: ./data/epoch0?.selavy.components.txt + + noise: + # list input noise (rms) images here, as above with the images + glob: ./data/epoch0?.noiseMap.fits + + # Required only if source_monitoring.monitor is true, otherwise optional. If not providing + # background images, remove the entire background section below. + # background: + # list input background images here, as above with the images + + measurements: + # Replace the selavy errors with Condon (1997) errors. + condon_errors: True + + # Sometimes the local rms for a source is reported as 0 by selavy. + # Choose a value to use for the local rms in these cases in mJy/beam. + selavy_local_rms_fill_value: 0.2 + + # The positional uncertainty of a measurement is in reality the fitting errors and the + # astrometric uncertainty of the image/survey/instrument combined in quadrature. + # These two parameters are the astrometric uncertainty in RA/Dec and they may be different. + ra_uncertainty: 1 # arcsec + dec_uncertainty: 1 # arcsec + + ``` diff --git a/docs/architecture/intro.md b/docs/architecture/intro.md index 76c4fe9e0..55abd209d 100644 --- a/docs/architecture/intro.md +++ b/docs/architecture/intro.md @@ -31,11 +31,10 @@ The front-end is built in simple HTML, CSS and Javascript using a freely availab * [Django Crispy Forms](https://django-crispy-forms.readthedocs.io/en/latest/index.html){:target="_blank"} * [Django Tagulous](https://github.com/radiac/django-tagulous){:target="_blank"} * [Pandas 1+](https://pandas.pydata.org/){:target="_blank"} -* [Python 3.7+](https://www.python.org/){:target="_blank"} +* [Python 3.8+](https://www.python.org/){:target="_blank"} * [Pyarrow 0.17+](https://arrow.apache.org/docs/python/install.html){:target="_blank"} * [Postgres 10+](https://www.postgresql.org/){:target="_blank"} * [Q3C](https://github.com/segasai/q3c){:target="_blank"} -* [Vaex 3+](https://vaex.readthedocs.io/en/latest/){:target="_blank"} ### Front-End diff --git a/docs/design/imageingest.md b/docs/design/imageingest.md index 5b7af5be9..f6219c626 100644 --- a/docs/design/imageingest.md +++ b/docs/design/imageingest.md @@ -8,7 +8,7 @@ The portion of a pipeline log file below shows the messages for the ingestion of !!! note Once an image is uploaded then that image is available for all other runs to use without having to re-upload. -!!! example "log.txt" +!!! example "2021-03-11-12-48-21_log.txt" ```console 2021-03-11 12:59:49,751 loading INFO Reading image VAST_0127-73A.EPOCH01.I.cutout.fits ... 2021-03-11 12:59:49,756 utils INFO Adding new frequency band: 887 diff --git a/docs/design/newsources.md b/docs/design/newsources.md index 6748e5367..78ac95910 100644 --- a/docs/design/newsources.md +++ b/docs/design/newsources.md @@ -43,4 +43,4 @@ The `new source high sigma` is then equal to the maximum $\text{rms}_{location,i ## Viewing New Sources -New sources are marked as new on the website interface (see [Source Pages](../exploringwebsite/sourcepages.md)) and in the source parquet file output there is a boolean column named `new`. +New sources are marked as new on the website interface (see [Source Pages](../exploringwebsite/sourcedetail.md)) and in the source parquet file output there is a boolean column named `new`. diff --git a/docs/design/overview.md b/docs/design/overview.md index 7dce57fa4..39c24d73e 100644 --- a/docs/design/overview.md +++ b/docs/design/overview.md @@ -54,18 +54,15 @@ All the results from the pipeline run are uploaded to the database. Specifically * Sources and their statistics. * Relations between sources. * Associations. -* Measurement two-epoch pairs. -For large runs this can be a substantional component of the pipeline run time. +For large runs this can be a substantial component of the pipeline run time. `Bulk upload` statements will be seen in the pipeline run log file such as these shown below: -!!! example "log.txt" +!!! example "2021-03-11-12-48-21_log.txt" ```console 2021-03-11 13:00:04,893 loading INFO Bulk created #557 Source 2021-03-11 13:00:04,910 loading INFO Populate "related" field of sources... 2021-03-11 13:00:04,919 loading INFO Bulk created #29 RelatedSource 2021-03-11 13:00:04,943 loading INFO Upload associations... 2021-03-11 13:00:05,650 loading INFO Bulk created #3276 Association - 2021-03-11 13:00:08,374 loading INFO Bulk created #10000 MeasurementPair - 2021-03-11 13:00:08,663 loading INFO Bulk created #1150 MeasurementPair ``` diff --git a/docs/design/sourcestats.md b/docs/design/sourcestats.md index e35fbd977..dfbcafe0b 100644 --- a/docs/design/sourcestats.md +++ b/docs/design/sourcestats.md @@ -70,8 +70,11 @@ where $w$ is the uncertainty ($e$) in $I$ of a measurement, and is given by $w=\ ### Two-Epoch Metrics -Alternative variability metrics, $V_s$ and $m$, are also calculated which we refer to as the 'two-epoch metrics'. -They are calculated for each unique pair of measurements assoicated with the source, with the most significant pair of values attached to the source (see section below). Please refer to [Mooley et al. (2016)](https://ui.adsabs.harvard.edu/abs/2016ApJ...818..105M/abstract){:target="_blank"} for further details. +By default, alternative variability metrics, $V_s$ and $m$, are also calculated which we refer to as the 'two-epoch metrics'. +They are calculated for each unique pair of measurements associated with the source, with the most significant pair of values attached to the source (see section below). Please refer to [Mooley et al. (2016)](https://ui.adsabs.harvard.edu/abs/2016ApJ...818..105M/abstract){:target="_blank"} for further details. + +!!!warning + The number of measurement pairs scales exponentially with the number of measurements per source (i.e. the number of images). Runs that contain a large number of input images per source may run out memory while calculating the two-epoch metrics. If this occurs, it is recommended that the pair metric calculation is turned off in the run configuration by setting `variability.pair_metrics: False` (see [variability run configuration options](../using/runconfig.md#variability)). !!! note All the two-epoch pair $V_s$ and $m$ values for a run are saved in the output file `measurement_pairs.parquet` for offline analysis. @@ -104,8 +107,8 @@ which equates to a variability of 30%. However the user is free to set their own #### Significant Source Values -The $V_s$ and $m$ metrics of the 'maximum signficant pair' is attached to the source. -The maximum significant pair is determind by selecting the most significant $\mid m \mid$ value given a minimum $V_s$ threshold which is defined in the pipeline configuration file `variability.source_aggregate_pair_metrics_min_abs_vs`: +The $V_s$ and $m$ metrics of the 'maximum significant pair' is attached to the source. +The maximum significant pair is determined by selecting the most significant $\mid m \mid$ value given a minimum $V_s$ threshold which is defined in the pipeline configuration file `variability.source_aggregate_pair_metrics_min_abs_vs`: !!! example "config.yaml" ```yaml @@ -115,7 +118,7 @@ The maximum significant pair is determind by selecting the most significant $\mi source_aggregate_pair_metrics_min_abs_vs: 4.3 ``` -By default this value is set to 4.3. For example, if a source with three associatied measurements gave the following pair metrics: +By default this value is set to 4.3. For example, if a source with three associated measurements gave the following pair metrics: | Pair | $\mid V_s \mid$ | $\mid m \mid$ | | ---- | --------------- | ------------- | diff --git a/docs/developing/docsdev.md b/docs/developing/docsdev.md index 81eda98a8..da7a362a4 100644 --- a/docs/developing/docsdev.md +++ b/docs/developing/docsdev.md @@ -211,6 +211,14 @@ For example, using the `vast_pipeline` as the base for the python files and `ref Please refer to the [`mkdocstrings` documentation ](https://mkdocstrings.github.io){:target="_blank"} for full details of the options available to declare in `mkdocs.yml`. +## Versioning + +Versioning is enabled on the documentation by using the [`mike`](https://github.com/jimporter/mike){:target="_blank"} package. +The documentation is published for each release version, along with a development version that is updated with every commit to the default `dev` branch. +Setup of this has been completed on the initial deployment, and the GitHub workflows will automatically take care of the continual deployment. +Vist the `mike` package page linked above for details on general management commands such as how to delete a version. + ## Deployment to GitHub pages -Automatic deployment to GitHub pages is set up using GitHub actions and workflows. See source code under the `.github` folder. +Automatic deployment to GitHub pages is set up using GitHub actions and workflows. +See the workflows `ci-docs-dev.yml` and `ci-docs-release.yml`. diff --git a/docs/developing/tests.md b/docs/developing/tests.md index 0a9236978..d28c74804 100644 --- a/docs/developing/tests.md +++ b/docs/developing/tests.md @@ -34,7 +34,7 @@ to run the tests located in [`test_webserver.py`](https://github.com/askap-vast/ ## Regression Tests -Regression tests located in [`test_regression`](https://github.com/askap-vast/vast-pipeline/blob/master/vast_pipeline/tests/test_regression/){:target="\_blank"} require the use of the _VAST\_2118-06A_ field test dataset which is not a part of the repository. This data is downloadable from [cloudstor](https://cloudstor.aarnet.edu.au/plus/s/xjh0aRr1EGY6Bt3){:target="_blank"}. You can use the script located in [tests/regression-data/](https://github.com/askap-vast/vast-pipeline/blob/master/vast_pipeline/tests/regression-data/){:target="_blank"}: +Regression tests located in [`test_regression`](https://github.com/askap-vast/vast-pipeline/blob/master/vast_pipeline/tests/test_regression/){:target="\_blank"} require the use of the _VAST\_2118-06A_ field test dataset which is not a part of the repository. This data is downloadable from [the USyd machines](http://www.physics.usyd.edu.au/~ddob1600/vast-pipeline/pipeline-test-data.zip){:target="_blank"}. You can use the script located in [tests/regression-data/](https://github.com/askap-vast/vast-pipeline/blob/master/vast_pipeline/tests/regression-data/){:target="_blank"}: ```bash cd vast_pipeline/tests/regression-data/ && ./download.sh @@ -42,7 +42,7 @@ cd vast_pipeline/tests/regression-data/ && ./download.sh to download the _VAST\_2118-06A_ field test dataset into the [`regression-data`](https://github.com/askap-vast/vast-pipeline/blob/master/vast_pipeline/tests/regression-data/){:target="_blank"} folder. Or manually by clicking the button below: -[Download data for test :material-cloud-download-outline:](https://cloudstor.aarnet.edu.au/plus/s/xjh0aRr1EGY6Bt3/download){: .md-button target="_blank"} +[Download data for test :material-cloud-download-outline:](http://www.physics.usyd.edu.au/~ddob1600/vast-pipeline/pipeline-test-data.zip){: .md-button target="_blank"} and place the _VAST\_2118-06A_ field test dataset into the [`regression-data`](https://github.com/askap-vast/vast-pipeline/blob/master/vast_pipeline/tests/regression-data/){:target="_blank"} folder. These regression tests are skipped if the dataset is not present. diff --git a/docs/exploringwebsite/measurementpages.md b/docs/exploringwebsite/measurementdetail.md similarity index 73% rename from docs/exploringwebsite/measurementpages.md rename to docs/exploringwebsite/measurementdetail.md index 80a146a78..fee5b0b00 100644 --- a/docs/exploringwebsite/measurementpages.md +++ b/docs/exploringwebsite/measurementdetail.md @@ -1,22 +1,10 @@ -# Measurement Pages - -This page details the website pages for information on the measurements. - -## List of Measurements - -A list of measurements that have been ingested into the pipeline database from **all** pipeline runs, along with their statistics, is shown on this page. -From this page the full detail page of a specific measurement can be accessed by clicking on the name of the measurement. -Explanation of the table options can be found in the [DataTables section](datatables.md). - -![!Pipeline measurements table.](../img/measurements-list.png){: loading=lazy } - -## Measurement Detail Page +# Measurement Detail Page This page presents all the information about the selected measurement, including a postage stamp cutout of the component. ![!Measurement detail page.](../img/measurement-detail1.png){: loading=lazy } -### SIMBAD, NED, Previous & Next Buttons +## SIMBAD, NED, Previous & Next Buttons These buttons do the following: @@ -25,11 +13,18 @@ These buttons do the following: * **Previous**: Navigates to the previous measurement by `id` value. * **Next**: Navigates to the next measurement by `id` value. -### Details +## Details A text representation of details of the measurement. -### JS9 Viewer +## Aladin Lite Viewer + +[Aladin Lite Documentation](https://aladin.u-strasbg.fr/AladinLite/doc/){ target=_blank }. + +The central panel contains an Aladin Lite viewer, which by default displays the HIPS image from the [Rapid ASKAP Continuum Survey](https://research.csiro.au/racs/){ target=_blank }, centred on the location of the measurement. +Other surveys are available such as all epochs of the VAST Pilot Survey (including Stokes V) and other wavelength surveys such as 2MASS. + +## JS9 Viewer [JS9 website](https://js9.si.edu){ target=_blank }. @@ -38,26 +33,19 @@ The right panel contains a JS9 viewer showing the postage stamp FITS image of th !!! note If the image data is removed from its location when the pipeline run was processed the JS9 viewer will no longer work. -### Aladin Lite Viewer - -[Aladin Lite Documentation](https://aladin.u-strasbg.fr/AladinLite/doc/){ target=_blank }. - -The central panel contains an Aladin Lite viewer, which by default displays the HIPS image from the [Rapid ASKAP Continuum Survey](https://research.csiro.au/racs/){ target=_blank }, centred on the location of the measurement. -Other surveys are available such as all epochs of the VAST Pilot Survey (including Stokes V) and other wavelength surveys such as 2MASS. - -### User Comments +## User Comments Users are able to read and post comments on a measurement using this form. ![!Measurement detail page: user comments.](../img/measurement-detail2.png){: loading=lazy } -### Sources Table +## Sources Table The sources table shows all the sources, from all pipeline runs, that the measurement is associated to. Explanation of the table options can be found on the overview page [here](websiteoverview.md#data-tables). ![!Measurement detail page: source and siblings tables.](../img/measurement-detail3.png){: loading=lazy } -### Siblings Table +## Siblings Table The siblings table displays all other measurements that are a sibling of the current measurement, i.e., the measurements belong to the same island (as determined by `Selavy`). diff --git a/docs/exploringwebsite/runpages.md b/docs/exploringwebsite/runpages.md index 3d01efc55..dad284029 100644 --- a/docs/exploringwebsite/runpages.md +++ b/docs/exploringwebsite/runpages.md @@ -10,15 +10,41 @@ Explanation of the table options can be found in the [DataTables section](datata ![!Pipeline Runs table.](../img/pipeline-runs.png){: loading=lazy } -## Pipeline Run Detail Page +| **Run Status** | **Description** | +| ---------------- | -------------------------------------------------------------------------------- | +| **Completed** | The run has successfully finished processing. | +| **Deleting** | The pipeline run is currently being deleted. | +| **Error** | The run has encountered an error during processing and has stopped. | +| **Initialised** | The run has been created but not yet run. | +| **Queued** | The run has been sent to the scheduler for running but has not started yet. | +| **Restoring** | The pipeline run is currently being restored. | +| **Running** | The run is currently processing. | -This page presents all the information about the pipeline run, including options to edit the configuration file and to schedule the run for processing. +## Pipeline Run Detail Page -!!! note - For full details on how to process a run please refer to [this page](../using/processrun.md). +This page presents all the information about the pipeline run, including options to edit the configuration file and to schedule the run for processing, restore the run, delete the run and generate the arrow measurement files. ![!Pipeline Run detail page.](../img/run-detail1.png){: loading=lazy } +### Action Buttons + +![!Pipeline Run action buttons.](../img/action-buttons.png){: loading=lazy } + +For admins and creators of runs there are four action buttons available: + +* **Generate Arrow Files** + A process to generate the arrow measurement files. + See [Generating Arrow Files](../../using/genarrow). +* **Delete Run** + Delete the pipeline run. + See [Deleting a Run](../../using/deleterun). +* **Restore Run** + A process to restore the run to the previous successful state. + See [Restoring a Run](../../using/restorerun). +* **Add Images or Re-Process Run/Process Run** + Process the pipeline run. + See [Processing a Run](../../using/processrun). + ### Summary Cards The cards at the top of the page give a summary of the total numbers of: @@ -28,7 +54,7 @@ The cards at the top of the page give a summary of the total numbers of: * New sources in the pipeline run. Clicking on the total number of images or measurements will navigate the user to the [Image and Measurements tables](#image-and-measurements-tables) on this page, -where as the source cards will take the user to the [Sources Query](sourcepages.md#source-query-page) page. +where as the source cards will take the user to the [Sources Query](sourcequery.md) page. !!! warning When sent to the source query page, the user should make sure to click submit on the search. @@ -73,11 +99,31 @@ The feedback may take a moment to appear as the check is performed. Users are able to read and post comments on a pipeline run using this form. -### Log File +### Log Files + +There are three log files available, which are present depending on the actions performed. +All logs are timestamped with the run time, and by default the most recent log is shown. +A dropdown menu of available log files to view is available at the right hand side of the header as shown in the examples below. +If there are no logs to show for the respective task then the log window will display `No logs to show` and the dropdown menu will appear empty. + +#### Run Log File + +The full log file of the pipeline run process. ![!Run log file.](../img/run-detail4.png){: loading=lazy } -The full log file of the pipeline run is able to viewed. +#### Restore Log File + +The log file of the restore run action. + +![!Restore log file.](../img/run-detail8.png){: loading=lazy } + +#### Generate Arrow Files Log File + +The log file of the generate arrow files action. + +![!Generate arrow files log file.](../img/run-detail9.png){: loading=lazy } + ### Image and Measurements Tables diff --git a/docs/exploringwebsite/sourceanalysis.md b/docs/exploringwebsite/sourceanalysis.md new file mode 100644 index 000000000..5b96643d8 --- /dev/null +++ b/docs/exploringwebsite/sourceanalysis.md @@ -0,0 +1,75 @@ +# Source η-V Analysis + +This page details the interactive analysis tool of the _η_ and _V_ metrics for a selection of sources. + +!!! info "Further Reading" + Descriptions of the η and V metrics can be found on the [source statistics page](../../design/sourcestats#v-and-metrics). + + For a detailed overview of the method, please refer to [Rowlinson et al., 2019](https://ui.adsabs.harvard.edu/abs/2019A%26C....27..111R/abstract){:target="_blank"}. + +The analysis can be performed on the results of a [source query](sourcequery.md). + +!!! tip "Tip: Sensible Querying!" + To get the most out of the tool is advised to design a query that will eliminate as many erroneous results as possible. + For example, making sure sources are isolated and have no siblings or relations. + The [VAST Tools](https://vast-survey.org/vast-tools/){:target="_blank"} package should be used for more advanced queries. + +## Accessing the Analysis Page + +Once a query has been performed on the [source query page](sourcequery.md) the `Go to η-V analysis` button will be active as highlighted in the image below. + +![!Go to η-V analysis button.](../img/go-to-analysis-button.png){: loading=lazy } + +Click this button and the analysis page will open. + +!!! warning "Warning: Bad Sources" + Bad sources for analysis will be automatically removed. + These include sources that only have one datapoint or where the _η_ and/or _V_ are equal to 0 or have failed. + +![!Page appearance on initial load.](../img/analysis-page-first-load.png){: loading=lazy } + +## η-V Plot Description + +Shown on the left side of the page is the the log-log plot of the peak flux η and V metrics of the sources from the query. +The distributions of the metrics are fitted with a Gaussian, and a sigma cut is displayed on the plot that by default is set to a value of $3\sigma$. +The highlighted region represents the area of plot that is beyond both thresholds, and this is where transient sources will be found. +The colour of the points represent how many detection datapoints the source contains. + +### Plot Options + +![!η-V plot options.](../img/analysis-page-plot-options.png){: loading=lazy } + +At the bottom of the plot are options to change both the flux type and the multiplication factor of the sigma cut. +Once the new settings are entered click the `Apply` button and the plot will reload with the new options. + +## Viewing Source Light Curves & Information + +Hovering over a source on the plot will show an information window that displays the source name, id and the η and V values. + +![!Hover source information.](../img/analysis-page-hover.png){: loading=lazy } + +Clicking on the source will load the light curve, source information and external crossmatching search results into the panels on the right side of the page. +Selecting another source will dynamically update these panels without having to leave the page. + +![!Source light curve and information.](../img/analysis-page-source-display.png){: loading=lazy } + +The source information panel contains a link to go to the full [source detail page](sourcedetail.md) and the ability to [favourite a source](sourcetagsfavs.md) directly from this page. + +## Displaying High Source Counts + +When querying large pipeline runs it is possible that a query will return tens of thousands of results. +Plotting such a high number of sources is very intensive and would take a significant amount of time to render. +To solve this, when a high number of sources are requested to be plotted, all the sources outside of the threshold transient area are plotted as a static image that represents the distribution of the sources. +These sources are not interactive. +By default the threshold is set to 20,000 datapoints and is [configurable by the administrator](../gettingstarted/configuration.md#.env-file). + +Any sources that fall within the transient threshold region are plotted as normal and are interactive as with the standard plot. + +!!! warning "Warning: Setting Low Thresholds" + Setting low thresholds with high source counts could cause a significant amount of candidates to be plotted with the normal method. + This may result in the plot taking up to a few minutes to load within the browser. + +!!! note "Note: Colour Bar" + The colour bar only applies to the interactive datapoints. + +![!Page appearance on initial load with high source counts.](../img/analysis-page-first-load-datashader.png){: loading=lazy } diff --git a/docs/exploringwebsite/sourcedetail.md b/docs/exploringwebsite/sourcedetail.md new file mode 100644 index 000000000..8fd696140 --- /dev/null +++ b/docs/exploringwebsite/sourcedetail.md @@ -0,0 +1,80 @@ +# Source Detail + +This page presents all the information about the selected source, including a light curve and cutouts of all the measurements that are associated to the source. + +![!Source detail page.](../img/source-detail1.png){: loading=lazy } + +## Star, SIMBAD, NED, ZTF, Previous & Next Buttons + +These buttons do the following: + +* **Star**: Adds the source to the user's favourites, see [Source Tags and Favourites](sourcetagsfavs.md). +* **SIMBAD**: Performs a cone search on SIMBAD with a radius of 10 arcmin centered on the source location. +* **NED**: Performs a cone search on NED with a radius of 10 arcmin centered on the source location. +* **ZTF**: Performs a cone search for optical transient alerts from the Zwicky Transient Facility (ZTF) with a radius of 10 arcsec centered on the source location. +* **Previous**: Navigates to the previous source that was returned in the source query. +* **Next**: Navigates to the next source that was returned in the source query + +## Details + +A text representation of details of the source. + +## First Detection Postage Stamp + +The JS9 viewer is used to show the postage stamp FITS image (cutout) of the first source-finder detection for this source. Cutouts for each measurement are shown further down the page. + +## Aladin Lite Viewer + +[Aladin Lite Documentation](https://aladin.u-strasbg.fr/AladinLite/doc/){ target=_blank }. + +The central panel contains an Aladin Lite viewer, which by default displays the HIPS image from the [Rapid ASKAP Continuum Survey](https://research.csiro.au/racs/){ target=_blank }, centred on the location of the source. +Other surveys are available such as all epochs of the VAST Pilot Survey (including Stokes V) and other wavelength surveys such as 2MASS. + +## Flux & Variability Details + +Aggregate flux density statistics and variability metrics for this source, separated by flux type (peak or integrated). + +## Light Curve + +The light curve of the source is shown. The peak or integrated flux can be selected by using the radio selection buttons. + +![!Source detail page: light curve hover panel.](../img/light-curve-hover.png){: loading=lazy align=right } + +Hovering over the data points on the light curve will show an information panel that contains the date of the measurement, the flux and the measurement number. +It also contains a thumbnail image preview of the respective measurement. + +## Two-epoch Node Graph + +The node graph is a visual representation of what two-epoch pairings have significant variability metric values. +If an epoch pairing is significant then they are joined by a line on the graph. Hovering over the line will display the pair metrics for the selected flux type (peak or integrated) and highlight the epoch pairing on the light curve plot. + +## External Search Results Table + +This table shows the result of a query to the SIMBAD, NED, and TNS services for astronomical sources within 1 arcmin of the source location. Along with the name and coordinate of the matches, the on-sky separation between the source is shown along with the source type. + +![!Source detail page: external search results, user assigned tags and comments.](../img/source-detail2.png){: loading=lazy } + +## User Comments & Tags + +Users are able to read and post comments on a source using this form, in addition to adding and removing tags, see [Source Tags and Favourites](sourcetagsfavs.md). + +## JS9 Viewer Postage Stamps + +[JS9 website](https://js9.si.edu){ target=_blank }. + +More JS9 viewers are used to show the postage stamp FITS images of the measurements that are associated with the source, loaded from their respective image FITS files. The number of cutouts to display is configurable with the `MAX_CUTOUT_IMAGES` setting: only the first `MAX_CUTOUT_IMAGES` measurements will be displayed as cutout images. A warning will be displayed if the number of displayed cutouts has been truncated. Refer to the [pipeline configuration](../gettingstarted/configuration.md#pipeline) documentation for more information. + +!!! note + If the image data is removed from its location when the pipeline run was processed the JS9 viewer will no longer work. + +![!Source detail page: postage stamps.](../img/source-detail3.png){: loading=lazy } + +## Source Measurements Table + +This table displays the measurements that are associated with the source. The detail page for the measurement can be reached by clicking the name of the respective measurement. + +![!Source detail page: measurement and related tables.](../img/source-detail4.png){: loading=lazy } + +## Related Sources Table + +This table displays the sources that are a relation of the source in question. For further information refer to the [Relations](../design/association.md#relations) section in the association documentation. diff --git a/docs/exploringwebsite/sourcepages.md b/docs/exploringwebsite/sourcepages.md deleted file mode 100644 index 281426fe4..000000000 --- a/docs/exploringwebsite/sourcepages.md +++ /dev/null @@ -1,158 +0,0 @@ -# Source Pages - -This page details the website pages for information on the sources. - -## Source Query - -Users can filter and query the sources currently in the database by using the form located on this page. - -The form is submitted by clicking the blue :fontawesome-solid-search: button, the red :fontawesome-solid-trash: button will reset the form by removing all entered values. -Once the form is submitted the results are dynamically updated in the results table below the form (i.e. on the same page). - -The following sections provide further details on the form. - -![!The source query form.](../img/source-query-form.png){: loading=lazy } - -### Data Source - -Here a specific pipeline run can be selected from a dropdown list to filter sources to only those from the chosen run. By default sources from all runs are shown. - -!!! note - Only successfully completed pipeline runs are available to select. This rule also applies when `all` is selected. - -### Cone Search - -Users can choose whether to input their coordinates directly or use the object name resolver to attempt to fetch the coordinates. - -#### Manual Input - -The format of the coordinates should be in a standard format that is recognised by astropy, for example: - -* `21 29 45.29 -04 29 11.9` -* `21:29:45.29 -04:29:11.9` -* `322.4387083 -4.4866389` - -Galactic coordinates can also be entered by selecting `Galactic` from the dropdown menu that is set to `ICRS` by default. -Feedback will be given immediately whether the coordinates are valid, as shown in the screenshots below: - -![!Cone search: accepted coordinate input.](../img/cone-search-ok.png){: loading=lazy } -![!Cone search: rejected coordinate input.](../img/cone-search-bad.png){: loading=lazy } - -Once the coordinates have been entered the radius value must also be specified as shown in the screenshot above. Use the dropdown menu to change the radius unit to be `arcsec`, `arcmin` or `deg`. - -#### Name Resolver - -To use the name resolver, the name of the source should be entered into the `Object Name` field (e.g. `PSR J2129-04`), select the name resolver service and then click the `Resolve` button. -The coordinates will then be automatically filled on a successful match. - -![!Cone search: successful name resolver.](../img/cone-search-resolve.png){: loading=lazy } - -if no match is found then this will be communicated by the form as below: - -![!Cone search: unsuccessful name resolver.](../img/cone-search-resolver-bad.png){: loading=lazy } - -### Table Filters - -This section of the form allows the user to place thresholds and selections on specific metrics of the sources. -Please refer to the [Source Statistics page](../design/sourcestats.md) for details on the different metrics. There are also tooltips located on the form to offer explanations. - -The following options are not standard source metrics: - -#### Include and Exclude Tags - -Users can attach tags to sources (see [Source Tags and Favourites](sourcetagsfavs.md)) and here tags can be selected to include or exclude in the source search. - -#### Source Selection - -Here specific sources can be searched for by entering the source names, or source database `id` values, in a comma-separated list. For example: - -* `J011816.05-730747.77,J011816.05-730747.77,J213221.21-040900.42` -* `1031,1280,52` - -are valid entries to this search field. Use the dropdown menu to declare whether `name` (default) or `id` values are being searched. - -### Results Table - -Located directly below the form is the results table which is dynamically updated once the form is submitted. -The full detail page of a specific source can be accessed by clicking on the source name in the table. -Explanation of the table options can be found in the [DataTables section](datatables.md). - -![!Source query results table.](../img/source-query-results.png){: loading=lazy } - -## Source Detail Page - -This page presents all the information about the selected source, including a light curve and cutouts of all the measurements that are associated to the source. - -![!Source detail page.](../img/source-detail1.png){: loading=lazy } - -### Star, SIMBAD, NED, Previous & Next Buttons - -These buttons do the following: - -* **Star**: Adds the source to the user's favourites, see [Source Tags and Favourites](sourcetagsfavs.md). -* **SIMBAD**: Performs a cone search on SIMBAD with a radius of 10 arcmin centered on the source location. -* **NED**: Performs a cone search on NED with a radius of 10 arcmin centered on the source location. -* **Previous**: Navigates to the previous source that was returned in the source query. -* **Next**: Navigates to the next source that was returned in the source query - -### Details - -A text representation of details of the source, including position, number of measurements and whether the source is defined as `new`. - -### First Detection Postage Stamp - -Shown in this panel is a postage stamp of the first detection of the source in the pipeline run. Postage stamps of all measurements can be seen in the [main postage stamp section](#js9-viewer-postage-stamps) lower on the page. - -### Aladin Lite Viewer - -[Aladin Lite Documentation](https://aladin.u-strasbg.fr/AladinLite/doc/){ target=_blank }. - -The central panel contains an Aladin Lite viewer, which by default displays the HIPS image from the [Rapid ASKAP Continuum Survey](https://research.csiro.au/racs/){ target=_blank }, centred on the location of the source. -Other surveys are available such as all epochs of the VAST Pilot Survey (including Stokes V) and other wavelength surveys such as 2MASS. - -### Flux & Variability Details - -The flux details, both integrated and peak, of the source. Also shown are the variability parameters calculated by the pipeline. - -![!Source detail page: Flux and variability details, light curve and the node graph.](../img/source-detail2.png){: loading=lazy } - -### Light Curve - -The light curve of the source is shown. The peak or integrated flux can be selected by using the radio selection buttons. - -### Two-epoch Node Graph - -The node graph is a visual representation of what two-epoch pairings have significant variability metric values. -If an epoch pairing is significant then they are joined by a line on the graph. Hovering over the line will highlight the epoch pairing on the light curve plot. - -### External Search Results Table - -This table shows the result of a query to the SIMBAD and NED services for astronomical sources within 1 arcmin of the source location. -Along with the name and coordinate of the matches, the on-sky separation between the source is shown along with the source type. - -![!Source detail page: External results table and the user comments field.](../img/source-detail3.png){: loading=lazy } - -### User Comments & Tags - -Users are able to read and post comments on a measurement using this form, in addition to adding and removing tags, see [Source Tags and Favourites](sourcetagsfavs.md). - -### JS9 Viewer Postage Stamps - -[JS9 website](https://js9.si.edu){ target=_blank }. - -The JS9 viewer is used to show the postage stamp FITS images of the measurements that are associated with the source, loaded from their respective image FITS files. - -!!! note - If the image data is removed from its location when the pipeline run was processed the JS9 viewer will no longer work. - -![!Source detail page: postage stamps.](../img/source-detail4.png){: loading=lazy } - -### Source Measurements Table - -This table displays the measurements that are associated with the source. The detail page for the measurement can be reached by clicking the name of the respective measurement. - -![!Source detail page: measurement and related tables.](../img/source-detail5.png){: loading=lazy } - -### Related Sources Table - -This table displays the sources that are a relation of the source in question. For further information refer to the [Relations](../design/association.md#relations) section in the association documentation. diff --git a/docs/exploringwebsite/sourcequery.md b/docs/exploringwebsite/sourcequery.md new file mode 100644 index 000000000..060e34d7e --- /dev/null +++ b/docs/exploringwebsite/sourcequery.md @@ -0,0 +1,86 @@ +# Source Query + +This page details the Source Query interface. + +## Query Options + +Users can filter and query the sources currently in the database by using the form located on this page. + +The form is submitted by clicking the blue :fontawesome-solid-search: button, the red :fontawesome-solid-trash: button will reset the form by removing all entered values. +Once the form is submitted the results are dynamically updated in the results table below the form (i.e. on the same page). + +The following sections provide further details on the form. + +![!The source query form.](../img/source-query-form.png){: loading=lazy } + +## Data Source + +Here a specific pipeline run can be selected from a dropdown list to filter sources to only those from the chosen run. By default sources from all runs are shown. + +!!! note + Only successfully completed pipeline runs are available to select. This rule also applies when `all` is selected. + +## Cone Search + +Users can choose whether to input their coordinates directly or use the object name resolver to attempt to fetch the coordinates. + +### Manual Input + +The format of the coordinates should be in a standard format that is recognised by astropy, for example: + +* `21 29 45.29 -04 29 11.9` +* `21:29:45.29 -04:29:11.9` +* `322.4387083 -4.4866389` + +Galactic coordinates can also be entered by selecting `Galactic` from the dropdown menu that is set to `ICRS` by default. +Feedback will be given immediately whether the coordinates are valid, as shown in the screenshots below: + +![!Cone search: accepted coordinate input.](../img/cone-search-ok.png){: loading=lazy } +![!Cone search: rejected coordinate input.](../img/cone-search-bad.png){: loading=lazy } + +Once the coordinates have been entered the radius value must also be specified as shown in the screenshot above. Use the dropdown menu to change the radius unit to be `arcsec`, `arcmin` or `deg`. + +### Name Resolver + +To use the name resolver, the name of the source should be entered into the `Object Name` field (e.g. `PSR J2129-04`), select the name resolver service and then click the `Resolve` button. +The coordinates will then be automatically filled on a successful match. + +![!Cone search: successful name resolver.](../img/cone-search-resolve.png){: loading=lazy } + +if no match is found then this will be communicated by the form as below: + +![!Cone search: unsuccessful name resolver.](../img/cone-search-resolver-bad.png){: loading=lazy } + +## Table Filters + +This section of the form allows the user to place thresholds and selections on specific metrics of the sources. +Please refer to the [Source Statistics page](../design/sourcestats.md) for details on the different metrics. There are also tooltips located on the form to offer explanations. + +The following options are not standard source metrics: + +### Include and Exclude Tags + +Users can attach tags to sources (see [Source Tags and Favourites](sourcetagsfavs.md)) and here tags can be selected to include or exclude in the source search. + +### Source Selection + +Here specific sources can be searched for by entering the source names, or source database `id` values, in a comma-separated list. For example: + +* `J011816.05-730747.77,J011816.05-730747.77,J213221.21-040900.42` +* `1031,1280,52` + +are valid entries to this search field. Use the dropdown menu to declare whether `name` (default) or `id` values are being searched. + +## Results Table + +Located directly below the form is the results table which is dynamically updated once the form is submitted. +The full [detail page](sourcedetail.md) of a specific source can be accessed by clicking on the source name in the table. +Explanation of the table options can be found in the [DataTables section](datatables.md). +The `Go to η-V analysis` button will launch the η-V analysis page for the sources contained in the query results. +Please refer to the [Source η-V Analysis Page](sourceanalysis.md) section for the full details of this feature. + +!!! note + The `Go to η-V analysis` button is only active after a query has been performed. + +![!Source query results table.](../img/source-query-results.png){: loading=lazy } + diff --git a/docs/exploringwebsite/websiteoverview.md b/docs/exploringwebsite/websiteoverview.md index 31242d6a4..c9346112c 100644 --- a/docs/exploringwebsite/websiteoverview.md +++ b/docs/exploringwebsite/websiteoverview.md @@ -10,14 +10,7 @@ For admins, refer to the following pages for details of the configuration and se ![!VAST Pipeline homepage.](../img/homepage.png){: loading=lazy } -The homepage provides a summary of the data currently held in the pipeline instance that has been processed. The four cards at the top of the homepage provide total values -for the amount of pipeline runs, images, measurements and sources that are stored in the database. Clicking any of them will take you to the respective overview page for the data type. - -!!! note - The totals presented on the homepage are totals for all pipeline runs combined! - -Also displayed is a sky region map that shows all the areas of the sky that have had successful and completed pipeline runs performed. - +The homepage contains a welcome message and links to popular pages of the list of pipeline runs, the source query page or the list of images. ## Navbar @@ -46,18 +39,11 @@ Navigates the user to the list of pipeline runs available, which in turn link to ### Sources Query -See the [Source Pages](sourcepages.md) section. +See the [Source Query](sourcequery.md) section. Takes the user to the source query page, where users can search for sources by defining a set of thresholds and feature requirements. From the results users can also access the detail page for individual sources. -### Measurements - -See the [Measurement Pages](measurementpages.md) section. - -Navigates the user to the measurements page that features a table containing all the measurements currently held in the database. -From here users can also access the detail page for individual measurements. - ### Images See the [Image Pages](imagepages.md) section. diff --git a/docs/faq.md b/docs/faq.md index d15a40daa..7075f4685 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -24,7 +24,7 @@ Please open a discussion or issue on GitHub if you intend to give this a go! Currently the pipeline only supports Stokes I data. -Users can view Stokes V HIPS maps of the RACS and VAST surveys in the Aladin Lite tool on the [source detail page](exploringwebsite/sourcepages.md#source-detail-page). +Users can view Stokes V HIPS maps of the RACS and VAST surveys in the Aladin Lite tool on the [source detail page](exploringwebsite/sourcedetail.md). The support of Stokes V is [planned in a future update](https://github.com/askap-vast/vast-pipeline/issues/223){:target="_blank"}. diff --git a/docs/gen_doc_stubs.py b/docs/gen_doc_stubs.py index 29249b125..bacdaa728 100755 --- a/docs/gen_doc_stubs.py +++ b/docs/gen_doc_stubs.py @@ -11,7 +11,7 @@ # See https://github.com/mkdocstrings/mkdocstrings/issues/141 # __init__ is there just to avoid building these. # TODO: Fix Django pages for mkdocs. -problem_files = ['serializers.py', 'views.py', '__init__.py', '_version.py'] +problem_files = ['__init__.py', '_version.py'] for path in Path("vast_pipeline").glob("**/*.py"): diff --git a/docs/gettingstarted/configuration.md b/docs/gettingstarted/configuration.md index ab85d18f3..0fd9ccbab 100644 --- a/docs/gettingstarted/configuration.md +++ b/docs/gettingstarted/configuration.md @@ -60,16 +60,17 @@ The following instructions, will get you started in setting up the database and 3. Set the database connection settings in the `webinterface/.env` file by modifying `DATABASE_URL` (for URL syntax see [this link](https://django-environ.readthedocs.io/en/latest/#tips){:target="_blank"}). For example: - ```bash - DATABASE_URL=psql://vast:@localhost:55002/vastdb - ``` + !!! example ".env" + ```console + DATABASE_URL=psql://vast:@localhost:55002/vastdb + ``` !!! note The connection details are the same that you setup during the [installation](installation.md). The database/user names must not contain any spaces or dashes, so use the underscore if you want, e.g. `this_is_my_db_name`. 4. Create the pipeline database tables. The `createcachetable` command creates the cache tables required by DjangoQ. - ```bash + ```terminal python manage.py migrate python manage.py createcachetable ``` @@ -78,7 +79,23 @@ The following instructions, will get you started in setting up the database and * `PIPELINE_WORKING_DIR`: location to store various pipeline output files. * `RAW_IMAGE_DIR`: default location that the pipeline will search for input images and catalogues to ingest during a pipeline run. Data inputs can also be defined as absolute paths in a pipeline run configuration file, so this setting only affects relative paths in the pipeline run configuration. - * `HOME_DATA_DIR`: additional location to search for input images and catalogues that is relative to the user's home directory. Intended for multi-user server deployments and unlikely to be useful for local installations. + * `HOME_DATA_DIR`: a path relative to a user's home directory to search for additional input images and catalogues. Intended for multi-user server deployments and unlikely to be useful for local installations. See below for some examples. + * `HOME_DATA_ROOT`: path to the location of user home directories. Used together with `HOME_DATA_DIR`. If not supplied, the pipeline will search for the user's home directory using the default OS location. See below for some examples. + + !!! example ".env – User data configuration examples" + In the following examples, assume that the user's name is `foo`. + + ```console + # HOME_DATA_ROOT=Uncomment to set a custom path to user data dirs + HOME_DATA_DIR=vast-pipeline-extra-data + ``` + Using the above settings, the pipeline will search for additional input data in the user's home directory as resolved by the OS. e.g. on an Ubuntu system, this would be `/home/foo/vast-pipeline-extra-data`. + + ```console + HOME_DATA_ROOT=/data/home + HOME_DATA_DIR=vast-pipeline-extra-data + ``` + Using the above settings, the pipeline will search for additional input data in `/data/home/foo/vast-pipeline-extra-data`. While the default values for these settings are relative to the pipeline codebase root (i.e. within the repo), we recommend creating these directories outside of the repo and updating the `webinterface/.env` file appropriately with absolute paths. For example, assuming you wish to create these directories in `/data/vast-pipeline`: @@ -86,16 +103,15 @@ The following instructions, will get you started in setting up the database and mkdir -p /data/vast-pipeline mkdir /data/vast-pipeline/pipeline-runs mkdir /data/vast-pipeline/raw-images - mkdir /data/vast-pipeline/vast-pipeline-extra-data ``` and update the `webinterface/.env` file with: - ```bash - PIPELINE_WORKING_DIR=/data/vast-pipeline/pipeline-runs - RAW_IMAGE_DIR=/data/vast-pipeline/raw-images - HOME_DATA_DIR=/data/vast-pipeline/vast-pipeline-extra-data - ``` + !!!example ".env" + ```console + PIPELINE_WORKING_DIR=/data/vast-pipeline/pipeline-runs + RAW_IMAGE_DIR=/data/vast-pipeline/raw-images + ``` ## .env File @@ -124,18 +140,30 @@ Shown below is the [`.env.template`](https://github.com/askap-vast/vast-pipeline SOCIAL_AUTH_GITHUB_ORG_NAME=fillMeUp SOCIAL_AUTH_GITHUB_ADMIN_TEAM=fillMeUp + # External APIs + # TNS_API_KEY= uncomment and fill to use + # TNS_USER_AGENT= uncomment and fill to use + # Pipeline PIPELINE_WORKING_DIR=pipeline-runs FLUX_DEFAULT_MIN_ERROR=0.001 POS_DEFAULT_MIN_ERROR=0.01 RAW_IMAGE_DIR=raw-images HOME_DATA_DIR=vast-pipeline-extra-data + # HOME_DATA_ROOT=Uncomment to set a custom path to user data dirs # PIPELINE_MAINTAINANCE_MESSAGE=Uncomment and fill to show MAX_PIPELINE_RUNS=3 MAX_PIPERUN_IMAGES=200 + MAX_CUTOUT_IMAGES=30 + + # Q_CLUSTER_TIMEOUT=86400 + # Q_CLUSTER_RETRY=86402 + # Q_CLUSTER_MAX_ATTEMPTS=1 + + ETA_V_DATASHADER_THRESHOLD=20000 ``` -The available settings are grouped into three distinct categories: +The available settings are grouped into 4 distinct categories: ### Django @@ -143,9 +171,10 @@ These settings are standard Django settings that are commonly set in the `settin Please see [this page](https://docs.djangoproject.com/en/3.2/ref/settings/){:target="_blank"} in the Django documentation for explanations on their meaning. Multiple entries for settings such as `EXTRA_APPS` or `EXTRA_MIDDLEWARE` can be entered as comma-separated strings like the following example: -```console -EXTRA_APPS=django_extensions,debug_toolbar -``` +!!!example ".env" + ```console + EXTRA_APPS=django_extensions,debug_toolbar + ``` ### GitHub Authentication @@ -156,6 +185,29 @@ Please refer to the [Python Social Auth documentation](https://python-social-aut By default the pipeline is set up for authentication using GitHub organizations. Note that switching to teams will require changes to `settings.py`. Please refer to the instructions in the [Python Social Auth documentation](https://python-social-auth.readthedocs.io/en/latest/backends/github.html){:target="_blank"}. +### External APIs + +The pipeline website interface supports querying some external APIs, e.g. SIMBAD, NED, VizieR, TNS. Some of these APIs require authentication which are described below. + +#### Transient Name Server (TNS) + +If you wish to enable TNS cone search results on the [source detail page](../exploringwebsite/sourcedetail.md), you must first obtain an API key for TNS. + +1. Create a TNS account at https://www.wis-tns.org/user/register if you do not already have one. +2. Once logged in, create a bot by navigating to https://www.wis-tns.org/bots and clicking "Add bot" near the top of the table. +3. Fill in the create bot form. Ensure that you select "Create new API key". +4. Securely store the API key and paste it into your pipeline `webinterface/.env` file under `TNS_API_KEY`. + + !!! warning + Do not lose the API key! It is not possible to retrieve it again past this point. + +5. Navigate to your [account page on TNS](https://www.wis-tns.org/user) and copy the User-Agent specification. Paste it into your pipeline `webinterface/.env` file under `TNS_USER_AGENT`. + + !!! example "webinterface/.env" + ```console + TNS_USER_AGENT='tns_marker{"tns_id": 0000, "type": "user", "name": "your_username"}' + ``` + ### Pipeline These settings apply to various aspects of the VAST pipeline itself. The table below provides descriptions of each setting. @@ -166,16 +218,23 @@ These settings apply to various aspects of the VAST pipeline itself. The table b | `FLUX_DEFAULT_MIN_ERROR` | 0.001 | In the event a measurement is ingested with a flux error of 0 from Selavy, the error is replaced with this default value (mJy). | | `POS_DEFAULT_MIN_ERROR` | 0.01 | In the event a measurement is ingested with an positional error of 0 from Selavy, the error is replaced with this default value (arcsec). | | `RAW_IMAGE_DIR` | raw-images | Directory where the majority of raw ASKAP FITS images are expected to be stored. This directory is scanned to provide user with an image list when configuration a job using the website interface. | -| `HOME_DATA_DIR` | vast-pipeline-extra-data | Not currently used. Safe to ignore. | +| `HOME_DATA_DIR` | vast-pipeline-extra-data | Directory relative to the user's home directory that contains additional input images and catalogues. Safe to ignore if you don't intend to use this functionality. | +| `HOME_DATA_ROOT` | Disabled | Path to directory that contains user's home directories. Enable by uncommenting and setting the desired path. If left disabled (commented), the pipeline will assume the OS default home directory location. | `PIPELINE_MAINTAINANCE_MESSAGE` | Disabled | The message to display at the top of the webserver. See image below this table for an example. Comment out the setting to disable. | | `MAX_PIPELINE_RUNS` | 3 | The allowed maximum number of concurrent pipeline runs. | | `MAX_PIPERUN_IMAGES` | 200 | The allowed maximum number of images in a single pipeline run (non-admins). | +| `MAX_CUTOUT_IMAGES` | 30 | The maximum number of cutout images to display on the source detail pages. The first `MAX_CUTOUT_IMAGES` cutouts are displayed, in temporal order. | +| `Q_CLUSTER_TIMEOUT` | 86400 | Number of seconds a Django-Q cluster worker may spend on a task before it is terminated. See the [Django-Q documentation](https://django-q.readthedocs.io/en/latest/configure.html#timeout). | +| `Q_CLUSTER_RETRY` | 86402 | Number of seconds a Django-Q broker will wait for a cluster to finish a task before it's presented again. See the [Django-Q documentation](https://django-q.readthedocs.io/en/latest/configure.html#retry). | +| `Q_CLUSTER_MAX_ATTEMPTS` | 1 | Number of times a failed task is retried. See the [Django-Q documentation](https://django-q.readthedocs.io/en/latest/configure.html#max-attempts). | +| `ETA_V_DATASHADER_THRESHOLD` | 20000 | The number of datapoints above which the eta-V plot uses datashader to plot the non-threshold distribution. | #### Maintenance Message Example -```console -PIPELINE_MAINTAINANCE_MESSAGE=This website is subject to rapid changes which may result in data loss and may go offline with minimal warning. Please be mindful of usage. -``` +!!!example ".env" + ```console + PIPELINE_MAINTAINANCE_MESSAGE=This website is subject to rapid changes which may result in data loss and may go offline with minimal warning. Please be mindful of usage. + ``` ![!Maintenance message example.](../img/maintenance-message.png){: loading=lazy } diff --git a/docs/help_and_acknowledgements.md b/docs/help_and_acknowledgements.md index bc349c509..e4298d929 100644 --- a/docs/help_and_acknowledgements.md +++ b/docs/help_and_acknowledgements.md @@ -29,4 +29,6 @@ The VAST Pipeline development was supported by: * Software support resources awarded under the Astronomy Data and Computing Services (ADACS) Merit Allocation Program. ADACS is funded from the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government and managed by Astronomy Australia Limited (AAL). * NSF grant AST-1816492. +We also acknowledge the [LOFAR Transients Pipeline (TraP)](https://ascl.net/1412.011) ([Swinbank, et al. 2015](https://ui.adsabs.harvard.edu/abs/2015A%26C....11...25S/abstract)) from which various concepts and design choices have been implemented in the VAST Pipeline. + The developers thank the creators of [SB Admin 2](https://github.com/StartBootstrap/startbootstrap-sb-admin-2) to make the dashboard template freely available. diff --git a/docs/img/action-buttons.png b/docs/img/action-buttons.png new file mode 100644 index 000000000..6c004cd76 Binary files /dev/null and b/docs/img/action-buttons.png differ diff --git a/docs/img/add-images-detail.png b/docs/img/add-images-detail.png index fea5f4b60..ae0285f34 100644 Binary files a/docs/img/add-images-detail.png and 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--git a/docs/img/source-query-results.png b/docs/img/source-query-results.png index 6e3712f71..c8d6d8fce 100644 Binary files a/docs/img/source-query-results.png and b/docs/img/source-query-results.png differ diff --git a/docs/outputs/coldesc.md b/docs/outputs/coldesc.md index c952543aa..f38493690 100644 --- a/docs/outputs/coldesc.md +++ b/docs/outputs/coldesc.md @@ -120,7 +120,6 @@ This page details the columns contained in each output file. |`m_peak`| n/a | The pair $m$ value using the peak flux. | |`m_int`| n/a | The pair $m$ value using the integrated flux. | |`source_id`| n/a | The database `id` of the source the pair is associated to. | -|`id`| n/a | The database `id` of the measurement pair. | ## relations diff --git a/docs/outputs/outputs.md b/docs/outputs/outputs.md index 754eee30e..fd662216c 100644 --- a/docs/outputs/outputs.md +++ b/docs/outputs/outputs.md @@ -37,7 +37,7 @@ pipeline-runs │   ├── forced_measurements_VAST_2118-06A_EPOCH06x_I_cutout_fits.parquet │   ├── forced_measurements_VAST_2118-06A_EPOCH12_I_cutout_fits.parquet │   ├── images.parquet -│   ├── log.txt +│   ├── YYYY-MM-DD-HH-MM-SS_log.txt │   ├── measurements.arrow │   ├── measurement_pairs.arrow │   ├── measurement_pairs.parquet @@ -58,10 +58,8 @@ The two-epoch measurement pairs are also saved to arrow format due to the same r !!! note At the time of development `vaex` could not open parquets in an out-of-core context. This will be reviewed in the future if such functionality is added to `vaex`. -!!! tip - The arrow files can be generated after a run has successfully completed (must be done by an administrator, refer to the admin command [`createmaeasarrow`](../adminusage/cli.md#createmeasarrow)). - To enable the arrow files to be produced, the option `measurements.write_arrow_files` is required to be set to `True` in the pipeline run config. +Alternatively, the arrow files can be generated after the completion of the run, see the [Generating Arrow Files page](../../using/genarrow) for full details. ### Image Data @@ -112,7 +110,7 @@ Here, for each image, the selavy measurements that have been ingested are stored | `config_prev.yaml` | The previous pipeline run configuration file used by the [add image](../using/addtorun.md) mode. | | `forced_measurements*.parquet` | Multiple files that contain the forced measurements extracted from the respective image denoted in the filename. | | `images.parquet` | Contains the information of the images processed in the pipeline run. | -| `log.txt` | The log file of the pipeline run. | +| `YYYY-MM-DD-HH-MM-SS_log.txt` | The log file of the pipeline run. It is timestamped with the date and time of the run start. | | `measurements.arrow` | An [Apache Arrow](https://arrow.apache.org/overview/){:target="_blank"} format file containing all the measurements associated with the pipeline run (see [Arrow Files](#arrow-files)).| | `measurement_pairs.arrow` | An [Apache Arrow](https://arrow.apache.org/overview/){:target="_blank"} format file containing all the measurement pair metrics (see [Arrow Files](#arrow-files)). | | `measurement_pairs.parquet` | Contains all the measurement pairs metrics. | diff --git a/docs/outputs/usingoutputs.md b/docs/outputs/usingoutputs.md index 2aa894503..bbd9d4c0b 100644 --- a/docs/outputs/usingoutputs.md +++ b/docs/outputs/usingoutputs.md @@ -129,8 +129,8 @@ The table below shows what parameters act as keys to link data from the differen ## vast-tools -[Link to the `vast-tools` repository](https://github.com/askap-vast/vast-tools){:target="_blank"}. +[Link to the `vast-tools` documentation](https://vast-survey.org/vast-tools/){:target="_blank"}. VAST has developed a python library called `vast-tools` that makes the exploration of results from the pipeline simple and efficient, in addition to being designed to be used in a [Jupyter Notebook](https://jupyter.org) environment. -For full details, refer to the repository located on [GitHub](https://github.com/askap-vast/vast-tools){:target="_blank"} and be sure to take a look at the [example notebooks](https://github.com/askap-vast/vast-tools/tree/master/notebook-examples). +Full details can be found in the documentation linked above, which also includes example notebooks of how to interact with the data. diff --git a/docs/theme/main.html b/docs/theme/main.html index 1b2de320d..5ecf60ff2 100644 --- a/docs/theme/main.html +++ b/docs/theme/main.html @@ -28,13 +28,17 @@ {% block announce %} - New in version 1.0.0: Documentation, add epoch mode and more 🙌. Check the release notes! + + + New in version 1.1.0: Support for compressed fits files, python 3.10 and more 🧑‍💻. Check the release notes! + + {% endblock %} @@ -48,3 +52,10 @@ {% endblock libs %} + +{% block outdated %} + You're viewing an outdated or development version. + + Click here to go to latest stable release. + +{% endblock %} diff --git a/docs/using/addtorun.md b/docs/using/addtorun.md index 4949e9b8f..9e5683b78 100644 --- a/docs/using/addtorun.md +++ b/docs/using/addtorun.md @@ -46,7 +46,7 @@ Select the `Add Images or Re-Process Run` button at the top right of the run det You can refresh the page to check the status of the run. You can confirm that the images have been added correctly by consulting the log output found below the configuration file. New images should have been ingested and output similar to the following should be present: -!!! example "log.txt" +!!! example "2021-04-02-21-14-31_log.txt" ```console 2021-04-02 21:17:00,628 association INFO Starting association. @@ -56,15 +56,14 @@ You can refresh the page to check the status of the run. You can confirm that th Once the processing has `Completed` the run detail page will now show the updated statistics and information of the run. -![!Pipeline run successfully completed after adding images.](../img/add-images-finished.png){: loading=lazy } +![!Pipeline run successfully completed after adding images.](../img/docs-example-run-detail.png){: loading=lazy } ## Restore Run to Pre-Add Version -When images are added to a run, a backup is made of the run before proceeding which can be used to restore the run to the pre-addition version. For example, perhaps the wrong images were added or an error occured mid-addition that could not be resolved. +When images are added to a run, a backup is made of the run before proceeding which can be used to restore the run to the pre-addition version. +For example, perhaps the wrong images were added or an error occurred mid-addition that could not be resolved. -This command is currently unavailable to perform through the website interface but can be performed by an administrator of the pipeline via the command line interface. Please contact the adminstrator for the pipeline instance to request a run restoration. - -The command to perform this operation is `restorepiperun` and is described in the admin section [here](../../adminusage/cli/#restorepiperun). +For full details see the documentation page for restoring a run [here](restorerun.md). !!! warning Do not add any further images if you wish to restore otherwise the backup version will be lost! diff --git a/docs/using/deleterun.md b/docs/using/deleterun.md index 4cbfc31df..098b4a52a 100644 --- a/docs/using/deleterun.md +++ b/docs/using/deleterun.md @@ -1,5 +1,46 @@ # Deleting a Run -Currently a run can only be deleted by an administrator via the command line interface, and is not able to be performed using the website. Please contact the administrator if you wish to delete a run. +This page describes how to delete a pipeline run through the website interface. -Administrators can refer to the [`clearpiperun`](../adminusage/cli/#clearpiperun) command for details on how to reset a pipeline run. \ No newline at end of file +Deleting a run means that all outputs such as sources and associations are deleted from the database and the run itself is also removed. +Images and the accompanying measurements are only removed if they were used solely by the deleted run. +The run directory is also deleted that contains the output files and configuration files. + +A pipeline run can only be deleted by the creator or an administrator. + +!!! warning + As stated above, deleting a run through the website will also delete the full run directory, which includes the configuration files. + Please manually back up the configuration file if you think you are likely to revisit that particular run configuration in the future. + +!!! tip "Admin Tip" + Administrators can refer to the [`clearpiperun`](../adminusage/cli/#clearpiperun) command for details on how to reset a pipeline run via the command line. + +## Step-by-step Guide + +### 1. Navigate to the Run Detail Page + +Navigate to the detail page of the run you wish to delete. + +![!Pipeline run detail page.](../img/docs-example-run-detail.png){: loading=lazy } + +### 2. Click on the Delete Run Button + +Click on the `Delete Run` button at the top right of the page, to open the confirmation modal. + +![!Delete Run button.](../img/delete-run-button.png){: loading=lazy } +![!Delete Run confirmation modal.](../img/delete-run-modal.png){: loading=lazy } + +### 3. Confirm Deletion + +To confirm the deletion click on the `Delete Run` button in the modal. +Once pressed the website will direct back to the `Pipeline Runs` page and a confirmation message will appear in the top right. + +!!! note + Runs with lots of database entries may take a short time to delete. + Hence, the run may still appear in the pipeline runs list for a short time following the request with a status of `Deleting`. + +![!Delete Run notification.](../img/delete-run-notification.png){: loading=lazy } + +Refreshing the page will show a deleting status if the process is still running: + +![!Delete Run deleting status.](../img/delete-run-deleting-status.png){: loading=lazy } diff --git a/docs/using/genarrow.md b/docs/using/genarrow.md new file mode 100644 index 000000000..160a7c75b --- /dev/null +++ b/docs/using/genarrow.md @@ -0,0 +1,66 @@ +# Generating Arrow Files + +This page describes how to generate the measurement arrow files for a pipeline run if the option in the configuration file to create them was turned off. + +Arrow files only be generated by the creator or an administrator. + +Two files are produced by the method: + +| File | Description | +| ---- | ----------- | +| `measurements.arrow` | An [Apache Arrow](https://arrow.apache.org/overview/){:target="_blank"} format file containing all the measurements associated with the pipeline run (see [Arrow Files](#arrow-files)). Extra processing is performed in the creation of this file such that source ids are already in place for the measurements. | +| `measurement_pairs.arrow` | An [Apache Arrow](https://arrow.apache.org/overview/){:target="_blank"} format file containing all the measurement pair metrics (see [Arrow Files](#arrow-files)). | + +!!! tip "Arrow Files Available" + Users can see if arrow files are present for the run of interest by checking the respective run detail page. + ![!Arrow files available.](../img/arrow-files-available.png){: loading=lazy } + + +!!! tip "Admin Tip" + The arrow files can be generated using the command line using the command [`createmaeasarrow`](../adminusage/cli.md#createmeasarrow)). + +## Why Create Arrow Files? + +Large pipeline runs (hundreds of images) mean that to read the measurements, hundreds of parquet files need to be read in, and can contain millions of rows. +This can be slow using libraries such as pandas, and also consumes a lot of system memory. + +Instead, if the measurements are saved in the [Apache Arrow](https://arrow.apache.org/overview/){:target="_blank"} format, libraries such as [`vaex`](https://vaex.io){:target="_blank"} are able to open `.arrow` files in an out-of-core context so the memory footprint is hugely reduced along with the reading of the file being very fast. +The two-epoch measurement pairs are also saved to arrow format due to the same reasons. + +See [Reading with vaex](../../outputs/usingoutputs#reading-with-vaex) for further details on using `vaex`. + +## Step-by-step Guide + +### 1. Navigate to the Run Detail Page + +Navigate to the detail page of the run you wish to generate arrow files for. + +![!Pipeline run detail page.](../img/docs-example-run-detail.png){: loading=lazy } + +### 2. Select the Generate Arrow Files Option + +Click the `Generate Arrow Files` option at the top-right of the page. + +![!Generate arrow button.](../img/generate-arrow-button.png){: loading=lazy } + +This will open the generate arrow files modal. + +![!Generate arrow modal.](../img/generate-arrow-modal.png){: loading=lazy } + +### 3. Submit Generate Arrow Files Request + +It is possible to overwrite existing arrow files by toggling the `Overwrite Current Files` option. + +When ready, click the `Generate Arrow Files` button on the modal to submit the generate request. +A notification will show to indicate whether the submission was successful. + +![!Generate arrow files notification.](../img/generate-arrow-notification.png){: loading=lazy } + +### 4. Refresh and Check the Generate Arrow Files Log File + +It is possible to check the progress by looking at the Generate Arrow Files Log File which can be found on the run detail page. +The log will not be refreshed automatically and instead the page needs to be manually refreshed. + +Once completed the arrow files will be available for use. + +![!Generate arrow files log file.](../img/generate-arrow-files-log.png){: loading=lazy } diff --git a/docs/using/requireddata.md b/docs/using/requireddata.md new file mode 100644 index 000000000..001e87bfa --- /dev/null +++ b/docs/using/requireddata.md @@ -0,0 +1,237 @@ +# Required Data + +This page gives an overview of what data is required to run the pipeline along with how to obtain the data. + +## Acquiring ASKAP Data + +!!! note "Note: VAST Data Releases" + If you are a member of the VAST collaboration you will have access to the VAST data release which contains the data for the VAST Pilot Survey. + Refer to the [VAST wiki](https://github.com/askap-vast/vast-project/wiki/Pilot-Survey-Status-&-Data){:target="_blank"} for more details. + +Data produced by the ASKAP telescope can be accessed by using [The CSIRO ASKAP Science Data Archive (CASDA)](https://research.csiro.au/casda/){:target="_blank"}. +CASDA provides a web form for users to search for the data they are interested in and request for the data to be staged for download. +Note that a form of account or registration is required to download image cube products. + +All data products from CASDA should be compatible without any modifications. +Please report an [issue](https://github.com/askap-vast/vast-pipeline/issues){:target="_blank"} if you find this to not be the case. + +!!! tip "Tip: CASDA Data Products" + Descriptions of the data products available on CASDA can be found on [this page](https://research.csiro.au/casda/data-products/){:target="_blank"}. + +## Pipeline Required Data + +!!! warning "Warning: Correcting Data" + Currently, the pipeline does not contain any processes to correct the data as it is ingested by the pipeline. + For example, if corrections to the flux or positions need to be applied, these should be done to the data directly before they are processed by the pipeline. + + If an image has been previously ingested before corrections were applied, the filename of the corrected image must be changed. + Doing so will make the pipeline see the corrected image as a new image and reingest the corrected data. + +A pipeline run requires a minimum of two ASKAP observational images to process. +For each image, the following table provides a summary of the files that are required. + +| Data | File Type | Description | +| ---- | --------- | ----------- | +| Primary image | .fits | The primary, Stokes I, taylor 0, image of the observation. | +| Component Catalogue | .xml, .csv, .txt | The component source catalogue produced by the source finder [`selavy`](https://www.atnf.csiro.au/computing/software/askapsoft/sdp/docs/current/analysis/index.html){:target="_blank"} from the primary image. | +| Noise map of primary image | .fits | The noise (or rms) map produced by the source finder [`selavy`](https://www.atnf.csiro.au/computing/software/askapsoft/sdp/docs/current/analysis/index.html){:target="_blank"} from the primary image. | +| Background map of primary image (optional) | .fits | The background (or mean) map produced by the source finder [`selavy`](https://www.atnf.csiro.au/computing/software/askapsoft/sdp/docs/current/analysis/index.html){:target="_blank"} from the primary image. The background image is only required when [source monitoring](../../design/monitor) is enabled. | + +Refer to the respective sections on this page for more details on these inputs. + +### Image File + +This is the primary image file produced by the ASKAP pipeline. +The pipeline requires the Stokes I, total intensity (taylor 0) image file. +It is also recommended to use the convolved final image, where each of the 36 individual beams have been convolved to a common resolution prior to the creation of the combined mosaic of the field. +These files are denoted by a `.conv` in the file name. + +Must be in the `FITS` file format. + +!!! example "Example CASDA Filename" + ``` + image.i.SB25597.cont.taylor.0.restored.conv.fits + ``` + +### Component Catalogues + +Currently, the pipeline does not contain any source finding capabilities so the source catalogue must be provided. +In particular, the pipeline requires the continuum component catalogue produced by the [`selavy`](https://www.atnf.csiro.au/computing/software/askapsoft/sdp/docs/current/analysis/index.html){:target="_blank"} source finder. + +The files can be one of three formats: + + * `XML` The VOTable XML format of the selavy components catalogue. + Provided by CASDA. + Must have a file extension of `.xml`. + * `CSV` The csv format of the selavy components catalogue. + Provided by CASDA. + Must have a file extension of `.csv`. + * `TXT` 'Fixed-width' formatted output produced by the selavy source finder directly. + These are not available through CASDA. + Must have a file extension of `.txt`. + +!!! example "Example CASDA Filenames" + === "xml" + ``` + selavy-image.i.SB25597.cont.taylor.0.restored.conv.components.xml + ``` + === "csv" + ``` + AS107_Continuum_Component_Catalogue_25597_3792.csv + ``` + + Note that CASDA does not provide the fixed width text format selavy output. + +!!! example "File format examples" + + === "xml" + + ``` xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SB25600_island_1 SB25600_component_1a J053527-691611 05:35:27.9 -69:16:11 83.866441 -69.269927 0.00 0.00 1367.5 780.068 0.721 843.563 1.692 11.80 8.40 176.22 0.01 0.01 0.11 3.09 1.82 56.44 0.78 2.35 1.49 596.911 1811.003 -0.93 -99.00 0.00 0.00 1.038 0 0 1 0
SB25600_island_2 SB25600_component_2a J060004-703833 06:00:04.9 -70:38:33 90.020608 -70.642664 0.01 0.01 1367.5 438.885 1.202 494.222 2.918 11.65 8.86 9.51 0.03 0.04 0.40 5.22 0.00 58.20 0.09 0.00 0.82 5351.118 4419.234 -1.06 -99.00 0.00 0.00 0.715 1 0 1 1
SB25600_island_2 SB25600_component_2b J060006-703854 06:00:06.1 -70:38:54 90.025359 -70.648364 0.19 0.24 1367.5 24.719 1.326 24.048 2.986 10.66 8.37 167.45 0.59 0.80 9.41 3.00 0.00 -86.64 5.43 0.00 14.18 5351.118 4419.234 -99.00 -99.00 0.00 0.00 0.715 1 0 1 1
SB25600_island_3 SB25600_component_3a J061931-681533 06:19:31.4 -68:15:33 94.880875 -68.259404 0.49 0.65 1367.5 258.844 72.505 302.138 84.967 11.83 9.04 0.90 0.21 0.18 3.84 4.77 1.40 63.44 1.39 19.02 9.15 1284.683 2467.498 -0.74 -99.00 0.01 0.00 0.277 1 0 1 0
SB25600_island_3 SB25600_component_3b J061931-681531 06:19:31.0 -68:15:31 94.879037 -68.258688 0.21 0.57 1367.5 153.160 84.478 157.853 87.200 11.40 8.28 6.90 0.20 0.21 1.40 4.07 0.00 55.21 1.06 0.00 5.16 1284.683 2467.498 -1.17 -99.00 0.02 0.00 0.277 1 0 1 0
+ + + ``` + + === "csv" + + ``` txt + id,catalogue_id,first_sbid,other_sbids,project_id,island_id,component_id,component_name,ra_hms_cont,dec_dms_cont,ra_deg_cont,dec_deg_cont,ra_err,dec_err,freq,flux_peak,flux_peak_err,flux_int,flux_int_err,maj_axis,min_axis,pos_ang,maj_axis_err,min_axis_err,pos_ang_err,maj_axis_deconv,min_axis_deconv,maj_axis_deconv_err,pos_ang_deconv,min_axis_deconv_err,pos_ang_deconv_err,chi_squared_fit,rms_fit_gauss,spectral_index,spectral_index_err,spectral_curvature,spectral_curvature_err,rms_image,has_siblings,fit_is_estimate,spectral_index_from_tt,flag_c4,comment,quality_level,released_date + 20793260,3790,25600,,18,SB25600_island_2768,SB25600_component_2768a,J055644-690942,05:56:44.5,-69:09:42,89.185558,-69.161889,0.04,0.05,1367.5,0.834,0.009,0.68,0.037,9.63,7.76,165.12,0.23,0.33,3.9,0.0,0.0,0.0,-89.09,0.0,3.48,0.034,52.957,-99.0,0.0,-99.0,0.0,0.166,0,0,,0,,NOT_VALIDATED,2021-04-22T03:48:37.856Z + 20793259,3790,25600,,18,SB25600_island_2767,SB25600_component_2767a,J060047-692500,06:00:47.7,-69:25:00,90.198913,-69.416437,0.09,0.37,1367.5,0.889,0.016,2.642,0.2,29.12,9.35,2.08,2.0,0.99,1.35,26.73,4.84,0.03,2.91,6.03,1.5,1.869,234.431,-99.0,0.0,-99.0,0.0,0.175,0,0,,0,,NOT_VALIDATED,2021-04-22T03:48:37.856Z + 20793258,3790,25600,,18,SB25600_island_2766,SB25600_component_2766a,J055849-690051,05:58:49.4,-69:00:51,89.705808,-69.014253,0.14,0.24,1367.5,0.885,0.025,1.181,0.191,13.04,9.38,6.04,1.3,1.45,8.83,6.87,3.72,3.2,36.6,14.03,34.31,0.561,193.467,-99.0,0.0,-99.0,0.0,0.177,0,0,,0,,NOT_VALIDATED,2021-04-22T03:48:37.856Z + 20793257,3790,25600,,18,SB25600_island_2765,SB25600_component_2765a,J055339-683120,05:53:39.5,-68:31:20,88.414758,-68.522436,0.08,0.1,1367.5,0.906,0.014,0.895,0.063,12.78,7.08,10.0,0.43,0.43,2.33,6.37,0.0,0.54,28.11,0.0,4.65,0.138,99.302,-99.0,0.0,-99.0,0.0,0.173,0,0,,0,,NOT_VALIDATED,2021-04-22T03:48:37.856Z + ``` + + === "txt" + + ``` txt + # island_id component_id component_name ra_hms_cont dec_dms_cont ra_deg_cont dec_deg_cont ra_err dec_err freq flux_peak flux_peak_err flux_int flux_int_err maj_axis min_axis pos_ang maj_axis_err min_axis_err pos_ang_err maj_axis_deconv min_axis_deconv pos_ang_deconv maj_axis_deconv_err min_axis_deconv_err pos_ang_deconv_err chi_squared_fit rms_fit_gauss spectral_index spectral_curvature spectral_index_err spectral_curvature_err rms_image has_siblings fit_is_estimate spectral_index_from_TT flag_c4 comment + # -- -- [deg] [deg] [arcsec] [arcsec] [MHz] [mJy/beam] [mJy/beam] [mJy] [mJy] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] -- [mJy/beam] -- -- -- -- [mJy/beam] + SB10342_island_1000 SB10342_component_1000a B2337-0423 23:37:09.9 -04:23:13 354.291208 -4.387204 0.03 0.02 -0.0 15.907 0.067 19.273 0.112 17.29 13.32 86.15 0.08 0.01 0.66 7.68 2.86 -35.37 0.08 0.72 0.71 57.119 678.701 -0.79 -99.00 0.00 0.00 0.304 0 0 1 0 + SB10342_island_1001 SB10342_component_1001a B0001-0346 00:01:57.6 -03:46:12 0.490036 -3.770075 0.19 0.15 -0.0 12.139 0.425 24.396 0.914 24.14 15.82 61.05 0.45 0.04 2.12 17.96 10.07 50.17 0.06 0.24 4.91 690.545 1639.191 -0.58 -99.00 0.00 0.00 0.339 1 0 1 0 + SB10342_island_1001 SB10342_component_1001b B0001-0346 00:01:57.8 -03:46:04 0.491002 -3.767781 1.13 1.35 -0.0 4.044 0.387 10.072 1.053 50.40 9.39 42.19 3.33 0.04 0.73 48.09 0.00 40.49 0.08 0.00 31.48 690.545 1639.191 -0.36 -99.00 0.00 0.00 0.339 1 0 1 0 + SB10342_island_1002 SB10342_component_1002a B2333-0141 23:33:29.4 -01:41:04 353.372617 -1.684465 0.13 0.17 -0.0 14.516 0.128 47.232 0.561 35.56 17.39 140.82 0.51 0.02 0.52 33.30 7.00 -35.89 0.02 0.39 3.49 926.099 1383.267 -0.53 -99.00 0.00 0.00 0.459 1 0 1 0 + SB10342_island_1002 SB10342_component_1002b B2333-0141 23:33:30.8 -01:41:38 353.378227 -1.693919 0.20 0.26 -0.0 8.051 0.134 25.654 0.550 28.02 21.61 133.92 0.67 0.06 3.37 24.95 14.85 -35.94 0.05 0.13 4.34 926.099 1383.267 -0.21 -99.00 0.00 0.00 0.459 1 0 1 0 + ``` + +### Noise Image File + +This is the noise map that is created from the primary image file during source finding by [`selavy`](https://www.atnf.csiro.au/computing/software/askapsoft/sdp/docs/current/analysis/index.html){:target="_blank"}. + +Must be in the `FITS` file format. + +!!! example "Example CASDA Filename" + ``` + noiseMap.image.i.SB25600.cont.taylor.0.restored.conv.fits + ``` + +### Background Image File + +This is the mean map that is created from the primary image file during source finding by [`selavy`](https://www.atnf.csiro.au/computing/software/askapsoft/sdp/docs/current/analysis/index.html){:target="_blank"}. +The background image files are only required when [source monitoring](../../design/monitor) is enabled in the pipeline run. + +Must be in the `FITS` file format. + +!!! example "Example CASDA Filename" + ``` + meanMap.image.i.SB25600.cont.taylor.0.restored.conv.fits + ``` + +## Data Location + +Data should be placed in the directories denoted by `RAW_IMAGE_DIR` and `HOME_DATA_DIR` in the pipeline configuration. +The `HOME_DATA_DIR` is designed to allow users to upload their own data to their home directory on the system where the pipeline is installed. +By default, the `HOME_DATA_DIR` is set to scan the directory called `vast-pipeline-extra-data` in the users home area. + +Refer to the [Pipeline Configuration](../../gettingstarted/configuration#pipeline) section for more information. \ No newline at end of file diff --git a/docs/using/restorerun.md b/docs/using/restorerun.md new file mode 100644 index 000000000..fbcd1954b --- /dev/null +++ b/docs/using/restorerun.md @@ -0,0 +1,58 @@ +# Restoring a Run + +This page details the process of restoring a pipeline run to the previous successful version. + +When images are added to a run, a backup is made of the run before proceeding which can be used to restore the run to the pre-addition version. +For example, perhaps the wrong images were added or an error occurred mid-addition that could not be resolved. + +A pipeline run can only be restored by the creator or an administrator. + +!!! tip "Admin Tip" + This process can also be launched via the command line using the `restorepiperun` command. + It is described in the admin section [here](../../adminusage/cli/#restorepiperun). + +!!! warning "Warning: One time use" + This process can only be used to restore the run once. + I.e. it is not possible to restore the run to an even earlier version. + +## Step-by-step Guide + +In this example, the `docs_example_run` will be restored to the state before the images were added in the [Adding Images to a Run](addtorun.md) example. + +### 1. Navigate to the Run Detail Page + +Navigate to the detail page of the run you wish to restore. + +![!Pipeline run detail page.](../img/docs-example-run-detail.png){: loading=lazy } + +### 2. Select the Restore Run Option + +Click the `Restore Run` option at the top-right of the page. + +![!Restore run button.](../img/restore-run-button.png){: loading=lazy } + +This will open the restore confirmation modal. + +![!Restore run modal.](../img/restore-run-modal.png){: loading=lazy } + +### 3. Check the Restore Configuration + +Shown in the modal is the configuration file of the previous successful run. +This can be used to check that the images listed here are those that are expected. + +Debug level logging can also be turned on using the toggle button. + +When ready, click the `Restore Run` button on the modal to submit the restore request. +A notification will show to indicate whether the submission was successful. + +![!Restore notification.](../img/restore-notification.png){: loading=lazy } + +### 4. Refresh and Check the Restore Log File + +While restoring the pipeline run will show a status of `Restoring`. +It is possible to check the progress by looking at the Restore Log File which can be found on the run detail page. +The log will not be refreshed automatically and instead the page needs to be manually refreshed. + +Upon a successful restoration the status will be changed back to `Completed`. + +![!Restore log file.](../img/restore-log.png){: loading=lazy } diff --git a/docs/using/runconfig.md b/docs/using/runconfig.md index 859c93f53..864f3bdc3 100644 --- a/docs/using/runconfig.md +++ b/docs/using/runconfig.md @@ -119,6 +119,10 @@ Below is an example of a default `config.yaml` file. Note that no images or othe dec_uncertainty: 1.0 # arcsec variability: + # For each source, calculate the measurement pair metrics (Vs and m) for each unique + # combination of measurements. + pair_metrics: True + # Only measurement pairs where the Vs metric exceeds this value are selected for the # aggregate pair metrics that are stored in Source objects. source_aggregate_pair_metrics_min_abs_vs: 4.3 @@ -141,8 +145,14 @@ Boolean. Astropy warnings are suppressed in the logging output if set to `True`. ### Input Images and Selavy Files +!!! warning "Warning: Entry Order" + The order of the the inputs must be consistent between the different input types. + I.e. if `image1.fits` is the first listed image then `image1_selavy.txt` must be the first selavy input listed. + **`inputs.image`** -Line entries or epoch headed entries. The full paths to the image FITS files to be processed. Also accepts a dictionary format that will activate _epoch mode_ (see [Epoch Based Association](../design/association.md#epoch-based-association)) in which case all inputs must also be in dictionary format. The order of the entries must be consistent with the other input types. +Line entries or epoch headed entries. +The full paths to the image FITS files to be processed - these can be regular FITS files, or FITS files that use a [`CompImageHDU`](https://docs.astropy.org/en/stable/io/fits/api/images.html#astropy.io.fits.CompImageHDU). In principle the pipeline also supports [`.fits.fz`](https://heasarc.gsfc.nasa.gov/fitsio/fpack/) files, although this is not officially supported. Epoch mode is activated by including an extra key value with the epoch name, see the example below for a demonstration. +Refer to [this section](../design/association.md#epoch-based-association) of the documentation for more information on epoch based association. !!! example "config.yaml" @@ -167,10 +177,27 @@ Line entries or epoch headed entries. The full paths to the image FITS files to epoch02: - /full/path/to/image3.fits ``` + Make sure that the epoch names are orderable in the correct order, for example, use: + ``` + epoch01: + ... + epoch09: + epoch10: + ``` + and not: + ``` + epoch1: + ... + epoch9: + epoch10: + ``` **`inputs.selavy`** -Line entries or epoch headed entries. The full paths to the selavy text files to be processed. Also accepts a dictionary format that will activate _epoch mode_ (see [Epoch Based Association](../design/association.md#epoch-based-association)) in which case all inputs must also be in dictionary format. The order of the entries must be consistent with the other input types. +Line entries or epoch headed entries. +The full paths to the selavy text files to be processed. +Epoch mode is activated by including an extra key value with the epoch name, see the example below for a demonstration. +Refer to [this section](../design/association.md#epoch-based-association) of the documentation for more information on epoch based association. !!! example "config.yaml" @@ -195,10 +222,27 @@ Line entries or epoch headed entries. The full paths to the selavy text files to epoch02: - /full/path/to/image3_selavy.txt ``` + Make sure that the epoch names are orderable in the correct order, for example, use: + ``` + epoch01: + ... + epoch09: + epoch10: + ``` + and not: + ``` + epoch1: + ... + epoch9: + epoch10: + ``` **`inputs.noise`** -Line entries or epoch headed entries. The full paths to the image noise (RMS) FITS files to be processed. Also accepts a dictionary format that will activate _epoch mode_ (see [Epoch Based Association](../design/association.md#epoch-based-association)) in which case all inputs must also be in dictionary format. The order of the entries must be consistent with the other input types. +Line entries or epoch headed entries. +The full paths to the image noise (RMS) FITS files to be processed. +Epoch mode is activated by including an extra key value with the epoch name, see the example below for a demonstration. +Refer to [this section](../design/association.md#epoch-based-association) of the documentation for more information on epoch based association. !!! example "config.yaml" @@ -223,10 +267,28 @@ Line entries or epoch headed entries. The full paths to the image noise (RMS) FI epoch02: - /full/path/to/image3_rms.fits ``` + Make sure that the epoch names are orderable in the correct order, for example, use: + ``` + epoch01: + ... + epoch09: + epoch10: + ``` + and not: + ``` + epoch1: + ... + epoch9: + epoch10: + ``` **`inputs.background`** -Line entries or epoch headed entries. The full paths to the image background (mean) FITS files to be processed. Also accepts a dictionary format that will activate _epoch mode_ (see [Epoch Based Association](../design/association.md#epoch-based-association)) in which case all inputs must also be in dictionary format. The order of the entries must be consistent with the other input types. Only required to be defined if `source_monitoring.monitor` is set to `True`. +Line entries or epoch headed entries. +The full paths to the image background (mean) FITS files to be processed. +Epoch mode is activated by including an extra key value with the epoch name, see the example below for a demonstration. +Refer to [this section](../design/association.md#epoch-based-association) of the documentation for more information on epoch based association. +The background images are only required to be defined if `source_monitoring.monitor` is set to `True`. !!! example "config.yaml" @@ -251,6 +313,20 @@ Line entries or epoch headed entries. The full paths to the image background (me epoch02: - /full/path/to/image3_bkg.fits ``` + Make sure that the epoch names are orderable in the correct order, for example, use: + ``` + epoch01: + ... + epoch09: + epoch10: + ``` + and not: + ``` + epoch1: + ... + epoch9: + epoch10: + ``` #### Using glob expressions @@ -400,12 +476,13 @@ Float. Value to substitute for the `local_rms` parameter in selavy extractions i Boolean. When `True` then two `arrow` format files are produced: * `measurements.arrow` - an arrow file containing all the measurements associated with the run. -* `measurement_pairs.arrow` - an arrow file containing the measurement pairs information pre-merged with extra information from the measurements. +* `measurement_pairs.arrow` - an arrow file containing the measurement pairs information pre-merged with extra information from the measurements. Only output if `variability.pair_metrics` is also set to `True`. Producing these files for large runs (200+ images) is recommended for post-processing. Defaults to `False`. !!! note - The arrow files can optionally be produced after the run has completed by an administrator. + The arrow files can optionally be produced after the run has completed. + See the [Generating Arrow Files page](genarrow.md). **`measurements.ra_uncertainty`** Float. Defines an uncertainty error to the RA that will be added in quadrature to the existing source extraction error. Used to represent a systematic positional error. Unit is arcseconds. Defaults to 1.0. @@ -415,5 +492,8 @@ Float. Defines an uncertainty error to the Dec that will be added in quadrature ### Variability +**`variability.pair_metrics`** +Boolean. When `True` then the two-epoch metrics are calculated for each source. It is recommended that users set this to `False` to skip calculating the pair metrics for runs that contain many input images per source. Defaults to `True`. + **`variability.source_aggregate_pair_metrics_min_abs_vs`** Float. Defines the minimum $V_s$ two-epoch metric value threshold used to attach the most significant pair value to the source. Defaults to `4.3`. diff --git a/gulpfile.js b/gulpfile.js index 2f337052d..a998ca88f 100644 --- a/gulpfile.js +++ b/gulpfile.js @@ -105,6 +105,7 @@ function js9Config(bc) { "wcs", "zoom" ] + js9Config.globalOpts.updateTitlebar = false js9Config.textColorOpts = { "info": "#000064" } let outConfig = 'var JS9Prefs = ' + JSON.stringify(js9Config) @@ -129,13 +130,18 @@ function js9MoveGif() { } function js9FixStaticUrl(bc) { - const result = require('dotenv').config({ 'path': './webinterface/.env' }) - if (result.error) { - throw result.error + // If STATIC_URL isn't in the env, load it from the .env file. We also need BASE_URL + // but since it's optional it may not be set. + if (process.env.STATIC_URL === undefined) { + const result = require('dotenv').config({ 'path': './webinterface/.env' }) + // note that .config automatically copies the env vars to process.env + if (result.error) { + throw result.error + } } - let base_url = result.parsed.BASE_URL || null, - static_url = result.parsed.STATIC_URL || '/static/', - fileContent = fs.readFileSync(paths.js9Target + '/js9prefs.js', 'utf8') + let base_url = process.env.BASE_URL || null + let static_url = process.env.STATIC_URL || '/static/' + let fileContent = fs.readFileSync(paths.js9Target + '/js9prefs.js', 'utf8') let serving_url = (base_url) ? '/' + base_url.split('/').join('') + '/' + static_url.split('/').join('') + '/' : static_url return fs.writeFile( paths.js9Target + '/js9prefs.js', @@ -168,7 +174,7 @@ function clean() { // Bring third party dependencies from node_modules into vendor directory function modules() { - // Bootstrap JS + // Bootstrap JS var bootstrapJS = gulp.src('./node_modules/startbootstrap-sb-admin-2/vendor/bootstrap/js/*') .pipe(gulp.dest(paths.vendor + '/bootstrap/js')); @@ -181,6 +187,8 @@ function modules() { // Bokeh var bokehJS = gulp.src('./node_modules/@bokeh/bokehjs/build/js/bokeh.min.js') .pipe(gulp.dest(paths.vendor + '/bokehjs')); + var bokehJSwidgets = gulp.src('./node_modules/@bokeh/bokehjs/build/js/bokeh-widgets.min.js') + .pipe(gulp.dest(paths.vendor + '/bokehjs')); // SB Admin 2 Bootstrap template var bootstrapSbAdmin2 = gulp.src([ @@ -258,7 +266,7 @@ function modules() { var prismJsLineNumCss = gulp.src('./node_modules/prismjs/plugins/line-numbers/prism-line-numbers.css') .pipe(gulp.dest(paths.vendor + '/prismjs/line-numbers')); - return merge(bootstrapJS, bootstrapSbAdmin2, bootstrap4toggle, bokehJS, dataTables, dataTablesButtons, fontAwesome, jquery, jqueryEasing, jszip, d3Celestial, d3CelestialData, d3CelestialImage, particlesJs, prismJs, prismJsYaml, prismJsLineNum, prismJsCss, prismJsLineNumCss); + return merge(bootstrapJS, bootstrapSbAdmin2, bootstrap4toggle, bokehJS, bokehJSwidgets, dataTables, dataTablesButtons, fontAwesome, jquery, jqueryEasing, jszip, d3Celestial, d3CelestialData, d3CelestialImage, particlesJs, prismJs, prismJsYaml, prismJsLineNum, prismJsCss, prismJsLineNumCss); } // SCSS task diff --git a/mkdocs.yml b/mkdocs.yml index 84ba0b0e4..6ae38b6a3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -3,6 +3,7 @@ site_description: >- VAST Pipeline platform to enable processing of astronomical sources extracted from the ASKAP telescope for radio transient detection science site_author: VAST Development Team +site_url: https://vast-survey.org/vast-pipeline/ copyright: Copyright © 2020 - 2025 Vast Development Team, University of Sydney - all right reserved repo_url: https://github.com/askap-vast/vast-pipeline @@ -54,7 +55,8 @@ markdown_extensions: - pymdownx.caret - pymdownx.details - pymdownx.inlinehilite - - pymdownx.tabbed + - pymdownx.tabbed: + alternate_style: true - pymdownx.superfences - pymdownx.highlight - pymdownx.tabbed @@ -73,19 +75,24 @@ nav: - Configuration: gettingstarted/configuration.md - Deployment: gettingstarted/deployment.md - Using the Pipeline: + - Required Data: using/requireddata.md - Accessing the Pipeline: using/access.md - Initialising a Run: using/initrun.md - Run Configuration: using/runconfig.md - Processing a Run: using/processrun.md - Adding Images to a Run: using/addtorun.md + - Restoring a Run: using/restorerun.md - Deleting a Run: using/deleterun.md + - Generating Arrow Files: using/genarrow.md - Exploring the Pipeline Website: - - Overview: exploringwebsite/websiteoverview.md + - Website Overview: exploringwebsite/websiteoverview.md - DataTables: exploringwebsite/datatables.md - Pipeline Run Pages: exploringwebsite/runpages.md - - Source Pages: exploringwebsite/sourcepages.md + - Source Query: exploringwebsite/sourcequery.md + - Source η-V Analysis: exploringwebsite/sourceanalysis.md + - Source Detail: exploringwebsite/sourcedetail.md - Source Tags & Favourites: exploringwebsite/sourcetagsfavs.md - - Measurement Pages: exploringwebsite/measurementpages.md + - Measurement Detail Page: exploringwebsite/measurementdetail.md - Image Pages: exploringwebsite/imagepages.md - Pipeline Outputs: - Outputs Overview: outputs/outputs.md @@ -93,6 +100,7 @@ nav: - Column Descriptions: outputs/coldesc.md - Admin Usage: - CLI: adminusage/cli.md + - Standalone Image Ingestion: adminusage/ingestimages.md - Web App: adminusage/app.md - Web App Admin Tools: exploringwebsite/admintools.md - Features & Design: @@ -130,6 +138,8 @@ nav: - clearpiperun.py: reference/management/commands/clearpiperun.md - createmeasarrow.py: reference/management/commands/createmeasarrow.md - debugrun.py: reference/management/commands/debugrun.md + - ingestimages.py: reference/management/commands/ingestimages.md + - initingest.py: reference/management/commands/initingest.md - initpiperun.py: reference/management/commands/initpiperun.md - restorepiperun.py: reference/management/commands/restorepiperun.md - runpipeline.py: reference/management/commands/runpipeline.md @@ -144,11 +154,13 @@ nav: - main.py: reference/pipeline/main.md - model_generator.py: reference/pipeline/model_generator.md - new_sources.py: reference/pipeline/new_sources.md + - pairs.py: reference/pipeline/pairs.md - utils.py: reference/pipeline/utils.md - survey: - translators.py: reference/survey/translators.md - utils: - auth.py: reference/utils/auth.md + - external_query.py: reference/utils/external_query.md - unit_tags.py: reference/utils/unit_tags.md - utils.py: reference/utils/utils.md - view.py: reference/utils/view.md @@ -159,15 +171,18 @@ nav: - forms.py: reference/forms.md - models.py: reference/models.md - plots.py: reference/plots.md - # - serializers.py: reference/serializers.md + - serializers.py: reference/serializers.md + - signals.py: reference/signals.md - urls.py: reference/urls.md - # - views.py: reference/views.md + - views.py: reference/views.md plugins: - search - git-revision-date-localized: fallback_to_build_date: true enable_creation_date: true + exclude: + - index.md - gen-files: scripts: # - docs/gen_credits.py @@ -201,3 +216,5 @@ extra: name: vast-pipeline source on GitHub - icon: fontawesome/solid/globe link: https://vast-survey.org + version: + provider: mike diff --git a/package-lock.json b/package-lock.json index b82686c7f..cae7c54e4 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,14 +1,15 @@ { "name": "vast-pipeline", - "version": "1.0.0", + "version": "1.1.0", "lockfileVersion": 2, "requires": true, "packages": { "": { - "version": "1.0.0", + "name": "vast-pipeline", + "version": "1.1.0", "license": "MIT", "dependencies": { - "@bokeh/bokehjs": "2.3.0", + "@bokeh/bokehjs": "^2.4.2", "@fortawesome/fontawesome-free": "5.12.0", "bootstrap-select": "^1.13.18", "bootstrap4-toggle": "3.6.1", @@ -19,9 +20,9 @@ "jquery": "^3.6.0", "jquery.easing": "^1.4.1", "js9": "^3.4.0", - 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hash = "sha256:5dba5f530fec3f0988d83b78cc591b58c0b6eb8431a85edd1569a0539a8a5a0e"}, -] +lock-version = "2.0" +python-versions = ">=3.8.0,<3.11" +content-hash = "8153f316f4042aacea8eacc6fb2781de953a53d248905257f9aa10272272c752" diff --git a/pyproject.toml b/pyproject.toml index dac6c4e77..149f5a6e2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,32 +1,32 @@ [tool.poetry] name = "vast-pipeline" -version = "1.0.0" +version = "1.1.0" description = "Vast Pipeline code base for processing and analysing telescope images from the Square Kilometre Pathfinder" authors = ["The VAST Development Team"] license = "MIT" [tool.poetry.dependencies] -python = "^3.7.1" -astropy = "^4.0" -astroquery = "^0.4.0" -bokeh = "2.3.0" # must align with @bokeh/bokehjs version in package.json +python = ">=3.8.0,<3.11" +astropy = "^5.0" +astroquery = "^0.4.4" +bokeh = "2.4.2" # must align with @bokeh/bokehjs version in package.json cloudpickle = "^1.5.0" -dask = {extras = ["dataframe"], version = "^2021.3.0"} +dask = {extras = ["dataframe"], version = "^2022.1.0,<2022.4.2"} dill = "^0.3.3" -Django = "^3.1.7" +Django = "^3.2.13" django-crispy-forms = "^1.9.2" django-environ = "^0.4.5" djangorestframework = "^3.11.0" djangorestframework-datatables = "0.5.1" django-q = "^1.3.3" django-tagulous = "^1.1.0" -jinja2 = "^2.11.2" +Jinja2 = "^3.0.3" networkx = "^2.4" -numpy = "^1.18.1" +numpy = ">=1.18.1,<1.23" pandas = "^1.2.0" psutil = "^5.7.0" psycopg2 = "^2.8.4" -pyarrow = "^3.0.0" +pyarrow = "^7.0" scipy = "^1.6.0" social-auth-app-django = "^3.1.0" social-auth-core = "^3.3.3" @@ -36,31 +36,45 @@ gevent = { version = "^21.1.2", optional = true } gunicorn = { version = "^20.0.4", optional = true } forced-phot = { git = "https://github.com/askap-vast/forced_phot.git" } strictyaml = "^1.3.2" +colorcet = "^2.0.6" +matplotlib = "^3.5.0" +holoviews = "^1.14.7" +datashader = "^0.14" +channels = "^3.0.4" +param = "<2.0.0" [tool.poetry.dev-dependencies] -mkdocs-material = "^7.1.4" -mkdocstrings = "^0.15.0" -mkdocs-minify-plugin = "^0.4.0" +mkdocs-material = "^9.4" +mkdocstrings = {extras = ["python"], version = "^0.25.0"} +mkdocs-minify-plugin = "^0" mkdocs-material-extensions = "^1.0.1" -mkdocs-gen-files = "^0.3.1" +mkdocs-gen-files = "^0" pygraphviz = "^1.7" ipdb = "^0.13.6" -ipython = "^7.21.0" +ipython = "^7.31.1" pip-tools = "^5.5.0" tabulate = "^0.8.9" django-debug-toolbar = "^3.2" flake8 = "^3.9.0" -mypy = "^0.812" +mypy = "^0.910" black = "^20.8b1" django-extensions = "^3.1.1" rope = "^0.18.0" -django-stubs = "^1.7.0" -lightgallery = "^0.5" -mkdocs-git-revision-date-localized-plugin = "^0.9" +django-stubs = "^1.9.0" +lightgallery = "^0" +mkdocs-git-revision-date-localized-plugin = "^0" +mike = "^1.1.2" +markdown = "3.3.7" [tool.poetry.extras] prod = ["gevent", "gunicorn"] +[tool.mypy] +plugins = ["mypy_django_plugin.main"] + +[tool.django-stubs] +django_settings_module = "webinterface.settings" + [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/static/js/aladin-script.js b/static/js/aladin-script.js index 6d4f08f58..c792a2213 100644 --- a/static/js/aladin-script.js +++ b/static/js/aladin-script.js @@ -10,58 +10,58 @@ function configureAladin(aladin, aladinConf) { var nvss = aladin.createImageSurvey('NVSS', 'NVSS', 'https://alasky.u-strasbg.fr/NVSS/intensity/', 'equatorial', 5, {imgFormat: 'jpg'}); var racs = aladin.createImageSurvey('RACS', 'RACS', 'https://www.atnf.csiro.au/research/RACS/RACS_I1/', 'equatorial', 8, {imgFormat: 'png'}); var vast_epoch_01 = aladin.createImageSurvey( - 'VAST Pilot 1', 'VAST Pilot 1', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST1_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 01', 'VAST Pilot 01', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST1_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_01_v = aladin.createImageSurvey( - 'VAST Pilot 1 (Stokes V)', 'VAST Pilot 1 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST1_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 01 (Stokes V)', 'VAST Pilot 01 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST1_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_02 = aladin.createImageSurvey( - 'VAST Pilot 2', 'VAST Pilot 2', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST2_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 02', 'VAST Pilot 02', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST2_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_02_v = aladin.createImageSurvey( - 'VAST Pilot 2 (Stokes V)', 'VAST Pilot 2 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST2_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 02 (Stokes V)', 'VAST Pilot 02 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST2_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_03x = aladin.createImageSurvey( - 'VAST Pilot 3x', 'VAST Pilot 3x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST3x_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 03x', 'VAST Pilot 03x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST3x_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_03x_v = aladin.createImageSurvey( - 'VAST Pilot 3x (Stokes V)', 'VAST Pilot 3x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST3x_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 03x (Stokes V)', 'VAST Pilot 03x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST3x_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_04x = aladin.createImageSurvey( - 'VAST Pilot 4x', 'VAST Pilot 4x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST4x_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 04x', 'VAST Pilot 04x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST4x_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_04x_v = aladin.createImageSurvey( - 'VAST Pilot 4x (Stokes V)', 'VAST Pilot 4x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST4x_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 04x (Stokes V)', 'VAST Pilot 04x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST4x_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_05x = aladin.createImageSurvey( - 'VAST Pilot 5x', 'VAST Pilot 5x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST5x_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 05x', 'VAST Pilot 05x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST5x_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_05x_v = aladin.createImageSurvey( - 'VAST Pilot 5x (Stokes V)', 'VAST Pilot 5x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST5x_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 05x (Stokes V)', 'VAST Pilot 05x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST5x_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_06x = aladin.createImageSurvey( - 'VAST Pilot 6x', 'VAST Pilot 6x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST6x_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 06x', 'VAST Pilot 06x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST6x_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_06x_v = aladin.createImageSurvey( - 'VAST Pilot 6x (Stokes V)', 'VAST Pilot 6x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST6x_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 06x (Stokes V)', 'VAST Pilot 06x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST6x_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_07x = aladin.createImageSurvey( - 'VAST Pilot 7x', 'VAST Pilot 7x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST7x_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 07x', 'VAST Pilot 07x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST7x_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_07x_v = aladin.createImageSurvey( - 'VAST Pilot 7x (Stokes V)', 'VAST Pilot 7x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST7x_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 07x (Stokes V)', 'VAST Pilot 07x (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST7x_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_08 = aladin.createImageSurvey( - 'VAST Pilot 8', 'VAST Pilot 8', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST8_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 08', 'VAST Pilot 08', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST8_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_08_v = aladin.createImageSurvey( - 'VAST Pilot 8 (Stokes V)', 'VAST Pilot 8 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST8_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 08 (Stokes V)', 'VAST Pilot 08 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST8_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_09 = aladin.createImageSurvey( - 'VAST Pilot 9', 'VAST Pilot 9', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST9_I4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 09', 'VAST Pilot 09', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST9_I4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_09_v = aladin.createImageSurvey( - 'VAST Pilot 9 (Stokes V)', 'VAST Pilot 9 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST9_V4', 'equatorial', 6, {imgFormat: 'png'} + 'VAST Pilot 09 (Stokes V)', 'VAST Pilot 09 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST9_V4', 'equatorial', 6, {imgFormat: 'png'} ); var vast_epoch_10x = aladin.createImageSurvey( 'VAST Pilot 10x', 'VAST Pilot 10x', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST10x_I4', 'equatorial', 6, {imgFormat: 'png'} @@ -81,6 +81,42 @@ function configureAladin(aladin, aladinConf) { var vast_epoch_12_v = aladin.createImageSurvey( 'VAST Pilot 12 (Stokes V)', 'VAST Pilot 12 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST12_V4', 'equatorial', 6, {imgFormat: 'png'} ); + var vast_epoch_13 = aladin.createImageSurvey( + 'VAST Pilot 13', 'VAST Pilot 13', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST13_I4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_13_v = aladin.createImageSurvey( + 'VAST Pilot 13 (Stokes V)', 'VAST Pilot 13 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST13_V4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_17 = aladin.createImageSurvey( + 'VAST Pilot 17', 'VAST Pilot 17', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST17_I4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_17_v = aladin.createImageSurvey( + 'VAST Pilot 17 (Stokes V)', 'VAST Pilot 17 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST17_V4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_18 = aladin.createImageSurvey( + 'VAST Pilot 18', 'VAST Pilot 18', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST18_I4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_18_v = aladin.createImageSurvey( + 'VAST Pilot 18 (Stokes V)', 'VAST Pilot 18 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST18_V4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_19 = aladin.createImageSurvey( + 'VAST Pilot 19', 'VAST Pilot 19', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST19_I4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_19_v = aladin.createImageSurvey( + 'VAST Pilot 19 (Stokes V)', 'VAST Pilot 19 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST19_V4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_20 = aladin.createImageSurvey( + 'VAST Pilot 20', 'VAST Pilot 20', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST20_I4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_20_v = aladin.createImageSurvey( + 'VAST Pilot 20 (Stokes V)', 'VAST Pilot 20 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST20_V4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_21 = aladin.createImageSurvey( + 'VAST Pilot 21', 'VAST Pilot 21', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST21_I4', 'equatorial', 6, {imgFormat: 'png'} + ); + var vast_epoch_21_v = aladin.createImageSurvey( + 'VAST Pilot 21 (Stokes V)', 'VAST Pilot 21 (Stokes V)', 'https://www.atnf.csiro.au/people/Emil.Lenc/HiPS/VAST21_V4', 'equatorial', 6, {imgFormat: 'png'} + ); let survey = racs; aladin.setImageSurvey(survey); aladin.getBaseImageLayer().getColorMap().reverse(); diff --git a/static/js/datatables-pipeline.js b/static/js/datatables-pipeline.js index 9d7e43224..23b01a4fc 100644 --- a/static/js/datatables-pipeline.js +++ b/static/js/datatables-pipeline.js @@ -1,3 +1,9 @@ +let DEFAULT_DATATABLE_BUTTONS = [ + {extend: 'colvis', className: 'btn-info btn-sm'}, + {extend: 'csv', className: 'btn-info btn-sm'}, + {extend: 'excel', className: 'btn-info btn-sm'}, +] + // Formatting function for API function obj_formatter(obj) { if (obj.render.hasOwnProperty('url')) { @@ -43,23 +49,73 @@ function obj_formatter(obj) { }; }; +function drawExternalResultsTable(id, buttons = DEFAULT_DATATABLE_BUTTONS) { + $(id) + .DataTable({ + "searching": false, + "pageLength": 15, + "lengthChange": false, + // need a different dom for external results table + "dom": ( + "<'row'<'col-sm-2'l><'col-sm-8 text-center'B><'col-sm-2'f>>" + + "<'row'<'col-sm-12'tr>>" + + "<'row'<'col-sm-5'i><'col-sm-7'p>>" + ), + "buttons": buttons, + "order": [[1, "asc"]], + "columnDefs": [ + { + "targets": 0, + "render": function( data, type, row, meta) { + if (row["database"] === "SIMBAD") { + return '' + row['object_name'] + ' (' + row['database'] + ')' + } else if (row["database"] == "NED") { + return '' + row['object_name'] + ' (' + row['database'] + ')' + } else if (row["database"] == "TNS") { + return '' + row['object_name'] + ' (' + row['database'] + ')' + } else { + return row['object_name'] + ' (' + row['database'] + ')' + } + } + }, + { + "targets": 1, + "render": function ( data, type, row, meta ) { + return row['separation_arcsec'].toFixed(2); + } + }, + { + "targets": 2, + "render": function ( data, type, row, meta ) { + if (row['otype_long'] !== "" && row['otype'] !== row['otype_long']) { + return '' + row['otype'] + ''; + } else { + return row['otype']; + } + } + } + ] + }); + $(id).DataTable.ext.pager.numbers_length = 3; +} + // Call the dataTables jQuery plugin $(document).ready(function() { $('[data-toggle="tooltip"]').tooltip(); - let buttons = [ - {extend: 'colvis', className: 'btn-info btn-sm'}, - {extend: 'csv', className: 'btn-info btn-sm'}, - {extend: 'excel', className: 'btn-info btn-sm'} - ] let dom = ( "<'row'<'col-sm-3'l><'col-sm-6 text-center'B><'col-sm-3'f>>" + "<'row'<'col-sm-12'tr>>" + "<'row'<'col-sm-5'i><'col-sm-7'p>>" ) - let dataConfParsed = JSON.parse(document.getElementById('datatable-conf').textContent); - let dataConfList = (Array.isArray(dataConfParsed)) ? dataConfParsed : [dataConfParsed]; + let dataConfElement = document.getElementById('datatable-conf'); + let dataConfList = []; + let dataConfParsed; + if (dataConfElement !== null) { + dataConfParsed = JSON.parse(dataConfElement.textContent); + dataConfList = (Array.isArray(dataConfParsed)) ? dataConfParsed : [dataConfParsed]; + } for (let dataConf of dataConfList){ let table_id = (dataConfList.length == 1) ? '#dataTable' : '#' + dataConf.table_id; if (dataConf.hasOwnProperty('api')) { @@ -74,6 +130,14 @@ $(document).ready(function() { bFilter: dataConf.search, hover: true, serverSide: true, + processing: true, + language: { + processing: ( + '' + ) + }, ajax: { url: dataConf.api, data: function (data) { @@ -88,8 +152,16 @@ $(document).ready(function() { order: dataConf.order, searchDelay: 2000, dom : dom, - buttons: buttons + buttons: DEFAULT_DATATABLE_BUTTONS }; + // apply deferLoading config, if supplied + if (dataConf.hasOwnProperty('deferLoading')) { + dataTableConf.deferLoading = dataConf.deferLoading; + // change the message printed in the empty table if deferLoading active + dataTableConf.initComplete = function(settings, json) { + $("td.dataTables_empty").text("Submit a query to view results"); + } + } } else { // expect that there is a 'data' attribute with the data // data are in this format @@ -114,7 +186,7 @@ $(document).ready(function() { data: dataSet, order: dataConf.order, dom: dom, - buttons: buttons + buttons: DEFAULT_DATATABLE_BUTTONS }; if (dataConf.table == 'source_detail') { let tableElement = document.getElementById(table_id.replace('#', '')), @@ -243,59 +315,7 @@ $(document).ready(function() { } // Simbad and NED search results tables - $('#externalResultsTable') - /* - .on("xhr.dt", function(e, settings, json, xhr) { - console.log(json); - if (json === null) { - return true; - } - }) - */ - .DataTable({ - "searching": false, - "pageLength": 15, - "lengthChange": false, - // need a different dom for external results table - "dom": ( - "<'row'<'col-sm-2'l><'col-sm-8 text-center'B><'col-sm-2'f>>" + - "<'row'<'col-sm-12'tr>>" + - "<'row'<'col-sm-5'i><'col-sm-7'p>>" - ), - "buttons": buttons, - "order": [[1, "asc"]], - "columnDefs": [ - { - "targets": 0, - "render": function( data, type, row, meta) { - if (row["database"] === "SIMBAD") { - return '' + row['object_name'] + ' (' + row['database'] + ')' - } else if (row["database"] == "NED") { - return '' + row['object_name'] + ' (' + row['database'] + ')' - } else { - return row['object_name'] + ' (' + row['database'] + ')' - } - } - }, - { - "targets": 1, - "render": function ( data, type, row, meta ) { - return row['separation_arcsec'].toFixed(2); - } - }, - { - "targets": 2, - "render": function ( data, type, row, meta ) { - if (row['otype_long'] !== "" && row['otype'] !== row['otype_long']) { - return '' + row['otype'] + ''; - } else { - return row['otype']; - } - } - } - ] - }); - $('#externalResultsTable').DataTable.ext.pager.numbers_length = 3; + drawExternalResultsTable('#externalResultsTable'); // Trigger the update search on the datatable $("#catalogSearch").on('click', function(e) { @@ -494,6 +514,11 @@ $(document).ready(function() { }; table.ajax.url(qry_url); table.ajax.reload(); + + // activate eta-V plot button + let etaBtn = document.getElementById('etaAnalysisButton'); + etaBtn.classList.remove('disabled') + etaBtn.setAttribute('aria-disabled', false); }); // Trigger the search reset on the datatable @@ -529,6 +554,11 @@ $(document).ready(function() { $("#coordInput").removeClass(["is-valid", "is-invalid"]); table.ajax.url(dataConfParsed.api); table.ajax.reload(); + + // deactivate eta-V plot button + let etaBtn = document.getElementById('etaAnalysisButton'); + etaBtn.classList.add('disabled') + etaBtn.setAttribute('aria-disabled', true); }); // Object name resolver diff --git a/static/js/run-detail.js b/static/js/run-detail.js index d1fc3d8e8..9bf7eeb95 100644 --- a/static/js/run-detail.js +++ b/static/js/run-detail.js @@ -40,4 +40,55 @@ $(document).ready(function() { document.getElementById('runConfigForm').submit(); }); + // load logs on page load + var logfile = document.getElementById('logs').value; + if (!!logfile) { + loadlog(logfile, 'runlog'); + } + + var logfile = document.getElementById('restorelogs').value; + if (!!logfile) { + loadlog(logfile, 'restorelog'); + } + + var logfile = document.getElementById('genarrowlogs').value; + if (!!logfile) { + loadlog(logfile, 'genarrowlog'); + } + + // load logs on selection change + $('#logs').on('change', function () { + let logfile = document.getElementById('logs').value; + loadlog(logfile, 'runlog'); + }); + + $('#restorelogs').on('change', function () { + let logfile = document.getElementById('restorelogs').value; + loadlog(logfile, 'restorelog'); + }); + + $('#genarrowlogs').on('change', function () { + let logfile = document.getElementById('genarrowlogs').value; + loadlog(logfile, 'genarrowlog'); + }); }); + + +function loadlog(logfile, type) { + // Function to load a run log file + let apiUrl = document.getElementById('runDetailScript').getAttribute('apiRunLogUrl'); + + $.ajax({ + type: "GET", + url: apiUrl + "?logname="+ logfile, + success: function (data) { + if (type == 'runlog') { + $("#logtext").html(data.log_html_content); + } else if (type == 'restorelog') { + $("#restorelogtext").html(data.log_html_content); + } else if (type == 'genarrowlog') { + $("#genarrowlogtext").html(data.log_html_content); + } + } + }); +}; diff --git a/static/js/source-fav.js b/static/js/source-fav.js new file mode 100644 index 000000000..6d46a9b31 --- /dev/null +++ b/static/js/source-fav.js @@ -0,0 +1,20 @@ +function add_source_to_favs(e, url) { + $.post(url, e.serialize(), function(data) { + if (data.messages) { + showPopupMessage(data.messages); + } + if (data.success) { + $('#favStarButton').find("i").css({ + "color": "#ffe271" + }); + }; + }); + $('#starSource').modal('hide'); +} + +let apiUrl = document.getElementById('SourceFavScript').getAttribute('apiSourceFavUrl'); + +$(document).on('submit', '#commentStarForm', function(event){ + add_source_to_favs($(this), apiUrl); + event.preventDefault(); +}); diff --git a/static/js/toasts.js b/static/js/toasts.js index b8f1b5f51..e396bcb71 100644 --- a/static/js/toasts.js +++ b/static/js/toasts.js @@ -1,3 +1,12 @@ +// This function is used by ajax calls to show toast messages +function showPopupMessage(content) { + var elMessages = $('#messages-list'); + if (elMessages.length && content) { + elMessages.html(content); + } + $(".toast").toast('show'); +} + $(document).ready(function() { $(".toast").toast('show'); }); diff --git a/templates/base.html b/templates/base.html index 502057b9c..39ee5b39e 100644 --- a/templates/base.html +++ b/templates/base.html @@ -11,7 +11,9 @@ + {% block title %} VAST Pipeline + {% endblock title %} @@ -75,14 +77,6 @@ - -
  • - -
    • -
    • - +
  • diff --git a/templates/generic_table.html b/templates/generic_table.html index b7e29dcc4..be610307a 100644 --- a/templates/generic_table.html +++ b/templates/generic_table.html @@ -228,15 +228,15 @@
    - Note that this is only applied to new images that are not already present in the database, and to forced extractions." >
    - Note that this is only applied to new images that are not already present in the database, and to forced extractions." >
    @@ -259,8 +259,8 @@
    - Note that this is only applied to new images that are not already present in the database." > @@ -271,12 +271,21 @@
    +
    +
    + + + +
    +
    - Note that this is only applied to new images that are not already present in the database." >
    diff --git a/templates/image_detail.html b/templates/image_detail.html index c1e41de4a..6c0ed3162 100644 --- a/templates/image_detail.html +++ b/templates/image_detail.html @@ -4,6 +4,10 @@ {% load humanize %} {% load unit_tags %} +{% block title %} +VAST Pipeline: Image ID {{ image.id }} +{% endblock title %} + {% block head %} diff --git a/templates/index.html b/templates/index.html index 563bd53f7..b0b26db93 100644 --- a/templates/index.html +++ b/templates/index.html @@ -33,15 +33,14 @@

    Welcome back!

    - + -
    +
    -
    Total Nr Pipeline Runs
    -
    {{ totals.nr_pruns|intcomma }}
    +
    Go to Pipeline Runs
    @@ -52,64 +51,44 @@

    Welcome back!

    - -
    -
    -
    -
    -
    -
    Total Nr Images
    -
    {{ totals.nr_imgs|intcomma }}
    -
    -
    - -
    + +
    +
    +
    +
    +
    +
    Go to Source Query
    - -
    -
    -
    - - -
    -
    -
    -
    -
    -
    Total Nr Measurements
    -
    {{ totals.nr_meas|intcomma }}
    -
    -
    - -
    +
    +
    -
    +
    +
    - - -
    -
    + + +
    +
    -
    Total Nr Sources
    -
    {{ totals.nr_srcs|intcomma }}
    +
    Go to Images
    - +
    - +
    - +
    -
    +
    @@ -127,32 +106,8 @@
    Welcome to the VAST Pipeline!
    - - -
    -
    -
    -
    Current Sky Regions
    -
    -
    -
    -
    -
    -
    -
    {% endblock content %} - -{% block custom_page_scripts %} - - -{{ d3_celestial_skyregions|json_script:"skyregion-data" }} - - - - - -{% endblock custom_page_scripts %} diff --git a/templates/measurement_detail.html b/templates/measurement_detail.html index 0f3c209f8..d95d97d1b 100644 --- a/templates/measurement_detail.html +++ b/templates/measurement_detail.html @@ -4,6 +4,10 @@ {% load unit_tags %} +{% block title %} +VAST Pipeline: Measurement ID {{ measurement.id }} +{% endblock title %} + {% block head %} @@ -322,7 +326,7 @@
    addBaseUrl = (url) => baseUrl == null ? url : '/' + baseUrl + url; var measurement = JSON.parse(document.getElementById("data-js9").textContent); JS9.Load( - addBaseUrl('/cutout/' + measurement.name + '/large'), { + addBaseUrl('/cutout/' + measurement.id + '/large'), { scalemin: -0.001, scalemax: 0.004, color: 'gray', diff --git a/templates/messages.html b/templates/messages.html new file mode 100644 index 000000000..0e05aa2c1 --- /dev/null +++ b/templates/messages.html @@ -0,0 +1,72 @@ +
    + {% for message in messages %} + {% if message.tags == 'info' %} + + {% endif %} + {% if message.tags == 'success' %} + + {% endif %} + {% if message.tags == 'error' %} + + {% endif %} + {% if message.tags == 'warning' %} + + {% endif %} + {% endfor %} +
    diff --git a/templates/run_detail.html b/templates/run_detail.html index 1816c1414..d494bff60 100644 --- a/templates/run_detail.html +++ b/templates/run_detail.html @@ -2,6 +2,9 @@ {% load static %} {% load humanize %} +{% block title %} +VAST Pipeline: Run ID {{ p_run.id }} +{% endblock title %} {% block head %} @@ -38,18 +41,50 @@

    Pipeline Run: {{ p_run.name }}

    - {% if user.get_username == p_run.user or user.is_staff %} - - - - - {% if p_run.status == 'Completed' %}Add Images or Re-{% endif %}Process Run - - {% endif %} +
    @@ -145,9 +180,14 @@
    Details

    Epoch based: {{ p_run.epoch_based }}

    Run Status: {{ p_run.status }}

    Path: {{ p_run.path }}

    +

    Arrow Files Available: {% if p_run.arrow_files == True %}Yes{% else %}No{% endif %}

    Description: {{ p_run.description }}

    -

    Config File

     -

    Log File

     +

    + Config File
    + Run Log File
    + Restore Log File
    + Generate Arrow Files Log File +

    @@ -212,82 +252,104 @@
    -
    -
    +
    +
    -
    - +
    + - Images + Restore Log File +
    + +
    - -
    +
    -
    - - - - {% for name in datatables.0.colsNames %} - - {% endfor %} - - - - - {% for name in datatables.0.colsNames %} - - {% endfor %} - - -
    {{ name }}
    {{ name }}
    -
    + 
    No logs to show.
    +
    +
    +
    +
    +
    + +
    +
    + +
    +
    + + + Generate Arrow Files Log File + +
    + +
    +
    +
    +
    + 
    No logs to show.
    - +
    - + - Measurements + Images
    -
    +
    - +
    - {% for name in datatables.1.colsNames %} + {% for name in datatables.0.colsNames %} {% endfor %} - {% for name in datatables.1.colsNames %} + {% for name in datatables.0.colsNames %} {% endfor %} @@ -387,6 +449,161 @@
    Danger Area - Full Re-Run
    + + + + + + + + + {% endblock modal %} @@ -408,7 +625,7 @@
    Danger Area - Full Re-Run
    - + {{ d3_celestial_skyregions|json_script:"skyregion-data" }} diff --git a/templates/source_detail.html b/templates/source_detail.html index 2aea10c7c..0c1da33f7 100644 --- a/templates/source_detail.html +++ b/templates/source_detail.html @@ -5,6 +5,10 @@ {% load unit_tags %} +{% block title %} +VAST Pipeline: Source ID {{ source.id }} +{% endblock title %} + {% block head %} @@ -34,15 +38,16 @@

    Source: {{ source.name }}

    -
    +
    @@ -244,7 +265,7 @@
    External Search Results (1 arcmin)
    {{ name }}
    {{ name }}
    + data-ajax="{% url 'vast_pipeline:api_utils-external-search' %}?coord={{ source.wavg_ra_hms }}{{ source.wavg_dec_dms }}&format=datatables&keep=otype_long,database"> @@ -277,6 +298,13 @@
    + {% if cutout_measurements|length < datatables.0.dataQuery|length %} + + {% endif %}
    @@ -285,9 +313,10 @@
    - {% for measurement in datatables.0.dataQuery %} + {% for measurement in cutout_measurements %}
    + {{ forloop.counter0 }}

    {{ measurement.image_name }}

    Related Sources {% block modal %} - -

    +{% include 'source_fav_modal.html' %} {% endblock modal %} @@ -455,6 +457,7 @@ + -{{ datatables.0.dataQuery|json_script:"data-js9" }} +{{ cutout_measurements|json_script:"data-js9" }} +{{ first_det_meas|json_script:"data-js9-first-detection" }} {% endblock custom_page_scripts %} diff --git a/templates/source_fav_button.html b/templates/source_fav_button.html new file mode 100644 index 000000000..42a7d1c92 --- /dev/null +++ b/templates/source_fav_button.html @@ -0,0 +1,8 @@ + + + + + + + diff --git a/templates/source_fav_modal.html b/templates/source_fav_modal.html new file mode 100644 index 000000000..0e68e469f --- /dev/null +++ b/templates/source_fav_modal.html @@ -0,0 +1,29 @@ + + diff --git a/templates/sources_etav_plot.html b/templates/sources_etav_plot.html new file mode 100644 index 000000000..6432efe87 --- /dev/null +++ b/templates/sources_etav_plot.html @@ -0,0 +1,161 @@ +{% extends "base.html" %} + +{% load static %} +{% load crispy_forms_tags %} + +{% load unit_tags %} + +{% block head %} + + + + +{% endblock head %} + + +{% block breadcrumb %} + +
  • + Sources +
    • +
  • + Query η-V Plot +
    • + +{% endblock breadcrumb %} + +{% block content %} + + +
    + +
    +
    + + +
    +
    +
    η-V Plot
    +
    +
    +
    + {% if plot_ok == 0 %} +
    Error.
    + {% endif %} +
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    Selected Source Light Curve
    +
    +
    +
    +
    +
    +
    +
    + +
    + {% include 'sources_etav_plot_update.html' %} +
    + +
    +
    + +
    +
    +
    + +
    + +{% endblock content %} + +{% block scripts %} + + + + + + + + + + + + + +{% endblock scripts %} + + +{% block custom_page_scripts %} + + + + + + + + + + + + + + + + + + + + + + +{% endblock custom_page_scripts %} diff --git a/templates/sources_etav_plot_update.html b/templates/sources_etav_plot_update.html new file mode 100644 index 000000000..97a83ba58 --- /dev/null +++ b/templates/sources_etav_plot_update.html @@ -0,0 +1,97 @@ +{% load unit_tags %} + +
    +
    +
    +
    +
    +
    Source Information
    +
    +
    + {% if source %} +
    + {{ source.name }} +
    +
    + {% include 'source_fav_button.html' %} + + Source detail page + + + + +
    +
    + Position: {{ source.wavg_ra_hms }} {{ source.wavg_dec_dms }}
    + Position (Decimal): {{ source.wavg_ra|floatformat:6 }} {{ source.wavg_dec|floatformat:6 }}
    + Position (Galactic): {{ source.wavg_l|floatformat:6 }} {{ source.wavg_b|floatformat:6 }} +
    +
    + Avg. Compactness: {{ source.avg_compactness|floatformat:3 }} +
    +
    + Neighbour Dist: {{ source.n_neighbour_dist|deg_to_arcmin|floatformat:2 }}′ +
    +
    + Max SNR: {{ source.max_snr|floatformat:2 }} +
    +
    + Min SNR: {{ source.min_snr|floatformat:2 }} +
    +
    +
    +
    + Nr Relations: {{ source.n_rel }} +
    +
    + Nr Siblings: {{ source.n_sibl }} +
    +
    + New Source: {{ source.new }} +
    +
    + New High Sigma:{% if source.new %}{{ source.new_high_sigma|floatformat:2 }}{% else %}N/A{% endif %} +
    +
    + {{ datatables|json_script:"datatable-conf" }} + {% else %} + The details of a selected source will appear here. + {% endif %} +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    External Search Results (1 arcmin)
    +
    +
    +
    + {% if source %} +
    ID
    + + + + + + + + + +
    IDDist (arcsec)TypeRA (hms)Dec (dms)
    + {% endif %} +
    +
    +
    +
    +
    + +{% include 'source_fav_modal.html' %} diff --git a/templates/sources_query.html b/templates/sources_query.html index 0bfd04144..205f122da 100644 --- a/templates/sources_query.html +++ b/templates/sources_query.html @@ -24,7 +24,10 @@

    Sources Query

    -

    Search through the sources using the query parameters

    +

    + Search through the sources using the query parameters. + A query must be performed before the η-V analysis button becomes active. +

    @@ -489,8 +492,19 @@
    -
    -
    Results
    +
    +
    Results + +
    diff --git a/vast_pipeline/_version.py b/vast_pipeline/_version.py index 1f356cc57..1a72d32e5 100644 --- a/vast_pipeline/_version.py +++ b/vast_pipeline/_version.py @@ -1 +1 @@ -__version__ = '1.0.0' +__version__ = '1.1.0' diff --git a/vast_pipeline/apps.py b/vast_pipeline/apps.py index 2244afec4..7d1700db8 100644 --- a/vast_pipeline/apps.py +++ b/vast_pipeline/apps.py @@ -4,3 +4,10 @@ class PipelineConfig(AppConfig): """Class representing the configuration for the vast_pipeline app.""" name = 'vast_pipeline' + + def ready(self) -> None: + """Initialization tasks performed as soon as the app registry is populated. + See . + """ + # import the signals to register them with the application + import vast_pipeline.signals # noqa: F401 diff --git a/vast_pipeline/config_template.yaml.j2 b/vast_pipeline/config_template.yaml.j2 index 3c25f7128..85a93e5f1 100644 --- a/vast_pipeline/config_template.yaml.j2 +++ b/vast_pipeline/config_template.yaml.j2 @@ -18,7 +18,7 @@ inputs: - {{ image }} {% endfor %} {% endfor %} - + selavy: {% for epoch, image_list in selavy_files.items() %} '{{ epoch }}': @@ -26,7 +26,7 @@ inputs: - {{ image }} {% endfor %} {% endfor %} - + noise: {% for epoch, image_list in noise_files.items() %} '{{ epoch }}': @@ -85,7 +85,7 @@ source_monitoring: # Minimum SNR ratio a source has to be if it was placed in the area of minimum rms in # the image from which it is to be extracted from. If lower than this value it is skipped min_sigma: {{ monitor_min_sigma }} - + # Multiplicative scaling factor to the buffer size of the forced photometry from the # image edge edge_buffer_scale: {{ monitor_edge_buffer_scale }} @@ -135,24 +135,31 @@ measurements: # 0.05 = 5% error, 0 = no minimum error. flux_fractional_error: {{ flux_perc_error }} + # Create 'measurements.arrow' and 'measurement_pairs.arrow' files at the end of + # a successful run. + write_arrow_files: {{ create_measurements_arrow_files }} + # Replace the selavy errors with Condon (1997) errors. + # Only applied to NEW images that are not already present in the database. condon_errors: {{ use_condon_errors }} # Sometimes the local rms for a source is reported as 0 by selavy. # Choose a value to use for the local rms in these cases in mJy/beam. + # Only applied to NEW images that are not already present in the database. selavy_local_rms_fill_value: {{ selavy_local_rms_zero_fill_value }} - # Create 'measurements.arrow' and 'measurement_pairs.arrow' files at the end of - # a successful run. - write_arrow_files: {{ create_measurements_arrow_files }} - # The positional uncertainty of a measurement is in reality the fitting errors and the # astrometric uncertainty of the image/survey/instrument combined in quadrature. # These two parameters are the astrometric uncertainty in RA/Dec and they may be different. + # Only applied to NEW images that are not already present in the database, and to forced extractions. ra_uncertainty: {{ astrometric_uncertainty_ra }} # arcsec dec_uncertainty: {{ astrometric_uncertainty_dec }} # arcsec variability: + # For each source, calculate the measurement pair metrics (Vs and m) for each unique + # combination of measurements. + pair_metrics: {{ pair_metrics }} + # Only measurement pairs where the Vs metric exceeds this value are selected for the # aggregate pair metrics that are stored in Source objects. source_aggregate_pair_metrics_min_abs_vs: {{ source_aggregate_pair_metrics_min_abs_vs }} diff --git a/vast_pipeline/context_processors.py b/vast_pipeline/context_processors.py index 35859a52a..1e807072b 100644 --- a/vast_pipeline/context_processors.py +++ b/vast_pipeline/context_processors.py @@ -28,7 +28,7 @@ def pipeline_version(request: HttpRequest) -> Dict[str, Optional[str]]: request (HttpRequest): The web server request. Returns: - Dict[str, str]: key-value pairs to add to the template context. + Key-value pairs to add to the template context. """ url: Optional[str] = None if not __version__.endswith("dev"): diff --git a/vast_pipeline/converters.py b/vast_pipeline/converters.py index 753bf321f..9c618aa88 100644 --- a/vast_pipeline/converters.py +++ b/vast_pipeline/converters.py @@ -71,7 +71,7 @@ class AngleConverter: value.""" regex = r"\d+(\.\d+)?\s?\w+" - def to_python(self, value) -> float: + def to_python(self, value: Angle) -> float: """ Return the decimal degrees from the coordinate input as an Angle object. @@ -83,7 +83,7 @@ def to_python(self, value) -> float: """ return Angle(value).deg - def to_url(self, value) -> str: + def to_url(self, value: Angle) -> str: """ Return the string format of an Angle object from the coordinate input. diff --git a/vast_pipeline/forms.py b/vast_pipeline/forms.py index 227827d07..2b749ddbe 100644 --- a/vast_pipeline/forms.py +++ b/vast_pipeline/forms.py @@ -32,6 +32,7 @@ class PipelineRunForm(forms.Form): flux_perc_error = forms.FloatField() selavy_local_rms_zero_fill_value = forms.FloatField() source_aggregate_pair_metrics_min_abs_vs = forms.FloatField() + pair_metrics = forms.BooleanField(required=False) use_condon_errors = forms.BooleanField(required=False) create_measurements_arrow_files = forms.BooleanField(required=False) suppress_astropy_warnings = forms.BooleanField(required=False) diff --git a/vast_pipeline/image/main.py b/vast_pipeline/image/main.py index b8d9e0f59..2a8edaf5b 100644 --- a/vast_pipeline/image/main.py +++ b/vast_pipeline/image/main.py @@ -9,6 +9,7 @@ from django.conf import settings from astropy.io import fits +from astropy.table import Table from astropy.wcs import WCS from astropy.wcs.utils import proj_plane_pixel_scales from typing import Dict @@ -16,9 +17,9 @@ from .utils import calc_error_radius from .utils import calc_condon_flux_errors -from vast_pipeline.pipeline.config import PipelineConfig from vast_pipeline import models from vast_pipeline.survey.translators import tr_selavy +from vast_pipeline.image.utils import open_fits logger = logging.getLogger(__name__) @@ -33,6 +34,7 @@ class Image(object): path (str): The system path to the image. """ + def __init__(self, path: str) -> None: """ Initiliase an image object. @@ -80,7 +82,7 @@ class FitsImage(Image): entire_image = True - def __init__(self, path: str, hdu_index: int=0) -> None: + def __init__(self, path: str, hdu_index: int = 0) -> None: """ Initialise a FitsImage object. @@ -107,7 +109,7 @@ def __init__(self, path: str, hdu_index: int=0) -> None: def __get_header(self, hdu_index: int) -> fits.Header: """ - Retrieves the header from teh FITS image. + Retrieves the header from the FITS image. Args: hdu_index: @@ -116,13 +118,14 @@ def __get_header(self, hdu_index: int) -> fits.Header: Returns: The FITS header as an astropy.io.fits.Header object. """ + try: - with fits.open(self.path) as hdulist: + with open_fits(self.path) as hdulist: hdu = hdulist[hdu_index] except Exception: raise IOError(( - 'Could not read this FITS file: ' - f'{os.path.basename(self.path)}' + 'Could not read FITS file: ' + f'{self.path}' )) return hdu.header.copy() @@ -168,10 +171,20 @@ def __set_img_attr_for_telescope(self, header): # get the time as Julian Datetime using Pandas function self.jd = self.datetime.to_julian_date() - def __get_img_coordinates(self, header, fits_naxis1, fits_naxis2): + def __get_img_coordinates( + self, header: fits.Header, fits_naxis1: str, fits_naxis2: str + ) -> None: """ - set the image attributes ra, dec, fov_bmin and fov_bmaj, radius - from the image file header + Set the image attributes ra, dec, fov_bmin and fov_bmaj, radius + from the image file header. + + Args: + header: The FITS header object. + fits_naxis1: The header keyword of the NAXIS1 to use. + fits_naxis2: The header keyword of the NAXIS2 to use. + + Returns: + None """ wcs = WCS(header, naxis=2) pix_centre = [[header[fits_naxis1] / 2., header[fits_naxis2] / 2.]] @@ -195,34 +208,51 @@ def __get_img_coordinates(self, header, fits_naxis1, fits_naxis2): # TODO: check calcs self.radius_pixels = usable_radius_pix - def __get_radius_pixels(self, header, fits_naxis1, fits_naxis2): + def __get_radius_pixels( + self, header: fits.Header, fits_naxis1: str, fits_naxis2: str + ) -> float: """ Return the radius (pixels) of the full image. - If the image is not a square/circle then the shortest radius will be returned. + + If the image is not a square/circle then the shortest radius will be + returned. + + Args: + header: The FITS header object. + fits_naxis1: The header keyword of the NAXIS1 to use. + fits_naxis2: The header keyword of the NAXIS2 to use. + + Returns: + The radius of the image in pixels. """ if self.entire_image: - # a large circle that *should* include the whole image (and then some) + # a large circle that *should* include the whole image + # (and then some) diameter = np.hypot(header[fits_naxis1], header[fits_naxis2]) else: # We simply place the largest circle we can in the centre. diameter = min(header[fits_naxis1], header[fits_naxis2]) return diameter / 2. - def __get_frequency(self, header): + def __get_frequency(self, header: fits.Header) -> None: """ Set some 'shortcut' variables for access to the frequency parameters in the FITS file header. - @param hdulist: hdulist to parse - @type hdulist: hdulist + Args: + header: The FITS header object. + + Returns: + None """ self.freq_eff = None self.freq_bw = None try: - if ('ctype3' in header) and (header['ctype3'] in ('FREQ', 'VOPT')): + freq_keys = ('FREQ', 'VOPT') + if ('ctype3' in header) and (header['ctype3'] in freq_keys): self.freq_eff = header['crval3'] self.freq_bw = header['cdelt3'] if 'cdelt3' in header else 0.0 - elif ('ctype4' in header) and (header['ctype4'] in ('FREQ', 'VOPT')): + elif ('ctype4' in header) and (header['ctype4'] in freq_keys): self.freq_eff = header['crval4'] self.freq_bw = header['cdelt4'] if 'cdelt4' in header else 0.0 else: @@ -244,13 +274,14 @@ class SelavyImage(FitsImage): with the image. background_path (str): The system path to the background image associated with the image. - config (PipelineConfig): The pipeline configuration settings. + config (Dict): The image configuration settings. """ + def __init__( self, path: str, paths: Dict[str, Dict[str, str]], - config: PipelineConfig, + config: Dict, hdu_index: int = 0, ) -> None: """ @@ -261,7 +292,7 @@ def __init__( paths: Dictionary containing the system paths to the associated image products and selavy catalogue. The keys are 'selavy', 'noise', 'background'. - config: Configuration settings for the pipeline. + config: Configuration settings for the image ingestion. hdu_index: The index number to use to access the header from the hdu object. @@ -272,7 +303,7 @@ def __init__( self.selavy_path = paths['selavy'][path] self.noise_path = paths['noise'].get(path, '') self.background_path = paths['background'].get(path, '') - self.config: PipelineConfig = config + self.config: Dict = config super().__init__(path, hdu_index) def read_selavy(self, dj_image: models.Image) -> pd.DataFrame: @@ -288,7 +319,18 @@ def read_selavy(self, dj_image: models.Image) -> pd.DataFrame: Dataframe containing the cleaned and processed Selavy components. """ # TODO: improve with loading only the cols we need and set datatype - df = pd.read_fwf(self.selavy_path, skiprows=[1]) + if self.selavy_path.endswith( + ".xml") or self.selavy_path.endswith(".vot"): + df = Table.read( + self.selavy_path, format="votable", use_names_over_ids=True + ).to_pandas() + elif self.selavy_path.endswith(".csv"): + # CSVs from CASDA have all lowercase column names + df = pd.read_csv(self.selavy_path).rename( + columns={"spectral_index_from_tt": "spectral_index_from_TT"} + ) + else: + df = pd.read_fwf(self.selavy_path, skiprows=[1]) # drop first line with unit of measure, select only wanted # columns and rename them df = df.loc[:, tr_selavy.keys()].rename( @@ -354,12 +396,12 @@ def read_selavy(self, dj_image: models.Image) -> pd.DataFrame: # replace 0 local_rms values using user config value df.loc[ df['local_rms'] == 0., 'local_rms' - ] = self.config["measurements"]["selavy_local_rms_fill_value"] + ] = self.config["selavy_local_rms_fill_value"] df['snr'] = df['flux_peak'].values / df['local_rms'].values df['compactness'] = df['flux_int'].values / df['flux_peak'].values - if self.config["measurements"]["condon_errors"]: + if self.config["condon_errors"]: logger.debug("Calculating Condon '97 errors...") theta_B = dj_image.beam_bmaj theta_b = dj_image.beam_bmin @@ -391,8 +433,8 @@ def read_selavy(self, dj_image: models.Image) -> pd.DataFrame: logger.debug("Calculating positional errors...") # TODO: avoid extra column given that it is a single value - df['ew_sys_err'] = self.config["measurements"]["ra_uncertainty"] / 3600. - df['ns_sys_err'] = self.config["measurements"]["dec_uncertainty"] / 3600. + df['ew_sys_err'] = self.config["ra_uncertainty"] / 3600. + df['ns_sys_err'] = self.config["dec_uncertainty"] / 3600. df['error_radius'] = calc_error_radius( df['ra'].values, @@ -425,12 +467,12 @@ def read_selavy(self, dj_image: models.Image) -> pd.DataFrame: .agg('sum') ) - df['flux_int_isl_ratio'] = ( + df['flux_int_isl_ratio'] = ( df['flux_int'].values / island_flux_totals.loc[df['island_id']]['flux_int'].values ) - df['flux_peak_isl_ratio'] = ( + df['flux_peak_isl_ratio'] = ( df['flux_peak'].values / island_flux_totals.loc[df['island_id']]['flux_peak'].values ) diff --git a/vast_pipeline/image/utils.py b/vast_pipeline/image/utils.py index b620bf0d7..152017532 100644 --- a/vast_pipeline/image/utils.py +++ b/vast_pipeline/image/utils.py @@ -7,7 +7,9 @@ import numpy as np import pandas as pd -from typing import Tuple +from typing import Tuple, Union, Optional +from pathlib import Path +from astropy.io import fits logger = logging.getLogger(__name__) @@ -81,7 +83,7 @@ def calc_error_radius(ra, ra_err, dec, dec_err) -> float: np.deg2rad(i), dec_1, np.deg2rad(j) - )) for i,j in zip(ra_offsets, dec_offsets) + )) for i, j in zip(ra_offsets, dec_offsets) ] seps = np.column_stack(seps) @@ -90,10 +92,18 @@ def calc_error_radius(ra, ra_err, dec, dec_err) -> float: def calc_condon_flux_errors( - row: pd.Series, theta_B: float, theta_b: float , alpha_maj1: float=2.5, - alpha_min1: float=0.5, alpha_maj2: float=0.5, alpha_min2:float =2.5, - alpha_maj3: float=1.5, alpha_min3: float=1.5, clean_bias: float=0.0, - clean_bias_error: float=0.0, frac_flux_cal_error: float=0.0, + row: pd.Series, + theta_B: float, + theta_b: float, + alpha_maj1: float = 2.5, + alpha_min1: float = 0.5, + alpha_maj2: float = 0.5, + alpha_min2: float = 2.5, + alpha_maj3: float = 1.5, + alpha_min3: float = 1.5, + clean_bias: float = 0.0, + clean_bias_error: float = 0.0, + frac_flux_cal_error: float = 0.0 ) -> Tuple[float, float, float, float, float, float, float]: """ The following code for this function taken from the TraP with a few @@ -109,7 +119,7 @@ def calc_condon_flux_errors( Args: row (pd.Series): - The row containing the componenet information from the Selavy + The row containing the component information from the Selavy component catalogue. theta_B (float): The major axis size of the restoring beam of the image (degrees). @@ -138,10 +148,13 @@ def calc_condon_flux_errors( TraP). Returns: - Tuple containing the following calculated values: - peak flux error, integrated flux error, major axis error, minor - axis error, position angle error, right ascension error and the - declination error. + Peak flux error (Jy). + Integrated flux error (Jy). + Major axis error (deg). + Minor axis error (deg). + Position angle error (deg). + Right ascension error (deg). + Declination error (deg). """ major = row.bmaj / 3600. # degrees @@ -179,7 +192,7 @@ def calc_condon_flux_errors( (1. + (theta_B / major)**2)**alpha_maj2 * (1. + (theta_b / minor)**2)**alpha_min2 * snr**2) - rho_sq3 = ((major * minor / (4.* theta_B * theta_b)) * + rho_sq3 = ((major * minor / (4. * theta_B * theta_b)) * (1. + (theta_B / major)**2)**alpha_maj3 * (1. + (theta_b / minor)**2)**alpha_min3 * snr**2) @@ -199,9 +212,9 @@ def calc_condon_flux_errors( # ra and dec errors errorra = np.sqrt((error_par_major * np.sin(theta))**2 + - (error_par_minor * np.cos(theta))**2) + (error_par_minor * np.cos(theta))**2) errordec = np.sqrt((error_par_major * np.cos(theta))**2 + - (error_par_minor * np.sin(theta))**2) + (error_par_minor * np.sin(theta))**2) errormajor = np.sqrt(2) * major / rho1 errorminor = np.sqrt(2) * minor / rho2 @@ -227,11 +240,40 @@ def calc_condon_flux_errors( help1 = (errormajor / major)**2 help2 = (errorminor / minor)**2 help3 = theta_B * theta_b / (major * minor) - errorflux = np.abs(flux_int) * np.sqrt(errorpeaksq / flux_peak**2 + help3 * (help1 + help2)) + help4 = np.sqrt(errorpeaksq / flux_peak**2 + help3 * (help1 + help2)) + errorflux = np.abs(flux_int) * help4 # need to return flux_peak if used. return errorpeak, errorflux, errormajor, errorminor, errortheta, errorra, errordec except Exception as e: - logger.debug("Error in the calculation of Condon errors for a source", exc_info=True) + logger.debug( + "Error in the calculation of Condon errors for a source", + exc_info=True) return 0., 0., 0., 0., 0., 0., 0. + + +def open_fits(fits_path: Union[str, Path], memmap: Optional[bool] = True): + """ + This function opens both compressed and uncompressed fits files. + + Args: + fits_path: Path to the fits file + memmap: Open the fits file with mmap. + + Returns: + HDUList loaded from the fits file + """ + + if isinstance(fits_path, Path): + fits_path = str(fits_path) + + hdul = fits.open(fits_path, memmap=memmap) + + # This is a messy way to check, but I can't think of a better one + if len(hdul) == 1: + return hdul + elif type(hdul[1]) == fits.hdu.compressed.CompImageHDU: + return fits.HDUList(hdul[1:]) + else: + return hdul diff --git a/vast_pipeline/ingest_config_template.yaml.j2 b/vast_pipeline/ingest_config_template.yaml.j2 new file mode 100644 index 000000000..97ca25eea --- /dev/null +++ b/vast_pipeline/ingest_config_template.yaml.j2 @@ -0,0 +1,85 @@ +# This file is used for ingest only mode +# It specifies the images to be processed and the relevant +# settings to be used. + +inputs: +{% if epoch_mode %} + image: + {% for epoch, image_list in image_files.items() %} + '{{ epoch }}': + {% for image in image_list %} + - {{ image }} + {% endfor %} + {% endfor %} + + selavy: + {% for epoch, image_list in selavy_files.items() %} + '{{ epoch }}': + {% for image in image_list %} + - {{ image }} + {% endfor %} + {% endfor %} + + noise: + {% for epoch, image_list in noise_files.items() %} + '{{ epoch }}': + {% for image in image_list %} + - {{ image }} + {% endfor %} + {% endfor %} + + # Required only if source_monitoring.monitor is true, otherwise optional. If not providing + # background images, remove the entire background section below. + background: + {% for epoch, image_list in background_files.items() %} + '{{ epoch }}': + {% for image in image_list %} + - {{ image }} + {% endfor %} + {% endfor %} +{% else %} + # NOTE: all the inputs must match with each other, i.e. the catalogue for the first + # input image (inputs.image[0]) must be the first input catalogue (inputs.selavy[0]) + # and so on. + image: + # list input images here, e.g. (note the leading hyphens) + # - /path/to/image1.fits + # - /path/to/image2.fits + {% for image in image_files %} + - {{ image }} + {% endfor %} + + selavy: + # list input selavy catalogues here, as above with the images + {% for image in selavy_files %} + - {{ image }} + {% endfor %} + + noise: + # list input noise (rms) images here, as above with the images + {% for image in noise_files %} + - {{ image }} + {% endfor %} + + # Required only if source_monitoring.monitor is true, otherwise optional. If not providing + # background images, remove the entire background section below. + {% if not monitor and not background_files %} # {% else %} {% endif %}background: + # list input background images here, as above with the images + {% for image in background_files %} + - {{ image }} + {% endfor %} +{% endif %} + +measurements: + # Replace the selavy errors with Condon (1997) errors. + condon_errors: {{ use_condon_errors }} + + # Sometimes the local rms for a source is reported as 0 by selavy. + # Choose a value to use for the local rms in these cases in mJy/beam. + selavy_local_rms_fill_value: {{ selavy_local_rms_zero_fill_value }} + + # The positional uncertainty of a measurement is in reality the fitting errors and the + # astrometric uncertainty of the image/survey/instrument combined in quadrature. + # These two parameters are the astrometric uncertainty in RA/Dec and they may be different. + ra_uncertainty: {{ astrometric_uncertainty_ra }} # arcsec + dec_uncertainty: {{ astrometric_uncertainty_dec }} # arcsec diff --git a/vast_pipeline/management/commands/clearpiperun.py b/vast_pipeline/management/commands/clearpiperun.py index 36182c722..1ede5ea7b 100644 --- a/vast_pipeline/management/commands/clearpiperun.py +++ b/vast_pipeline/management/commands/clearpiperun.py @@ -9,6 +9,7 @@ from argparse import ArgumentParser from glob import glob from django.core.management.base import BaseCommand, CommandError +from django.db import transaction from vast_pipeline.models import Run from vast_pipeline.pipeline.forced_extraction import remove_forced_meas @@ -108,6 +109,9 @@ def handle(self, *args, **options) -> None: raise CommandError(f'Pipeline run {p_run_name} does not exist') logger.info("Deleting pipeline '%s' from database", p_run_name) + with transaction.atomic(): + p_run.status = 'DEL' + p_run.save() p_run.delete() # remove forced measurements in db if presents @@ -138,4 +142,3 @@ def handle(self, *args, **options) -> None: f'Issues in removing run folder: {e}' )) pass - diff --git a/vast_pipeline/management/commands/createmeasarrow.py b/vast_pipeline/management/commands/createmeasarrow.py index f769de0eb..f7c5009d4 100644 --- a/vast_pipeline/management/commands/createmeasarrow.py +++ b/vast_pipeline/management/commands/createmeasarrow.py @@ -5,8 +5,6 @@ import os import logging -import traceback -import warnings from argparse import ArgumentParser from django.core.management.base import BaseCommand, CommandError @@ -15,6 +13,7 @@ create_measurement_pairs_arrow_file ) from vast_pipeline.models import Run +from vast_pipeline.utils.utils import timeStamped from ..helpers import get_p_run_name @@ -67,22 +66,30 @@ def handle(self, *args, **options) -> None: Returns: None """ + piperun = options['piperun'] + + p_run_name, run_folder = get_p_run_name( + piperun, + return_folder=True + ) + # configure logging + root_logger = logging.getLogger('') + f_handler = logging.FileHandler( + os.path.join(run_folder, timeStamped('gen_arrow_log.txt')), + mode='w' + ) + f_handler.setFormatter(root_logger.handlers[0].formatter) + root_logger.addHandler(f_handler) + if options['verbosity'] > 1: # set root logger to use the DEBUG level - root_logger = logging.getLogger('') root_logger.setLevel(logging.DEBUG) # set the traceback on options['traceback'] = True - piperun = options['piperun'] - - p_run_name, run_folder = get_p_run_name( - piperun, - return_folder=True - ) try: - p_run = Run.objects.get(name=p_run_name) + p_run: Run = Run.objects.get(name=p_run_name) except Run.DoesNotExist: raise CommandError(f'Pipeline run {p_run_name} does not exist') @@ -99,6 +106,10 @@ def handle(self, *args, **options) -> None: logger.info("Removing previous 'measurements.arrow' file.") os.remove(measurements_arrow) else: + logger.error( + f'Measurements arrow file already exists for {p_run_name}' + ' and `--overwrite` has not been selected.' + ) raise CommandError( f'Measurements arrow file already exists for {p_run_name}' ' and `--overwrite` has not been selected.' @@ -111,6 +122,10 @@ def handle(self, *args, **options) -> None: ) os.remove(measurement_pairs_arrow) else: + logger.error( + 'Measurement pairs arrow file already exists for' + f' {p_run_name} and `--overwrite` has not been selected.' + ) raise CommandError( 'Measurement pairs arrow file already exists for' f' {p_run_name} and `--overwrite` has not been selected.' @@ -120,8 +135,13 @@ def handle(self, *args, **options) -> None: create_measurements_arrow_file(p_run) + if p_run.get_config(validate_inputs=False, prev=True)["variability"]["pair_metrics"]: + logger.info( + "Creating measurement pairs arrow file for '%s'.", p_run_name + ) + + create_measurement_pairs_arrow_file(p_run) + logger.info( - "Creating measurement pairs arrow file for '%s'.", p_run_name + "Arrow files created successfully for '%s'!", p_run_name ) - - create_measurement_pairs_arrow_file(p_run) diff --git a/vast_pipeline/management/commands/debugrun.py b/vast_pipeline/management/commands/debugrun.py index 88b53ced8..d9e15026a 100644 --- a/vast_pipeline/management/commands/debugrun.py +++ b/vast_pipeline/management/commands/debugrun.py @@ -7,7 +7,7 @@ from django.core.management.base import BaseCommand, CommandError from vast_pipeline.models import ( - Run, Measurement, Image, Source, Association + Run, Measurement, Source, Association ) from ..helpers import get_p_run_name diff --git a/vast_pipeline/management/commands/ingestimages.py b/vast_pipeline/management/commands/ingestimages.py new file mode 100644 index 000000000..92d047056 --- /dev/null +++ b/vast_pipeline/management/commands/ingestimages.py @@ -0,0 +1,95 @@ +import logging + +from argparse import ArgumentParser +from django.core.management.base import BaseCommand, CommandError +from vast_pipeline.pipeline.config import ImageIngestConfig +from vast_pipeline.pipeline.errors import PipelineConfigError +from vast_pipeline.pipeline.main import Pipeline +from vast_pipeline.pipeline.loading import make_upload_images +from typing import Dict + +logger = logging.getLogger(__name__) + + +class _DummyPipeline(object): + """ + A stripped down 'dummy' version of the Pipeline class, which + provides the right methods and attributes for interfacing with + the `make_upload_images()` function. + + Its main purpose is to correctly set the attribute: `img_paths` + """ + make_img_paths = Pipeline.match_images_to_data + + def __init__(self,config: ImageIngestConfig) -> None: + self.config = config + self.img_paths: Dict[str, Dict[str, str]] = { + 'selavy': {}, + 'noise': {}, + 'background': {}, + } # maps input image paths to their selavy/noise/background counterpart path + self.img_epochs: Dict[str, str] = {} # maps image names to their provided epoch + self.make_img_paths() + + +class Command(BaseCommand): + """ + This script runs the first part of the pipeline only. It ingests a set of + images into the database along with their measurements. + """ + help = ( + 'Ingest/add a set of images to the database' + ) + + def add_arguments(self, parser: ArgumentParser) -> None: + """ + Enables arguments for the command. + + Args: + parser (ArgumentParser): The parser object of the command. + + Returns: + None + """ + parser.add_argument( + 'image_ingest_config', + nargs=1, + type=str, + help=('Image ingestion configuration filename/path.') + ) + + def handle(self, *args, **options) -> None: + """ + Handle function of the command. + + Args: + *args: Variable length argument list. + **options: Variable length options. + + Returns: + None + """ + # configure logging + if options['verbosity'] > 1: + # set root logger to use the DEBUG level + root_logger = logging.getLogger('') + root_logger.setLevel(logging.DEBUG) + # set the traceback on + options['traceback'] = True + + # Create image ingestion configuration object from input file + image_config = ImageIngestConfig.from_file( + options['image_ingest_config'][0], validate=False + ) + + # Validate the config + try: + image_config.validate() + except PipelineConfigError as e: + raise CommandError(e) + + # Create a dummy Pipeline instance using the given image ingestion configuration options + d = _DummyPipeline(image_config) + + # Read, measure and upload the images listed in the image ingestion config + make_upload_images(d.img_paths,image_config.image_opts()) diff --git a/vast_pipeline/management/commands/initingest.py b/vast_pipeline/management/commands/initingest.py new file mode 100644 index 000000000..70826a567 --- /dev/null +++ b/vast_pipeline/management/commands/initingest.py @@ -0,0 +1,67 @@ +import logging +from argparse import ArgumentParser +from django.conf import settings +from django.core.management.base import BaseCommand +from vast_pipeline.pipeline.config import ImageIngestConfig, make_config_template + +logger = logging.getLogger(__name__) + + +class Command(BaseCommand): + """ + This script creates a template image configuration file for use with image ingestion + """ + help = ( + 'Create a template image ingestion configuration file' + ) + + def add_arguments(self, parser: ArgumentParser) -> None: + """ + Enables arguments for the command. + + Args: + parser (ArgumentParser): The parser object of the command. + + Returns: + None + """ + parser.add_argument( + 'config_file_name', + nargs=1, + type=str, + help=('Filename to write template ingest configuration to.') + ) + + def handle(self, *args, **options) -> None: + """ + Handle function of the command. + + Args: + *args: Variable length argument list. + **options: Variable length options. + + Returns: + None + """ + # configure logging + if options['verbosity'] > 1: + # set root logger to use the DEBUG level + root_logger = logging.getLogger('') + root_logger.setLevel(logging.DEBUG) + # set the traceback on + options['traceback'] = True + + template_str = make_config_template( + ImageIngestConfig.TEMPLATE_PATH, + **settings.PIPE_RUN_CONFIG_DEFAULTS + ) + + fname = options['config_file_name'][0] + + # Enforce .yml extension + if not fname.endswith('.yaml') and not fname.endswith('.yml'): + fname = fname + '.yml' + + print("Writing template to: ", fname) + with open(fname, 'w') as f: + f.write(template_str+"\n") diff --git a/vast_pipeline/management/commands/initpiperun.py b/vast_pipeline/management/commands/initpiperun.py index 0bb773a17..a44c57f35 100644 --- a/vast_pipeline/management/commands/initpiperun.py +++ b/vast_pipeline/management/commands/initpiperun.py @@ -8,7 +8,6 @@ import logging from typing import Any, Dict, Optional -from argparse import ArgumentParser from django.core.management.base import BaseCommand, CommandError from django.conf import settings as sett from django.contrib.auth.models import User @@ -45,7 +44,7 @@ def initialise_run( PipelineInitError: A directory named `run_name` already exists. Returns: - Run: The initialised pipeline Run Django model object. + The initialised pipeline Run Django model object. """ # check for duplicated run name p_run = Run.objects.filter(name__exact=run_name) diff --git a/vast_pipeline/management/commands/restorepiperun.py b/vast_pipeline/management/commands/restorepiperun.py index 523414001..9cb3a36a2 100644 --- a/vast_pipeline/management/commands/restorepiperun.py +++ b/vast_pipeline/management/commands/restorepiperun.py @@ -6,18 +6,18 @@ from argparse import ArgumentParser from glob import glob -from django.conf import settings from django.db.models import Q from django.db import transaction from django.core.management.base import BaseCommand, CommandError from typing import Dict from vast_pipeline.models import ( - Run, Source, Measurement, Image, Association, MeasurementPair + Run, Source, Measurement, Image, Association, RelatedSource ) from vast_pipeline.pipeline.loading import update_sources from vast_pipeline.pipeline.config import PipelineConfig from vast_pipeline.pipeline.main import Pipeline +from vast_pipeline.utils.utils import timeStamped from ..helpers import get_p_run_name @@ -126,8 +126,6 @@ def restore_pipe(p_run: Run, bak_files: Dict[str, str], prev_config: PipelineCon ' Cannot restore pipeline run.' ) - del meas - logger.info("Restoring '%s' from backup parquet files.", p_run.name) # Delete any new sources @@ -149,6 +147,33 @@ def restore_pipe(p_run: Run, bak_files: Dict[str, str], prev_config: PipelineCon ) logger.debug('(type, #deleted): %s', detail_del) + # Delete newly created relations of sources that still exist after deleting + # the new sources + bak_relations = pd.read_parquet(bak_files['relations']) + db_relations = pd.DataFrame( + list(RelatedSource.objects.filter(from_source_id__run=p_run).values()) + ) + + diff = pd.merge( + db_relations, + bak_relations, + on=['from_source_id', 'to_source_id'], + how='left', + indicator='exist' + ) + + relations_to_drop = diff[diff['exist'] == 'left_only']['id'].to_numpy() + relations_to_drop = RelatedSource.objects.filter(id__in=relations_to_drop) + + with transaction.atomic(): + n_del, detail_del = relations_to_drop.delete() + logger.info( + ('Deleting left over relations after dropping new sources' + ' Total objects deleted: %i'), + n_del, + ) + logger.debug('(type, #deleted): %s', detail_del) + if monitor: current_forced_parquets = glob(os.path.join( p_run.path, 'forced_*.parquet' @@ -194,15 +219,20 @@ def restore_pipe(p_run: Run, bak_files: Dict[str, str], prev_config: PipelineCon with transaction.atomic(): p_run.image_set.remove(*images_to_remove) - # load image meas - meas = pd.concat( - [pd.read_parquet( - i, columns=['id'] - ) for i in prev_images['measurements_path']] + # load old associations to remove all new assoc + bak_assoc = pd.read_parquet( + bak_files['associations'], + columns=['source_id', 'meas_id'] ) - association_criteria_1 = Q(source_id__in=bak_sources['id'].to_numpy()) - association_criteria_2 = ~Q(meas_id__in=meas['id'].to_numpy()) + # get unique source and meas id values in the previous run + bak_source_ids = bak_assoc['source_id'].unique() + bak_meas_ids = bak_assoc['meas_id'].unique() + + # create query to only obtain associations that are not part of the + # previous run + association_criteria_1 = Q(source_id__in=bak_source_ids) + association_criteria_2 = ~Q(meas_id__in=bak_meas_ids) associations_to_delete = Association.objects.filter( association_criteria_1 and association_criteria_2 ) @@ -217,30 +247,14 @@ def restore_pipe(p_run: Run, bak_files: Dict[str, str], prev_config: PipelineCon ) logger.debug('(type, #deleted): %s', detail_del) - pair_criteria_1 = Q(source_id__in=bak_sources['id'].to_numpy()) - pair_criteria_2 = ~Q(measurement_a__in=meas['id'].to_numpy()) - pair_criteria_3 = ~Q(measurement_b__in=meas['id'].to_numpy()) - - pairs_to_delete = MeasurementPair.objects.filter( - pair_criteria_1 and (pair_criteria_2 | pair_criteria_3) - ) - - if pairs_to_delete.exists(): - with transaction.atomic(): - n_del, detail_del = pairs_to_delete.delete() - logger.info( - ('Deleting measurement pairs to restore run.' - ' Total objects deleted: %i'), - n_del, - ) - logger.debug('(type, #deleted): %s', detail_del) - logger.info('Restoring run metrics.') p_run.n_images = prev_images.shape[0] p_run.n_sources = bak_sources.shape[0] - p_run.n_selavy_measurements = meas.shape[0] if monitor: + p_run.n_selavy_measurements = meas.shape[0] p_run.n_forced_measurements = forced_meas.shape[0] + else: + p_run.n_selavy_measurements = bak_meas_ids.shape[0] with transaction.atomic(): p_run.save() @@ -289,8 +303,7 @@ def add_arguments(self, parser: ArgumentParser) -> None: """ # positional arguments (required) parser.add_argument( - 'piperuns', - nargs='+', + 'piperun', type=str, default=None, help='Name or path of pipeline run(s) to restore.' @@ -318,89 +331,99 @@ def handle(self, *args, **options) -> None: Returns: None """ + piperun = options['piperun'] + + p_run_name, run_folder = get_p_run_name(piperun, return_folder=True) + # configure logging + root_logger = logging.getLogger('') + f_handler = logging.FileHandler( + os.path.join(run_folder, timeStamped('restore_log.txt')), + mode='w' + ) + f_handler.setFormatter(root_logger.handlers[0].formatter) + root_logger.addHandler(f_handler) + if options['verbosity'] > 1: # set root logger to use the DEBUG level - root_logger = logging.getLogger('') root_logger.setLevel(logging.DEBUG) # set the traceback on options['traceback'] = True - piperuns = options['piperuns'] + try: + p_run = Run.objects.get(name=p_run_name) + except Run.DoesNotExist: + raise CommandError(f'Pipeline run {p_run_name} does not exist') - for piperun in piperuns: - p_run_name = get_p_run_name(piperun) - try: - p_run = Run.objects.get(name=p_run_name) - except Run.DoesNotExist: - raise CommandError(f'Pipeline run {p_run_name} does not exist') + if p_run.status not in ['END', 'ERR']: + raise CommandError( + f"Run {p_run_name} does not have an 'END' or 'ERR' status." + " Unable to run restore." + ) - if p_run.status not in ['END', 'ERR']: - raise CommandError( - f"Run {p_run_name} does not have an 'END' or 'ERR' status." - " Unable to run restore." - ) + path = p_run.path + pipeline = Pipeline( + name=p_run_name, + config_path=os.path.join(path, 'config.yaml') + ) + try: + # update pipeline run status to restoring + prev_status = p_run.status + pipeline.set_status(p_run, 'RES') - path = p_run.path - pipeline = Pipeline( - name=p_run_name, - config_path=os.path.join(path, 'config.yaml') - ) - try: - # update pipeline run status to restoring - prev_status = p_run.status - pipeline.set_status('RES') + prev_config_file = os.path.join(p_run.path, 'config.yaml.bak') - prev_config_file = os.path.join(p_run.path, 'config.yaml.bak') + if os.path.isfile(prev_config_file): + shutil.copy( + prev_config_file, + prev_config_file.replace('.yaml.bak', '.bak.yaml') + ) + prev_config_file = prev_config_file.replace( + '.yaml.bak', '.bak.yaml' + ) + prev_config = PipelineConfig.from_file(prev_config_file) + os.remove(prev_config_file) + else: + raise CommandError( + 'Previous config file does not exist.' + ' Cannot restore pipeline run.' + ) - if os.path.isfile(prev_config_file): - shutil.copy( - prev_config_file, - prev_config_file.replace('.yaml.bak', '.bak.yaml') - ) - prev_config_file = prev_config_file.replace( - '.yaml.bak', '.bak.yaml' - ) - prev_config = PipelineConfig.from_file(prev_config_file) - os.remove(prev_config_file) + bak_files = {} + for i in [ + 'associations', 'bands', 'images', 'measurement_pairs', + 'relations', 'skyregions', 'sources', 'config' + ]: + if i == 'config': + f_name = os.path.join(p_run.path, f'{i}.yaml.bak') else: + f_name = os.path.join(p_run.path, f'{i}.parquet.bak') + + if os.path.isfile(f_name): + bak_files[i] = f_name + elif ( + i != "measurement_pairs" + or pipeline.config["variability"]["pair_metrics"] + ): raise CommandError( - 'Previous config file does not exist.' + f'File {f_name} does not exist.' ' Cannot restore pipeline run.' ) - bak_files = {} - for i in [ - 'associations', 'bands', 'images', 'measurement_pairs', - 'relations', 'skyregions', 'sources', 'config' - ]: - if i == 'config': - f_name = os.path.join(p_run.path, f'{i}.yaml.bak') - else: - f_name = os.path.join(p_run.path, f'{i}.parquet.bak') - - if os.path.isfile(f_name): - bak_files[i] = f_name - else: - raise CommandError( - f'File {f_name} does not exist.' - ' Cannot restore pipeline run.' - ) - - logger_msg = "Will restore the run to the following config:\n" - logger.info(logger_msg + prev_config._yaml.as_yaml()) - - user_continue = True if options['no_confirm'] else yesno("Would you like to restore the run") - - if user_continue: - restore_pipe(p_run, bak_files, prev_config) - pipeline.set_status('END') - logger.info('Restore complete.') - else: - pipeline.set_status(prev_status) - logger.info('No actions performed.') + logger_msg = "Will restore the run to the following config:\n" + logger.info(logger_msg + prev_config._yaml.as_yaml()) + + user_continue = True if options['no_confirm'] else yesno("Would you like to restore the run") + + if user_continue: + restore_pipe(p_run, bak_files, prev_config) + pipeline.set_status(p_run, 'END') + logger.info('Restore complete.') + else: + pipeline.set_status(p_run, prev_status) + logger.info('No actions performed.') - except Exception as e: - logger.error('Restoring failed!') - logger.error(e) - pipeline.set_status('ERR') + except Exception as e: + logger.error('Restoring failed!') + logger.error(e) + pipeline.set_status(p_run, prev_status) diff --git a/vast_pipeline/management/commands/runpipeline.py b/vast_pipeline/management/commands/runpipeline.py index 8dd7c9416..42918afec 100644 --- a/vast_pipeline/management/commands/runpipeline.py +++ b/vast_pipeline/management/commands/runpipeline.py @@ -16,16 +16,19 @@ from django.db import transaction from django.contrib.auth.models import User from django.core.management.base import BaseCommand, CommandError + from vast_pipeline._version import __version__ as pipeline_version from vast_pipeline.pipeline.forced_extraction import remove_forced_meas from vast_pipeline.pipeline.main import Pipeline from vast_pipeline.pipeline.utils import ( get_create_p_run, create_measurements_arrow_file, - create_measurement_pairs_arrow_file, backup_parquets + create_measurement_pairs_arrow_file, backup_parquets, + create_temp_config_file ) -from vast_pipeline.utils.utils import StopWatch +from vast_pipeline.utils.utils import StopWatch, timeStamped from vast_pipeline.models import Run from ..helpers import get_p_run_name + from astropy.utils.exceptions import AstropyWarning from vast_pipeline.pipeline.errors import PipelineConfigError @@ -65,8 +68,12 @@ def run_pipe( The previous status through the UI. Defaults to 'END'. Returns: - Boolean equal to `True` on a successful completion, or in cases of - failures a CommandError is returned. + Boolean equal to `True` on a successful completion. + + Raises: + CommandError: Raised if error occurs during processing. + PipelineConfigError: Raised if an error is found in the pipeline + config. ''' path = run_dj_obj.path if run_dj_obj else path_name # set up logging for running pipeline from UI @@ -76,7 +83,7 @@ def run_pipe( if debug: root_logger.setLevel(logging.DEBUG) f_handler = logging.FileHandler( - os.path.join(path, 'log.txt'), + os.path.join(path, timeStamped('log.txt')), mode='w' ) f_handler.setFormatter(root_logger.handlers[0].formatter) @@ -94,12 +101,16 @@ def run_pipe( pipeline.config["run"]["path"], ) - # copy across config file at the start - logger.debug("Copying temp config file.") - shutil.copyfile( - os.path.join(p_run.path, 'config.yaml'), - os.path.join(p_run.path, 'config_temp.yaml') - ) + # backup the last successful outputs. + # if the run is being run again and the last status is END then the + # user is highly likely to be attempting to add images. Making the backups + # now from a guaranteed successful run is safer in case of problems + # with the config file below that causes an error. + if flag_exist: + if cli and p_run.status == 'END': + backup_parquets(p_run.path) + elif not cli and prev_ui_status == 'END': + backup_parquets(p_run.path) # validate run configuration try: @@ -123,164 +134,187 @@ def run_pipe( # parquets and proceed. # C. Additional Run on errored run: Do not backup parquets, just delete # current. + # D. Running on initialised run that errored and is still the init run. # Flag on the pipeline object on whether the addition mode is on or off. pipeline.add_mode = False pipeline.previous_parquets = {} + prev_config_exists = False - if not flag_exist: - # check for and remove any present .parquet (and .arrow) files - parquets = ( - glob.glob(os.path.join(p_run.path, "*.parquet")) - # TODO Remove arrow when vaex support is dropped. - + glob.glob(os.path.join(p_run.path, "*.arrow")) - + glob.glob(os.path.join(p_run.path, "*.bak")) - ) - for parquet in parquets: - os.remove(parquet) - else: - # Check if the status is already running or queued. Exit if this is the - # case. - if p_run.status in ['RUN', 'RES']: - logger.error( - "The pipeline run requested to process already has a running" - " or restoring status! Performing no actions. Exiting." - ) - return True - - # Check for an error status and whether any previous config file - # exists - if it doesn't exist it means the run has failed during - # the first run. In this case we want to clear anything that has gone - # on before so to do that `complete-rerun` mode is activated. - if p_run.status == "ERR" and not os.path.isfile( - os.path.join(p_run.path, "config_prev.yaml") - ): - full_rerun = True - - # Backup the previous run config - if os.path.isfile( - os.path.join(p_run.path, 'config_prev.yaml') - ): - shutil.copy( - os.path.join(p_run.path, 'config_prev.yaml'), - os.path.join(p_run.path, 'config.yaml.bak') - ) - - # Check if the run has only been initialised, if so we don't want to do - # any previous run checks or cleaning. - if p_run.status == 'INI': - initial_run = True - # check if coming from UI - elif cli is False and prev_ui_status == 'INI': - initial_run = True - else: - initial_run = False - - if initial_run is False: + try: + if not flag_exist: + # check for and remove any present .parquet (and .arrow) files parquets = ( glob.glob(os.path.join(p_run.path, "*.parquet")) # TODO Remove arrow when arrow files are no longer needed. + glob.glob(os.path.join(p_run.path, "*.arrow")) + + glob.glob(os.path.join(p_run.path, "*.bak")) ) + for parquet in parquets: + os.remove(parquet) - if full_rerun: - if p_run.status == 'END': - backup_parquets(p_run.path) - logger.info( - 'Cleaning up pipeline run before re-process data' - ) - p_run.image_set.clear() + # copy across config file at the start + logger.debug("Copying temp config file.") + create_temp_config_file(p_run.path) - logger.info( - 'Cleaning up forced measurements before re-process data' + else: + # Check if the status is already running or queued. Exit if this is + # the case. + if p_run.status in ['RUN', 'RES']: + logger.error( + "The pipeline run requested to process already has a " + "running or restoring status! Performing no actions. " + "Exiting." ) - remove_forced_meas(p_run.path) - - for parquet in parquets: - os.remove(parquet) - - # remove bak files - bak_files = glob.glob(os.path.join(p_run.path, "*.bak")) - if bak_files: - for bf in bak_files: - os.remove(bf) + return True + + # copy across config file at the start + logger.debug("Copying temp config file.") + create_temp_config_file(p_run.path) + + # Check if there is a previous run config and back up if so + if os.path.isfile( + os.path.join(p_run.path, 'config_prev.yaml') + ): + prev_config_exists = True + shutil.copy( + os.path.join(p_run.path, 'config_prev.yaml'), + os.path.join(p_run.path, 'config.yaml.bak') + ) + logger.debug(f'config_prev.yaml exists: {prev_config_exists}') + + # Check for an error status and whether any previous config file + # exists - if it doesn't exist it means the run has failed during + # the first run. In this case we want to clear anything that has + # gone on before so to do that `complete-rerun` mode is activated. + if not prev_config_exists: + if cli and p_run.status == "ERR": + full_rerun = True + elif not cli and prev_ui_status == "ERR": + full_rerun = True + logger.debug(f'Full re-run: {full_rerun}') + + # Check if the run has only been initialised, if so we don't want + # to do any previous run checks or cleaning. + if p_run.status == 'INI': + initial_run = True + # check if coming from UI + elif cli is False and prev_ui_status == 'INI': + initial_run = True + else: + initial_run = False - # remove previous config if it exists - if os.path.isfile(os.path.join(p_run.path, 'config_prev.yaml')): - os.remove(os.path.join(p_run.path, 'config_prev.yaml')) + if initial_run is False: + parquets = ( + glob.glob(os.path.join(p_run.path, "*.parquet")) + # TODO Remove arrow when arrow files are no longer needed. + + glob.glob(os.path.join(p_run.path, "*.arrow")) + ) - # reset epoch_based flag - with transaction.atomic(): - p_run.epoch_based = False - p_run.save() - else: - # Before parquets are started to be copied and backed up, a - # check is run to see if anything has actually changed in - # the config - config_diff = pipeline.config.check_prev_config_diff() - if config_diff: + if full_rerun: logger.info( - "The config file has either not changed since the" - " previous run or other settings have changed such" - " that a new or complete re-run should be performed" - " instead. Performing no actions. Exiting." + 'Cleaning up pipeline run before re-process data' ) - os.remove(os.path.join(p_run.path, 'config_temp.yaml')) - pipeline.set_status(p_run, 'END') + p_run.image_set.clear() - return True - - if pipeline.config.epoch_based != p_run.epoch_based: logger.info( - "The 'epoch based' setting has changed since the" - " previous run. A complete re-run is required if" - " changing to epoch based mode or vice versa." + 'Cleaning up forced measurements before re-process data' ) - os.remove(os.path.join(p_run.path, 'config_temp.yaml')) - pipeline.set_status(p_run, 'END') - return True - - if cli and p_run.status == 'END': - backup_parquets(p_run.path) - elif not cli and prev_ui_status == 'END': - backup_parquets(p_run.path) - - pipeline.add_mode = True - for i in [ - 'images', 'associations', 'sources', 'relations', - 'measurement_pairs' - ]: - pipeline.previous_parquets[i] = os.path.join( - p_run.path, f'{i}.parquet.bak') + remove_forced_meas(p_run.path) + + for parquet in parquets: + os.remove(parquet) + + # remove bak files + bak_files = glob.glob(os.path.join(p_run.path, "*.bak")) + if bak_files: + for bf in bak_files: + os.remove(bf) + + # remove previous config if it exists + if prev_config_exists: + os.remove(os.path.join(p_run.path, 'config_prev.yaml')) + + # reset epoch_based flag + with transaction.atomic(): + p_run.epoch_based = False + p_run.save() + else: + # Before parquets are started to be copied and backed up, a + # check is run to see if anything has actually changed in + # the config + config_diff = pipeline.config.check_prev_config_diff() + if config_diff: + logger.info( + "The config file has either not changed since the" + " previous run or other settings have changed such" + " that a new or complete re-run should be performed" + " instead. Performing no actions. Exiting." + ) + os.remove(os.path.join(p_run.path, 'config_temp.yaml')) + pipeline.set_status(p_run, 'END') + + return True + + if pipeline.config.epoch_based != p_run.epoch_based: + logger.info( + "The 'epoch based' setting has changed since the" + " previous run. A complete re-run is required if" + " changing to epoch based mode or vice versa." + ) + os.remove(os.path.join(p_run.path, 'config_temp.yaml')) + pipeline.set_status(p_run, 'END') + return True + + pipeline.add_mode = True + + for i in [ + 'images', 'associations', 'sources', 'relations', + 'measurement_pairs' + ]: + pipeline.previous_parquets[i] = os.path.join( + p_run.path, f'{i}.parquet.bak') + except Exception as e: + logger.error('Unexpected error occurred in pre-run steps!') + pipeline.set_status(p_run, 'ERR') + logger.exception('Processing error:\n%s', e) + raise CommandError(f'Processing error:\n{e}') if pipeline.config["run"]["suppress_astropy_warnings"]: warnings.simplefilter("ignore", category=AstropyWarning) logger.info("VAST Pipeline version: %s", pipeline_version) - logger.info("Source finder: %s", pipeline.config["measurements"]["source_finder"]) + logger.info( + "Source finder: %s", + pipeline.config["measurements"]["source_finder"] + ) logger.info("Using pipeline run '%s'", pipeline.name) - logger.info("Source monitoring: %s", pipeline.config["source_monitoring"]["monitor"]) + logger.info( + "Source monitoring: %s", + pipeline.config["source_monitoring"]["monitor"] + ) # log the list of input data files for posterity + inputs = pipeline.config["inputs"] input_image_list = [ image - for image_list in pipeline.config["inputs"]["image"].values() + for image_list in inputs["image"].values() for image in image_list ] input_selavy_list = [ selavy - for selavy_list in pipeline.config["inputs"]["selavy"].values() + for selavy_list in inputs["selavy"].values() for selavy in selavy_list ] input_noise_list = [ noise - for noise_list in pipeline.config["inputs"]["noise"].values() + for noise_list in inputs["noise"].values() for noise in noise_list ] - if "background" in pipeline.config["inputs"].keys(): + if "background" in inputs.keys(): input_background_list = [ background - for background_list in pipeline.config["inputs"]["background"].values() + for background_list in inputs["background"].values() for background in background_list ] else: @@ -308,7 +342,8 @@ def run_pipe( # Create arrow file after success if selected. if pipeline.config["measurements"]["write_arrow_files"]: create_measurements_arrow_file(p_run) - create_measurement_pairs_arrow_file(p_run) + if pipeline.config["variability"]["pair_metrics"]: + create_measurement_pairs_arrow_file(p_run) except Exception as e: # set the pipeline status as error pipeline.set_status(p_run, 'ERR') @@ -385,7 +420,7 @@ def handle(self, *args, **options) -> None: # configure logging root_logger = logging.getLogger('') f_handler = logging.FileHandler( - os.path.join(run_folder, 'log.txt'), + os.path.join(run_folder, timeStamped('log.txt')), mode='w' ) f_handler.setFormatter(root_logger.handlers[0].formatter) diff --git a/vast_pipeline/management/helpers.py b/vast_pipeline/management/helpers.py index e6781b6aa..c2bbca310 100644 --- a/vast_pipeline/management/helpers.py +++ b/vast_pipeline/management/helpers.py @@ -12,7 +12,7 @@ logger = logging.getLogger(__name__) -def get_p_run_name(name: str, return_folder: bool=False) -> Tuple[str, str]: +def get_p_run_name(name: str, return_folder: bool = False) -> Tuple[str, str]: """ Determines the name of the pipeline run. Can also return the output folder if selected. @@ -22,8 +22,8 @@ def get_p_run_name(name: str, return_folder: bool=False) -> Tuple[str, str]: return_folder: When `True` the pipeline directory is also returned. Returns: - The name of the pipeline run. If return_folder is `True` then both - the name and directory are returned. + The name of the pipeline run (always returned). + The run directory (if `return_folder` is set to `True`). """ if '/' in name: folder = os.path.realpath(name) diff --git a/vast_pipeline/migrations/0006_alter_run_status.py b/vast_pipeline/migrations/0006_alter_run_status.py new file mode 100644 index 000000000..957f5faf0 --- /dev/null +++ b/vast_pipeline/migrations/0006_alter_run_status.py @@ -0,0 +1,18 @@ +# Generated by Django 3.2.2 on 2021-09-09 11:20 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('vast_pipeline', '0005_merge_0004_auto_20210521_1339_0004_source_name_change'), + ] + + operations = [ + migrations.AlterField( + model_name='run', + name='status', + field=models.CharField(choices=[('INI', 'Initialised'), ('QUE', 'Queued'), ('RUN', 'Running'), ('END', 'Completed'), ('ERR', 'Error'), ('RES', 'Restoring'), ('DEL', 'Deleting')], default='INI', help_text='Status of the pipeline run.', max_length=3), + ), + ] diff --git a/vast_pipeline/migrations/0007_alter_measurement_name.py b/vast_pipeline/migrations/0007_alter_measurement_name.py new file mode 100644 index 000000000..b9f54e7b9 --- /dev/null +++ b/vast_pipeline/migrations/0007_alter_measurement_name.py @@ -0,0 +1,18 @@ +# Generated by Django 3.2.2 on 2021-10-11 20:07 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('vast_pipeline', '0006_alter_run_status'), + ] + + operations = [ + migrations.AlterField( + model_name='measurement', + name='name', + field=models.CharField(max_length=64), + ), + ] diff --git a/vast_pipeline/migrations/0008_delete_measurementpair.py b/vast_pipeline/migrations/0008_delete_measurementpair.py new file mode 100644 index 000000000..f103feb54 --- /dev/null +++ b/vast_pipeline/migrations/0008_delete_measurementpair.py @@ -0,0 +1,16 @@ +# Generated by Django 3.2.9 on 2021-12-03 20:25 + +from django.db import migrations + + +class Migration(migrations.Migration): + + dependencies = [ + ('vast_pipeline', '0007_alter_measurement_name'), + ] + + operations = [ + migrations.DeleteModel( + name='MeasurementPair', + ), + ] diff --git a/vast_pipeline/migrations/0009_add_log_file_timestamps.py b/vast_pipeline/migrations/0009_add_log_file_timestamps.py new file mode 100644 index 000000000..e086674b0 --- /dev/null +++ b/vast_pipeline/migrations/0009_add_log_file_timestamps.py @@ -0,0 +1,108 @@ +# Generated by Django 3.2.9 on 2022-01-14 13:30 + +# This is a migration to rename the log files of old runs prior to when +# they were timestamped. No DB changes occur in this migration. + +import shutil +import warnings + +from django.db import migrations +from pathlib import Path + + +def get_timestamp(log_path: Path) -> str: + """ + Opens the provided log file and extracts the timestamp from the + first line. + + Args: + log_path: The Path object for the log file. + + Returns: + The string representation of the timestamp in the format of + YYYY-MM-DD-HH-MM-SS. + """ + with log_path.open() as f: + line = f.readline() + + timestamp = ( + line.split(',')[0] + .replace(' ', '-') + .replace(':', '-') + ) + + return timestamp + + +def add_timestamp_and_copy(log_path: Path) -> None: + """ + Creates a new log file with the timestamp added to the name. + + Args: + log_path: The Path object for the log file. + + Returns: + None + """ + timestamp = get_timestamp(log_path) + + new_log_name = f"{timestamp}_{log_path.name}" + new_log_path = log_path.parent / new_log_name + + # check if already exists, pretty much impossible + if new_log_path.exists(): + warnings.warn( + f"Log file '{log_path}' not migrated as log file with timestamp" + f" '{new_log_name}' already exists!" + ) + return + + else: + try: + shutil.copy(log_path, new_log_path) + except Exception as e: + warnings.warn( + f"Log file '{log_path}' could not be migrated as the copy" + " operation failed. Please migrate this log file manually." + f"Copy error: {e}." + ) + return + + # a final check before deletion. + if new_log_path.exists(): + log_path.unlink() + else: + warnings.warn( + "While the copy operation was apparently successful the" + f" new log file '{new_log_path}' does not exist. Please" + f" migrate the old log file '{log_path}' manually." + ) + + +def rename_logs(apps, schema_editor) -> None: + """ + Loops through the existing runs and adds timestamps to the log files + where required. + """ + Run = apps.get_model('vast_pipeline', 'Run') + + # old names of logs before timestamping was introduced. + logs_to_check = ['log.txt', 'restore_log.txt', 'gen_arrow_log.txt'] + + for run in Run.objects.all(): + run_path = Path(run.path) + for log in logs_to_check: + log_path = run_path / log + if log_path.exists(): + add_timestamp_and_copy(log_path) + + +class Migration(migrations.Migration): + + dependencies = [ + ('vast_pipeline', '0008_delete_measurementpair'), + ] + + operations = [ + migrations.RunPython(rename_logs), + ] diff --git a/vast_pipeline/migrations/0010_update_source_names_iau_compliant.py b/vast_pipeline/migrations/0010_update_source_names_iau_compliant.py new file mode 100644 index 000000000..d3aefdfb5 --- /dev/null +++ b/vast_pipeline/migrations/0010_update_source_names_iau_compliant.py @@ -0,0 +1,68 @@ +from django.db import migrations, transaction +from vast_pipeline.utils.utils import deg2hms, deg2dms + + +def _make_source_name(ra: float, dec: float, iau: bool = True) -> str: + return ( + f"J{deg2hms(ra, precision=1, truncate=iau)}" + f"{deg2dms(dec, precision=0, truncate=iau)}" + ).replace(":", "") + + +def update_source_names(apps, schema_editor): + """Update source names that were generated by rounding coordinates instead of + truncating. Truncating is required by IAU convention.""" + Source = apps.get_model("vast_pipeline", "Source") + + BATCH_SIZE = 1000 + sources = Source.objects.filter(name__startswith="J") + start = 0 + end = start + BATCH_SIZE + sources_batch = sources[start:end] + while sources_batch.exists(): + with transaction.atomic(): + sources_batch_list = list(sources_batch) + for source in sources_batch_list: + source.name = _make_source_name( + source.wavg_ra, source.wavg_dec, iau=True + ) + _ = Source.objects.bulk_update(sources_batch_list, ["name"]) + start = end + end = start + BATCH_SIZE + sources_batch = sources[start:end] + + +def reverse_update_source_names(apps, schema_editor): + """Undo the above update source names by rounding coordinates instead of truncating.""" + Source = apps.get_model("vast_pipeline", "Source") + + BATCH_SIZE = 1000 + sources = Source.objects.filter(name__startswith="J") + start = 0 + end = start + BATCH_SIZE + sources_batch = sources[start:end] + while sources_batch.exists(): + with transaction.atomic(): + sources_batch_list = list(sources_batch) + for source in sources_batch_list: + source.name = _make_source_name( + source.wavg_ra, source.wavg_dec, iau=False + ) + _ = Source.objects.bulk_update(sources_batch_list, ["name"]) + start = end + end = start + BATCH_SIZE + sources_batch = sources[start:end] + + +class Migration(migrations.Migration): + atomic = False + + dependencies = [ + ("vast_pipeline", "0009_add_log_file_timestamps"), + ] + + operations = [ + migrations.RunPython( + update_source_names, reverse_code=reverse_update_source_names, atomic=False + ), + ] diff --git a/vast_pipeline/migrations/0011_run_n_new_sources.py b/vast_pipeline/migrations/0011_run_n_new_sources.py new file mode 100644 index 000000000..44a42cd26 --- /dev/null +++ b/vast_pipeline/migrations/0011_run_n_new_sources.py @@ -0,0 +1,32 @@ +# Generated by Django 3.2.13 on 2023-01-25 18:13 + +from django.db import migrations, models, transaction + + +class Migration(migrations.Migration): + + dependencies = [ + ('vast_pipeline', '0010_update_source_names_iau_compliant'), + ] + + def fill_new_source_counts(apps, schema_editor): + """Loop over Run objects and count the number of new sources and save to new column.""" + Run = apps.get_model("vast_pipeline", "Run") + Source = apps.get_model("vast_pipeline", "Source") + + runs = Run.objects.all() + + for run in runs: + with transaction.atomic(): + n_new_sources = Source.objects.filter(run=run, new=True).count() + run.n_new_sources = n_new_sources + run.save() + + operations = [ + migrations.AddField( + model_name='run', + name='n_new_sources', + field=models.IntegerField(default=0, help_text='number of new sources in this run'), + ), + migrations.RunPython(fill_new_source_counts, atomic=False) + ] diff --git a/vast_pipeline/models.py b/vast_pipeline/models.py index db81df592..127a036b2 100644 --- a/vast_pipeline/models.py +++ b/vast_pipeline/models.py @@ -1,5 +1,8 @@ -import logging -import math +from dataclasses import dataclass +from itertools import combinations +from pathlib import Path +from typing import List + from django.db import models from django.core.validators import RegexValidator from django.contrib.auth.models import User @@ -8,6 +11,20 @@ from django.templatetags.static import static from social_django.models import UserSocialAuth from tagulous.models import TagField +from vast_pipeline.pipeline.config import PipelineConfig + +from vast_pipeline.pipeline.pairs import calculate_vs_metric, calculate_m_metric + + +@dataclass +class MeasurementPair: + source_id: int + measurement_a_id: int + measurement_b_id: int + vs_peak: float + m_peak: float + vs_int: float + m_int: float class Comment(models.Model): @@ -113,6 +130,7 @@ class Run(CommentableModel): ('END', 'Completed'), ('ERR', 'Error'), ('RES', 'Restoring'), + ('DEL', 'Deleting'), ] status = models.CharField( max_length=3, @@ -136,6 +154,10 @@ class Run(CommentableModel): default=0, help_text='number of forced measurements in this run' ) + n_new_sources = models.IntegerField( + default=0, + help_text='number of new sources in this run' + ) epoch_based = models.BooleanField( default=False, help_text=( @@ -158,6 +180,31 @@ def save(self, *args, **kwargs): self.full_clean() super(Run, self).save(*args, **kwargs) + def get_config( + self, validate: bool = True, validate_inputs: bool = True, prev: bool = False + ) -> PipelineConfig: + """Read, parse, and optionally validate the run configuration file. + + Args: + validate: Validate the run configuration. Defaults to False. + validate_inputs: Validate the config input files. Ensures + that the inputs match (e.g. each image has a catalogue), and that each + path exists. Set to False to skip these checks. Defaults to True. + prev: Get the previous config file instead of the current config. The + previous config is the one used for the last successfully completed run. + The current config may have been modified since the run was executed. + + Returns: + PipelineConfig: The run configuration object. + """ + config_name = "config_prev.yaml" if prev else "config.yaml" + config = PipelineConfig.from_file( + str(Path(self.path) / config_name), + validate=validate, + validate_inputs=validate_inputs, + ) + return config + class Band(models.Model): """ @@ -227,176 +274,6 @@ def cone_search( ) -class Source(CommentableModel): - run = models.ForeignKey(Run, on_delete=models.CASCADE, null=True,) - related = models.ManyToManyField( - 'self', - through='RelatedSource', - symmetrical=False, - through_fields=('from_source', 'to_source') - ) - - name = models.CharField(max_length=100) - new = models.BooleanField(default=False, help_text='New Source.') - tags = TagField( - space_delimiter=False, - autocomplete_view="vast_pipeline:source_tags_autocomplete", - autocomplete_settings={"width": "100%"}, - ) - - # average fields calculated from the source measurements - wavg_ra = models.FloatField( - help_text='The weighted average right ascension (Deg).' - ) - wavg_dec = models.FloatField( - help_text='The weighted average declination (Deg).' - ) - wavg_uncertainty_ew = models.FloatField( - help_text=( - 'The weighted average uncertainty in the east-' - 'west (RA) direction (Deg).' - ) - ) - wavg_uncertainty_ns = models.FloatField( - help_text=( - 'The weighted average uncertainty in the north-' - 'south (Dec) direction (Deg).' - ) - ) - avg_flux_int = models.FloatField( - help_text='The average integrated flux value.' - ) - avg_flux_peak = models.FloatField( - help_text='The average peak flux value.' - ) - max_flux_peak = models.FloatField( - help_text='The maximum peak flux value.' - ) - min_flux_peak = models.FloatField( - help_text='The minimum peak flux value.' - ) - max_flux_int = models.FloatField( - help_text='The maximum integrated flux value.' - ) - min_flux_int = models.FloatField( - help_text='The minimum integrated flux value.' - ) - min_flux_int_isl_ratio = models.FloatField( - help_text='The minimum integrated island flux ratio value.' - ) - min_flux_peak_isl_ratio = models.FloatField( - help_text='The minimum peak island flux ratio value.' - ) - avg_compactness = models.FloatField( - help_text='The average compactness.' - ) - min_snr = models.FloatField( - help_text='The minimum signal-to-noise ratio value of the detections.' - ) - max_snr = models.FloatField( - help_text='The maximum signal-to-noise ratio value of the detections.' - ) - - # metrics - v_int = models.FloatField( - help_text='V metric for int flux.' - ) - v_peak = models.FloatField( - help_text='V metric for peak flux.' - ) - eta_int = models.FloatField( - help_text='Eta metric for int flux.' - ) - eta_peak = models.FloatField( - help_text='Eta metric for peak flux.' - ) - new_high_sigma = models.FloatField( - help_text=( - 'The largest sigma value for the new source' - ' if it was placed in previous image.' - ) - ) - n_neighbour_dist = models.FloatField( - help_text='Distance to the nearest neighbour (deg)' - ) - vs_abs_significant_max_int = models.FloatField( - default=0.0, - help_text=( - 'Maximum value of all measurement pair variability t-statistics for int' - ' flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs in' - ' the pipeline run configuration.' - ) - ) - m_abs_significant_max_int = models.FloatField( - default=0.0, - help_text=( - 'Maximum absolute value of all measurement pair modulation indices for int' - ' flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs in' - ' the pipeline run configuration.' - ) - ) - vs_abs_significant_max_peak = models.FloatField( - default=0.0, - help_text=( - 'Maximum absolute value of all measurement pair variability t-statistics for' - ' peak flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs' - ' in the pipeline run configuration.' - ) - ) - m_abs_significant_max_peak = models.FloatField( - default=0.0, - help_text=( - 'Maximum absolute value of all measurement pair modulation indices for ' - ' peak flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs' - ' in the pipeline run configuration.' - ) - ) - - - # total metrics to report in UI - n_meas = models.IntegerField( - help_text='total measurements of the source' - ) - n_meas_sel = models.IntegerField( - help_text='total selavy extracted measurements of the source' - ) - n_meas_forced = models.IntegerField( - help_text='total force extracted measurements of the source' - ) - n_rel = models.IntegerField( - help_text='total relations of the source with other sources' - ) - n_sibl = models.IntegerField( - help_text='total siblings of the source' - ) - - objects = SourceQuerySet.as_manager() - - def __str__(self): - return self.name - - -class RelatedSource(models.Model): - ''' - Association table for the many to many Source relationship with itself - Django doc https://docs.djangoproject.com/en/3.1/ref/models/fields/#django.db.models.ManyToManyField.through - ''' - from_source = models.ForeignKey(Source, on_delete=models.CASCADE) - to_source = models.ForeignKey( - Source, - on_delete=models.CASCADE, - related_name='related_sources' - ) - - class Meta: - constraints = [ - models.UniqueConstraint( - name='%(app_label)s_%(class)s_unique_pair', - fields=['from_source', 'to_source'] - ) - ] - - class Image(CommentableModel): """An image is a 2D radio image from a cube""" band = models.ForeignKey(Band, on_delete=models.CASCADE) @@ -451,7 +328,7 @@ class Image(CommentableModel): jd = models.FloatField( help_text='Julian date of the observation (days).' ) - duration = models.FloatField( + duration = models.FloatField( default=0., help_text='Duration of the observation.' ) @@ -464,16 +341,16 @@ class Image(CommentableModel): ) fov_bmaj = models.FloatField( help_text='Field of view major axis (Deg).' - )# Major (Dec) radius of image (degrees) + ) # Major (Dec) radius of image (degrees) fov_bmin = models.FloatField( help_text='Field of view minor axis (Deg).' - )# Minor (RA) radius of image (degrees) + ) # Minor (RA) radius of image (degrees) physical_bmaj = models.FloatField( help_text='The actual size of the image major axis (Deg).' - )# Major (Dec) radius of image (degrees) + ) # Major (Dec) radius of image (degrees) physical_bmin = models.FloatField( help_text='The actual size of the image minor axis (Deg).' - )# Minor (RA) radius of image (degrees) + ) # Minor (RA) radius of image (degrees) radius_pixels = models.FloatField( help_text='Radius of the useable region of the image (pixels).' ) @@ -545,20 +422,20 @@ class Measurement(CommentableModel): Image, null=True, on_delete=models.CASCADE - )# first image seen in + ) # first image seen in source = models.ManyToManyField( - Source, + 'Source', through='Association', through_fields=('meas', 'source') ) - name = models.CharField(max_length=64, unique=True) + name = models.CharField(max_length=64) - ra = models.FloatField(help_text='RA of the source (Deg).')# degrees + ra = models.FloatField(help_text='RA of the source (Deg).') # degrees ra_err = models.FloatField( help_text='RA error of the source (Deg).' ) - dec = models.FloatField(help_text='DEC of the source (Deg).')# degrees + dec = models.FloatField(help_text='DEC of the source (Deg).') # degrees dec_err = models.FloatField( help_text='DEC error of the source (Deg).' ) @@ -608,7 +485,7 @@ class Measurement(CommentableModel): ' error_radius and ew_sys_err (Deg).' ) ) - # quadratic sum of error_radius and ns_sys_err + # quadratic sum of error_radius and ns_sys_err uncertainty_ns = models.FloatField( help_text=( 'Total north-south (Dec) uncertainty, quadratic sum of ' @@ -616,16 +493,16 @@ class Measurement(CommentableModel): ) ) - flux_int = models.FloatField()# mJy/beam - flux_int_err = models.FloatField()# mJy/beam + flux_int = models.FloatField() # mJy/beam + flux_int_err = models.FloatField() # mJy/beam flux_int_isl_ratio = models.FloatField( help_text=( 'Ratio of the component integrated flux to the total' ' island integrated flux.' ) ) - flux_peak = models.FloatField()# mJy/beam - flux_peak_err = models.FloatField()# mJy/beam + flux_peak = models.FloatField() # mJy/beam + flux_peak_err = models.FloatField() # mJy/beam flux_peak_isl_ratio = models.FloatField( help_text=( 'Ratio of the component peak flux to the total' @@ -651,7 +528,7 @@ class Measurement(CommentableModel): local_rms = models.FloatField( help_text='Local rms in mJy from Selavy.' - )# mJy/beam + ) # mJy/beam snr = models.FloatField( help_text='Signal-to-noise ratio of the measurement.' @@ -697,6 +574,194 @@ def __str__(self): return self.name +class Source(CommentableModel): + run = models.ForeignKey(Run, on_delete=models.CASCADE, null=True,) + related = models.ManyToManyField( + 'self', + through='RelatedSource', + symmetrical=False, + through_fields=('from_source', 'to_source') + ) + + name = models.CharField(max_length=100) + new = models.BooleanField(default=False, help_text='New Source.') + tags = TagField( + space_delimiter=False, + autocomplete_view="vast_pipeline:source_tags_autocomplete", + autocomplete_settings={"width": "100%"}, + ) + + # average fields calculated from the source measurements + wavg_ra = models.FloatField( + help_text='The weighted average right ascension (Deg).' + ) + wavg_dec = models.FloatField( + help_text='The weighted average declination (Deg).' + ) + wavg_uncertainty_ew = models.FloatField( + help_text=( + 'The weighted average uncertainty in the east-' + 'west (RA) direction (Deg).' + ) + ) + wavg_uncertainty_ns = models.FloatField( + help_text=( + 'The weighted average uncertainty in the north-' + 'south (Dec) direction (Deg).' + ) + ) + avg_flux_int = models.FloatField( + help_text='The average integrated flux value.' + ) + avg_flux_peak = models.FloatField( + help_text='The average peak flux value.' + ) + max_flux_peak = models.FloatField( + help_text='The maximum peak flux value.' + ) + min_flux_peak = models.FloatField( + help_text='The minimum peak flux value.' + ) + max_flux_int = models.FloatField( + help_text='The maximum integrated flux value.' + ) + min_flux_int = models.FloatField( + help_text='The minimum integrated flux value.' + ) + min_flux_int_isl_ratio = models.FloatField( + help_text='The minimum integrated island flux ratio value.' + ) + min_flux_peak_isl_ratio = models.FloatField( + help_text='The minimum peak island flux ratio value.' + ) + avg_compactness = models.FloatField( + help_text='The average compactness.' + ) + min_snr = models.FloatField( + help_text='The minimum signal-to-noise ratio value of the detections.' + ) + max_snr = models.FloatField( + help_text='The maximum signal-to-noise ratio value of the detections.' + ) + + # metrics + v_int = models.FloatField( + help_text='V metric for int flux.' + ) + v_peak = models.FloatField( + help_text='V metric for peak flux.' + ) + eta_int = models.FloatField( + help_text='Eta metric for int flux.' + ) + eta_peak = models.FloatField( + help_text='Eta metric for peak flux.' + ) + new_high_sigma = models.FloatField( + help_text=( + 'The largest sigma value for the new source' + ' if it was placed in previous image.' + ) + ) + n_neighbour_dist = models.FloatField( + help_text='Distance to the nearest neighbour (deg)' + ) + vs_abs_significant_max_int = models.FloatField( + default=0.0, + help_text=( + 'Maximum value of all measurement pair variability t-statistics for int' + ' flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs in' + ' the pipeline run configuration.' + ) + ) + m_abs_significant_max_int = models.FloatField( + default=0.0, + help_text=( + 'Maximum absolute value of all measurement pair modulation indices for int' + ' flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs in' + ' the pipeline run configuration.' + ) + ) + vs_abs_significant_max_peak = models.FloatField( + default=0.0, + help_text=( + 'Maximum absolute value of all measurement pair variability t-statistics for' + ' peak flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs' + ' in the pipeline run configuration.' + ) + ) + m_abs_significant_max_peak = models.FloatField( + default=0.0, + help_text=( + 'Maximum absolute value of all measurement pair modulation indices for ' + ' peak flux that exceed variability.source_aggregate_pair_metrics_min_abs_vs' + ' in the pipeline run configuration.' + ) + ) + + # total metrics to report in UI + n_meas = models.IntegerField( + help_text='total measurements of the source' + ) + n_meas_sel = models.IntegerField( + help_text='total selavy extracted measurements of the source' + ) + n_meas_forced = models.IntegerField( + help_text='total force extracted measurements of the source' + ) + n_rel = models.IntegerField( + help_text='total relations of the source with other sources' + ) + n_sibl = models.IntegerField( + help_text='total siblings of the source' + ) + + objects = SourceQuerySet.as_manager() + + def __str__(self): + return self.name + + def get_measurement_pairs(self) -> List[MeasurementPair]: + """Calculate the measurement pair metrics for the source. If the run config + set variability.pair_metrics to false, then no pairs are calculated and an empty + list is returned. + + Returns: + List[MeasurementPair]: The list of measurement pairs and their metrics. + """ + # do not calculate pair metrics if it was disabled in the run config + config = self.run.get_config(validate=False, validate_inputs=False, prev=True) + # validate the config schema only, not the full validation executed by + # PipelineConfig.validate. + config._yaml.revalidate(PipelineConfig.SCHEMA) + if not config["variability"]["pair_metrics"]: + return [] + + measurements = ( + Measurement.objects.filter(source=self) + .select_related("image") + .order_by("image__datetime") + ) + measurement_pairs: List[MeasurementPair] = [] + for meas_a, meas_b in combinations(measurements, 2): + # ensure the measurements are in time order + if meas_a.image.datetime > meas_b.image.datetime: + meas_a, meas_b = meas_b, meas_a + # calculate metrics + vs_peak = calculate_vs_metric( + meas_a.flux_peak, meas_b.flux_peak, meas_a.flux_peak_err, meas_b.flux_peak_err + ) + m_int = calculate_m_metric(meas_a.flux_int, meas_b.flux_int) + vs_int = calculate_vs_metric( + meas_a.flux_int, meas_b.flux_int, meas_a.flux_int_err, meas_b.flux_int_err + ) + m_peak = calculate_m_metric(meas_a.flux_peak, meas_b.flux_peak) + measurement_pairs.append( + MeasurementPair(self.id, meas_a.id, meas_b.id, vs_peak, m_peak, vs_int, m_int) + ) + return measurement_pairs + + class Association(models.Model): """ model association between sources and measurements based on @@ -721,6 +786,28 @@ def __str__(self): ) +class RelatedSource(models.Model): + ''' + Association table for the many to many Source relationship with itself + Django doc + https://docs.djangoproject.com/en/3.1/ref/models/fields/#django.db.models.ManyToManyField.through + ''' + from_source = models.ForeignKey(Source, on_delete=models.CASCADE) + to_source = models.ForeignKey( + Source, + on_delete=models.CASCADE, + related_name='related_sources' + ) + + class Meta: + constraints = [ + models.UniqueConstraint( + name='%(app_label)s_%(class)s_unique_pair', + fields=['from_source', 'to_source'] + ) + ] + + class SourceFav(models.Model): user = models.ForeignKey(User, on_delete=models.CASCADE) source = models.ForeignKey(Source, on_delete=models.CASCADE) @@ -731,35 +818,3 @@ class SourceFav(models.Model): blank=True, help_text='Why did you include this as favourite' ) - - -class MeasurementPair(models.Model): - """Links two Measurement objects from the same Source and stores two variability metrics - for peak and integrated fluxes: - - `vs_peak` and `vs_int` is the variability t-statistic. e.g. if Vs is 4.3, then - the source is considered variable to a 95% CI. - - `m_peak` and `m_int` is the modulation index, related to fractional variability. - See Section 5 of Mooley et al. (2016) for details, DOI: 10.3847/0004-637X/818/2/105. - """ - source = models.ForeignKey(Source, on_delete=models.CASCADE) - measurement_a = models.ForeignKey( - Measurement, related_name="measurement_pairs_a", on_delete=models.CASCADE - ) - measurement_b = models.ForeignKey( - Measurement, related_name="measurement_pairs_b", on_delete=models.CASCADE - ) - vs_peak = models.FloatField(help_text="Variability metric: t-statistic for peak fluxes.") - m_peak = models.FloatField(help_text="Variability metric: modulation index for peak fluxes.") - vs_int = models.FloatField(help_text="Variability metric: t-statistic for integrated fluxes.") - m_int = models.FloatField(help_text="Variability metric: modulation index for integrated fluxes.") - - class Meta: - constraints = [ - models.UniqueConstraint( - fields=["source", "measurement_a", "measurement_b"], - name="%(app_label)s_%(class)s_unique_pair" - ) - ] - - def __str__(self): - return f"({self.measurement_a}, {self.measurement_b})" diff --git a/vast_pipeline/pipeline/association.py b/vast_pipeline/pipeline/association.py index 7bf24a1d8..d5e46cb09 100644 --- a/vast_pipeline/pipeline/association.py +++ b/vast_pipeline/pipeline/association.py @@ -19,9 +19,8 @@ add_new_many_to_one_relations, reconstruct_associtaion_dfs ) -from vast_pipeline.models import Association from vast_pipeline.pipeline.config import PipelineConfig -from vast_pipeline.utils.utils import StopWatch +from vast_pipeline.utils.utils import StopWatch, calculate_n_partitions logger = logging.getLogger(__name__) @@ -79,8 +78,9 @@ def calc_de_ruiter(df: pd.DataFrame) -> np.ndarray: def one_to_many_basic( - skyc2_srcs: pd.DataFrame, sources_df: pd.DataFrame, - id_incr_par_assoc: int=0 + skyc2_srcs: pd.DataFrame, + sources_df: pd.DataFrame, + id_incr_par_assoc: int = 0 ) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Finds and processes the one-to-many associations in the basic @@ -103,8 +103,10 @@ def one_to_many_basic( association Returns: - Tuple containging the updated 'skyc2_srcs' and 'sources_df' with - all one_to_many relation information added. + Updated 'skyc2_srcs' with all the one_to_many relation information + added. + Updated 'sources_df' with all the one_to_many relation information + added. """ # select duplicated in 'source' field in skyc2_srcs, excluding -1 duplicated_skyc2 = skyc2_srcs.loc[ @@ -223,7 +225,7 @@ def one_to_many_basic( axis=1 ) - duplicated_skyc2 = original.append(not_original) + duplicated_skyc2 = pd.concat([original, not_original]) # duplicated_skyc2 # +-----+----------+-----------+---------+-----------------+ @@ -303,9 +305,9 @@ def one_to_many_basic( # Reset the related column to avoid rogue relations sources_to_copy['related'] = None - # and finally append. - sources_df = sources_df.append( - sources_to_copy, + # and finally concatenate. + sources_df = pd.concat( + [sources_df, sources_to_copy], ignore_index=True ) @@ -316,7 +318,7 @@ def one_to_many_advanced( temp_srcs: pd.DataFrame, sources_df: pd.DataFrame, method: str, - id_incr_par_assoc: int=0 + id_incr_par_assoc: int = 0 ) -> Tuple[pd.DataFrame, pd.DataFrame]: ''' Finds and processes the one-to-many associations in the advanced @@ -344,29 +346,31 @@ def one_to_many_advanced( Mainly useful for add mode with parallel association Returns: - Updated temp_srcs and sources_df with all one_to_many relation - information added. + Updated `temp_srcs` dataframe with all the one_to_many relation + information added. + Updated `sources_df` dataframe with all the one_to_many relation + information added. ''' # use only these columns for easy debugging of the dataframe cols = [ - 'index_old_skyc1', 'id_skyc1', 'source_skyc1', 'd2d_skyc1', + 'index_old_skyc1', 'id_skyc1', 'source_skyc1', 'related_skyc1', 'index_old_skyc2', 'id_skyc2', 'source_skyc2', - 'd2d_skyc2', 'dr' + 'd2d', 'dr' ] duplicated_skyc1 = temp_srcs.loc[ temp_srcs['source_skyc1'].duplicated(keep=False), cols ].copy() # duplicated_skyc1 - # +-----+-------------------+------------+----------------+-------------+ - # | | index_old_skyc1 | id_skyc1 | source_skyc1 | d2d_skyc1 | - # |-----+-------------------+------------+----------------+-------------+ - # | 117 | 121 | 122 | 122 | 0 | - # | 118 | 121 | 122 | 122 | 0 | - # | 238 | 253 | 254 | 254 | 0 | - # | 239 | 253 | 254 | 254 | 0 | - # | 246 | 261 | 262 | 262 | 0 | - # +-----+-------------------+------------+----------------+-------------+ + # +-----+-------------------+------------+----------------+ + # | | index_old_skyc1 | id_skyc1 | source_skyc1 | + # |-----+-------------------+------------+----------------+ + # | 117 | 121 | 122 | 122 | + # | 118 | 121 | 122 | 122 | + # | 238 | 253 | 254 | 254 | + # | 239 | 253 | 254 | 254 | + # | 246 | 261 | 262 | 262 | + # +-----+-------------------+------------+----------------+ # -----------------+-------------------+------------+----------------+ # related_skyc1 | index_old_skyc2 | id_skyc2 | source_skyc2 | # -----------------+-------------------+------------+----------------+ @@ -377,7 +381,7 @@ def one_to_many_advanced( # | 327 | 5869 | -1 | # -----------------+-------------------+------------+----------------+ # -------------+------+ - # d2d_skyc2 | dr | + # d2d | dr | # -------------+------| # 3.07478 | 0 | # 6.41973 | 0 | @@ -399,7 +403,7 @@ def one_to_many_advanced( # Get the column to check for the minimum depending on the method # set the column names needed for filtering the 'to-many' # associations depending on the method (advanced or deruiter) - dist_col = 'd2d_skyc2' if method == 'advanced' else 'dr' + dist_col = 'd2d' if method == 'advanced' else 'dr' # go through the doubles and # 1. Keep the closest d2d or de ruiter as the primary id @@ -512,7 +516,7 @@ def one_to_many_advanced( ) # Merge them back together - duplicated_skyc1 = original.append(not_original) + duplicated_skyc1 = pd.concat([original, not_original]) del original, not_original @@ -592,11 +596,8 @@ def one_to_many_advanced( # Reset the related column to avoid rogue relations sources_to_copy['related'] = None - # and finally append. - sources_df = sources_df.append( - sources_to_copy, - ignore_index=True - ) + # and finally concatenate. + sources_df = pd.concat([sources_df, sources_to_copy], ignore_index=True) return temp_srcs, sources_df @@ -643,7 +644,7 @@ def many_to_many_advanced(temp_srcs: pd.DataFrame, method: str) -> pd.DataFrame: m_to_m.shape[0] ) - dist_col = 'd2d_skyc2' if method == 'advanced' else 'dr' + dist_col = 'd2d' if method == 'advanced' else 'dr' min_col = 'min_' + dist_col # get the minimum de ruiter value for each extracted source @@ -667,7 +668,7 @@ def many_to_one_advanced(temp_srcs: pd.DataFrame) -> pd.DataFrame: Args: temp_srcs: - The temporary associtation dataframe used through the advanced + The temporary association dataframe used through the advanced association process. Returns: @@ -675,9 +676,9 @@ def many_to_one_advanced(temp_srcs: pd.DataFrame) -> pd.DataFrame: ''' # use only these columns for easy debugging of the dataframe cols = [ - 'index_old_skyc1', 'id_skyc1', 'source_skyc1', 'd2d_skyc1', + 'index_old_skyc1', 'id_skyc1', 'source_skyc1', 'related_skyc1', 'index_old_skyc2', 'id_skyc2', 'source_skyc2', - 'd2d_skyc2', 'dr' + 'd2d', 'dr' ] # select those sources which have been matched to the same measurement @@ -688,15 +689,15 @@ def many_to_one_advanced(temp_srcs: pd.DataFrame) -> pd.DataFrame: ] # duplicated_skyc2 - # +-----+-------------------+------------+----------------+------------- - # | | index_old_skyc1 | id_skyc1 | source_skyc1 | d2d_skyc1 - # |-----+-------------------+------------+----------------+------------- - # | 447 | 477 | 478 | 478 | 0 - # | 448 | 478 | 479 | 479 | 0 - # | 477 | 507 | 508 | 508 | 0 - # | 478 | 508 | 509 | 509 | 0 - # | 695 | 738 | 739 | 739 | 0 - # +-----+-------------------+------------+----------------+------------- + # +-----+-------------------+------------+----------------+ + # | | index_old_skyc1 | id_skyc1 | source_skyc1 | + # |-----+-------------------+------------+----------------+ + # | 447 | 477 | 478 | 478 | + # | 448 | 478 | 479 | 479 | + # | 477 | 507 | 508 | 508 | + # | 478 | 508 | 509 | 509 | + # | 695 | 738 | 739 | 739 | + # +-----+-------------------+------------+----------------+ # +-----------------+-------------------+------------+----------------+ # | related_skyc1 | index_old_skyc2 | id_skyc2 | source_skyc2 | # +-----------------+-------------------+------------+----------------+ @@ -707,7 +708,7 @@ def many_to_one_advanced(temp_srcs: pd.DataFrame) -> pd.DataFrame: # | | 561 | 6103 | -1 | # +-----------------+-------------------+------------+----------------+ # -------------+------+ - # d2d_skyc2 | dr | + # d2d | dr | # -------------+------| # 8.63598 | 0 | # 8.63598 | 0 | @@ -787,9 +788,13 @@ def many_to_one_advanced(temp_srcs: pd.DataFrame) -> pd.DataFrame: def basic_association( - sources_df: pd.DataFrame, skyc1_srcs: pd.DataFrame, skyc1: SkyCoord, - skyc2_srcs: pd.DataFrame, skyc2: SkyCoord, limit: Angle, - id_incr_par_assoc: int=0 + sources_df: pd.DataFrame, + skyc1_srcs: pd.DataFrame, + skyc1: SkyCoord, + skyc2_srcs: pd.DataFrame, + skyc2: SkyCoord, + limit: Angle, + id_incr_par_assoc: int = 0 ) -> Tuple[pd.DataFrame, pd.DataFrame]: ''' The loop for basic source association that uses the astropy @@ -820,10 +825,10 @@ def basic_association( image are being used). Defaults to 0. Returns: - The output sources_df containing all input measurements along with the - association and relation information. - The output skyc1_srcs with updated with new sources from the - association. + The output `sources_df` containing all input measurements along with the + association and relation information. + The output `skyc1_srcs` with updated with new sources from the + association. ''' # match the new sources to the base # idx gives the index of the closest match in the base for skyc2 @@ -856,16 +861,20 @@ def basic_association( ) ) - # and skyc2 is now ready to be appended to new sources - sources_df = sources_df.append( - skyc2_srcs, ignore_index=True + # and skyc2 is now ready to be concatenated with the new sources + sources_df = pd.concat( + [sources_df, skyc2_srcs], + ignore_index=True ).reset_index(drop=True) # and update skyc1 with the sources that were created from the one # to many relations and any new sources. - skyc1_srcs = skyc1_srcs.append( - skyc2_srcs[ - ~skyc2_srcs['source'].isin(skyc1_srcs['source']) + skyc1_srcs = pd.concat( + [ + skyc1_srcs, + skyc2_srcs[ + ~skyc2_srcs['source'].isin(skyc1_srcs['source']) + ] ], ignore_index=True ).reset_index(drop=True) @@ -874,9 +883,15 @@ def basic_association( def advanced_association( - method: str, sources_df: pd.DataFrame, skyc1_srcs: pd.DataFrame, - skyc1: SkyCoord, skyc2_srcs: pd.DataFrame, skyc2: SkyCoord, - dr_limit: float, bw_max: float, id_incr_par_assoc: int=0 + method: str, + sources_df: pd.DataFrame, + skyc1_srcs: pd.DataFrame, + skyc1: SkyCoord, + skyc2_srcs: pd.DataFrame, + skyc2: SkyCoord, + dr_limit: float, + bw_max: float, + id_incr_par_assoc: int = 0 ) -> Tuple[pd.DataFrame, pd.DataFrame]: ''' The loop for advanced source association that uses the astropy @@ -913,10 +928,10 @@ def advanced_association( image are being used). Defaults to 0. Returns: - The output sources_df containing all input measurements along with the - association and relation information. - The output skyc1_srcs with updated with new sources from the - association. + The output `sources_df` containing all input measurements along with the + association and relation information. + The output `skyc1_srcs` with updated with new sources from the + association. ''' # read the needed sources fields # Step 1: get matches within semimajor axis of image. @@ -944,6 +959,13 @@ def advanced_association( suffixes=('_skyc1', '_skyc2') ) + # drop the double d2d column and keep the d2d_skyc2 as assigned above + temp_srcs = ( + temp_srcs + .drop(['d2d_skyc1', 'dr_skyc1', 'dr_skyc2'], axis=1) + .rename(columns={'d2d_skyc2': 'd2d'}) + ) + del temp_skyc1_srcs, temp_skyc2_srcs # Step 3: Apply the beamwidth limit @@ -981,6 +1003,7 @@ def advanced_association( ].reset_index(drop=True) skyc2_srcs_toappend['source'] = temp_srcs['source_skyc1'].values skyc2_srcs_toappend['related'] = temp_srcs['related_skyc1'].values + skyc2_srcs_toappend['d2d'] = temp_srcs['d2d'].values skyc2_srcs_toappend['dr'] = temp_srcs['dr'].values # and get the skyc2 sources with no match @@ -1000,37 +1023,51 @@ def advanced_association( start_elem + new_sources.shape[0], dtype=int ) - skyc2_srcs_toappend = skyc2_srcs_toappend.append( - new_sources, ignore_index=True + skyc2_srcs_toappend = pd.concat( + [skyc2_srcs_toappend, new_sources], + ignore_index=True ) - # and skyc2 is now ready to be appended to source_df - sources_df = sources_df.append( - skyc2_srcs_toappend, ignore_index=True + # and skyc2 is now ready to be concatenated with source_df + sources_df = pd.concat( + [sources_df, skyc2_srcs_toappend], + ignore_index=True ).reset_index(drop=True) # update skyc1 and df for next association iteration # calculate average angles for skyc1 - skyc1_srcs = ( - skyc1_srcs.append(new_sources, ignore_index=True) - .reset_index(drop=True) - ) + skyc1_srcs = pd.concat( + [skyc1_srcs, new_sources], + ignore_index=True + ).reset_index(drop=True) # also need to append any related sources that created a new # source, we can use the skyc2_srcs_toappend to get these - skyc1_srcs = skyc1_srcs.append( - skyc2_srcs_toappend.loc[ - ~skyc2_srcs_toappend.source.isin(skyc1_srcs.source) + skyc1_srcs = pd.concat( + [ + skyc1_srcs, + skyc2_srcs_toappend.loc[ + ~skyc2_srcs_toappend.source.isin(skyc1_srcs.source) + ] ] ) return sources_df, skyc1_srcs -def association(images_df: pd.DataFrame, limit: Angle, dr_limit: float, - bw_limit: float, duplicate_limit: Angle, config: PipelineConfig, add_mode: bool, - previous_parquets: Dict[str, str], done_images_df: pd.DataFrame, - id_incr_par_assoc: int=0, parallel: bool=False) -> pd.DataFrame: +def association( + images_df: pd.DataFrame, + limit: Angle, + dr_limit: float, + bw_limit: float, + duplicate_limit: Angle, + config: PipelineConfig, + add_mode: bool, + previous_parquets: Dict[str, str], + done_images_df: pd.DataFrame, + id_incr_par_assoc: int = 0, + parallel: bool = False +) -> pd.DataFrame: ''' The main association function that does the common tasks between basic and advanced modes. @@ -1066,7 +1103,7 @@ def association(images_df: pd.DataFrame, limit: Angle, dr_limit: float, Returns: The output sources_df containing all input measurements along with the - association and relation information. + association and relation information. Raises: Exception: Raised if association method is not valid. @@ -1074,6 +1111,11 @@ def association(images_df: pd.DataFrame, limit: Angle, dr_limit: float, timer = StopWatch() if parallel: + # Skip empty groups that seems to sometimes happen with the + # dask groupby + if len(images_df) == 0: + return images_df + images_df = ( images_df.sort_values(by='image_datetime') .drop('image_datetime', axis=1) @@ -1244,7 +1286,7 @@ def association(images_df: pd.DataFrame, limit: Angle, dr_limit: float, # account for RA wrapping ra_wrap_mask = sources_df.ra <= 0.1 sources_df['ra_wrap'] = sources_df.ra.values - sources_df.at[ + sources_df.loc[ ra_wrap_mask, 'ra_wrap' ] = sources_df[ra_wrap_mask].ra.values + 360. @@ -1295,7 +1337,7 @@ def association(images_df: pd.DataFrame, limit: Angle, dr_limit: float, # correct the RA wrapping ra_wrap_mask = weighted_df.ra >= 360. - weighted_df.at[ + weighted_df.loc[ ra_wrap_mask, 'ra' ] = weighted_df[ra_wrap_mask].ra.values - 360. @@ -1425,8 +1467,8 @@ def _correct_parallel_source_ids_add_mode( The start elem number for the new source ids. Returns: - The input df with corrected source ids and relations, and the new - start elem for the next group. + The input dataframe with corrected source ids and relations. + The new start elem for the next group. """ # When using add_mode the correction becomes easier with the increment # as there's a clear difference between old and new. @@ -1502,7 +1544,7 @@ def parallel_association( n_skyregion_groups: The number of sky region groups. Returns: - pd.DataFrame: The combined association results of the parallel + The combined association results of the parallel association with corrected source ids. """ logger.info( @@ -1546,10 +1588,12 @@ def parallel_association( id_incr_par_assoc = max(done_source_ids) if add_mode else 0 n_cpu = cpu_count() - 1 - + logger.debug(f"Running association with {n_cpu} CPUs") + n_partitions = calculate_n_partitions(images_df, n_cpu) + # pass each skyreg_group through the normal association process. results = ( - dd.from_pandas(images_df, n_cpu) + dd.from_pandas(images_df.set_index('skyreg_group'), npartitions=n_partitions) .groupby('skyreg_group') .apply( association, diff --git a/vast_pipeline/pipeline/config.py b/vast_pipeline/pipeline/config.py index 696cf6198..733267d50 100644 --- a/vast_pipeline/pipeline/config.py +++ b/vast_pipeline/pipeline/config.py @@ -110,6 +110,7 @@ class PipelineConfig: ), "variability": yaml.Map( { + "pair_metrics": yaml.Bool(), "source_aggregate_pair_metrics_min_abs_vs": yaml.Float(), } ), @@ -120,11 +121,14 @@ class PipelineConfig: settings.BASE_DIR, "vast_pipeline", "config_template.yaml.j2" ) - def __init__(self, config_yaml: yaml.YAML): + def __init__(self, config_yaml: yaml.YAML, validate_inputs: bool = True): """Initialises PipelineConfig with parsed (but not necessarily validated) YAML. Args: config_yaml (yaml.YAML): Input YAML, usually the output of `strictyaml.load`. + validate_inputs (bool, optional): Validate the config input files. Ensures + that the inputs match (e.g. each image has a catalogue), and that each + path exists. Set to False to skip these checks. Defaults to True. Raises: PipelineConfigError: The input YAML config violates the schema. @@ -143,12 +147,17 @@ def __init__(self, config_yaml: yaml.YAML): # the epochs. # ensure the inputs are valid in case .from_file(..., validate=False) was used + if not validate_inputs: + return + try: self._validate_inputs() except yaml.YAMLValidationError as e: raise PipelineConfigError(e) # detect simple list inputs and convert them to epoch-mode inputs + yaml_inputs = self._yaml["inputs"] + inputs = yaml_inputs.data for input_file_type in self._REQUIRED_INPUT_TYPES: # skip missing optional input types, e.g. background if ( @@ -157,7 +166,7 @@ def __init__(self, config_yaml: yaml.YAML): ): continue - input_files = self["inputs"][input_file_type] + input_files = inputs[input_file_type] # resolve glob expressions if present if isinstance(input_files, dict): @@ -166,11 +175,9 @@ def __init__(self, config_yaml: yaml.YAML): # resolve the glob expressions self.epoch_based = False file_list = self._resolve_glob_expressions( - self._yaml["inputs"][input_file_type] - ) - self._yaml["inputs"][input_file_type] = self._create_input_epochs( - file_list + yaml_inputs[input_file_type] ) + inputs[input_file_type] = self._create_input_epochs(file_list) else: # epoch-mode with either a list of files or glob expressions self.epoch_based = True @@ -178,16 +185,17 @@ def __init__(self, config_yaml: yaml.YAML): if "glob" in input_files[epoch]: # resolve the glob expressions file_list = self._resolve_glob_expressions( - self._yaml["inputs"][input_file_type][epoch] + yaml_inputs[input_file_type][epoch] ) - self._yaml["inputs"][input_file_type][epoch] = file_list + inputs[input_file_type][epoch] = file_list else: # Epoch-based association not requested and no globs present. Replace # input lists with dicts where each input file has it's own epoch. self.epoch_based = False - self._yaml["inputs"][input_file_type] = self._create_input_epochs( + inputs[input_file_type] = self._create_input_epochs( input_files ) + self._yaml["inputs"] = inputs def __getitem__(self, name: str): """Retrieves the requested YAML chunk as a native Python object.""" @@ -212,10 +220,10 @@ def _create_input_epochs(input_files: List[str]) -> Dict[str, List[str]]: epochs. Args: - input_files (List[str]): the list of input file paths. + input_files: the list of input file paths. Returns: - Dict[str, List[str]]: the input file paths mapped to by unique epoch keys. + The input file paths mapped to by unique epoch keys. """ pad_width = len(str(len(input_files))) input_files_dict = { @@ -229,6 +237,7 @@ def from_file( yaml_path: str, label: str = "run config", validate: bool = True, + validate_inputs: bool = True, add_defaults: bool = True, ) -> "PipelineConfig": """Create a PipelineConfig object from a run configuration YAML file. @@ -242,6 +251,9 @@ def from_file( will not be performed until PipelineConfig.validate() is explicitly called. The inputs are always validated regardless. Defaults to True. + validate_inputs: Validate the config input files. Ensures that the inputs + match (e.g. each image has a catalogue), and that each path exists. Set + to False to skip these checks. Defaults to True. add_defaults: Add missing configuration parameters using configured defaults. The defaults are read from the Django settings file. Defaults to True. @@ -250,7 +262,7 @@ def from_file( PipelineConfigError: The run config YAML file fails schema validation. """ - schema = PipelineConfig.SCHEMA if validate else yaml.Any() + schema = cls.SCHEMA if validate else yaml.Any() with open(yaml_path) as fh: config_str = fh.read() try: @@ -269,7 +281,7 @@ def from_file( # merge configs config_dict = dict_merge(config_defaults_dict, config_yaml.data) config_yaml = yaml.as_document(config_dict, schema=schema, label=label) - return cls(config_yaml) + return cls(config_yaml, validate_inputs=validate_inputs) @staticmethod def _resolve_glob_expressions(input_files: yaml.YAML) -> List[str]: @@ -291,7 +303,7 @@ def _resolve_glob_expressions(input_files: yaml.YAML) -> List[str]: --- Returns: - List[str]: The resolved file paths in lexicographical order. + The resolved file paths in lexicographical order. """ file_list: List[str] = [] if input_files["glob"].is_sequence(): @@ -348,7 +360,7 @@ def validate(self, user: User = None): 2. The number of input files does not exceed the configured pipeline maximum. This is only enforced if a regular user (not staff/admin) created the run. 3. There are at least two input images. - 4. Background input images are required is source monitoring is turned on. + 4. Background input images are required if source monitoring is turned on. 5. All input files exist. Args: @@ -364,26 +376,25 @@ def validate(self, user: User = None): except yaml.YAMLValidationError as e: raise PipelineConfigError(e) + inputs = self["inputs"] + # epochs defined for images only, used as the reference list of epochs - epochs_image = self["inputs"]["image"].keys() + epochs_image = inputs["image"].keys() # map input type to a set of epochs epochs_by_input_type = { - input_type: set(self["inputs"][input_type].keys()) - for input_type in self["inputs"].keys() + input_type: set(inputs[input_type].keys()) + for input_type in inputs.keys() } # map input type to total number of files from all epochs - n_files_by_input_type = {} + n_files_by_input_type: Dict[str, int] = {} + epoch_n_files: Dict[str, Dict[str, int]] = {} + n_files = 0 for input_type, epochs_set in epochs_by_input_type.items(): + epoch_n_files[input_type] = {} n_files_by_input_type[input_type] = 0 for epoch in epochs_set: - n_files_by_input_type[input_type] += len(self["inputs"][input_type][epoch]) - n_files = 0 # total number of input files - # map input type to a mapping of epoch to file count - epoch_n_files: Dict[str, Dict[str, int]] = {} - for input_type in self["inputs"].keys(): - epoch_n_files[input_type] = {} - for epoch in self["inputs"][input_type].keys(): - n = len(self["inputs"][input_type][epoch]) + n = len(inputs[input_type][epoch]) + n_files_by_input_type[input_type] += n epoch_n_files[input_type][epoch] = n n_files += n @@ -391,7 +402,7 @@ def validate(self, user: User = None): # of the user's input format. # Ensure all input file types have the same epochs. try: - for input_type in self["inputs"].keys(): + for input_type in inputs.keys(): self._yaml["inputs"][input_type].revalidate( yaml.Map({epoch: yaml.Seq(yaml.Str()) for epoch in epochs_image}) ) @@ -427,7 +438,7 @@ def validate(self, user: User = None): # This could be combined with the number of epochs validation above, but we want # to give specific feedback to the user on failure. try: - for input_type in self["inputs"].keys(): + for input_type in inputs.keys(): self._yaml["inputs"][input_type].revalidate( yaml.Map( { @@ -444,11 +455,11 @@ def validate(self, user: User = None): except yaml.YAMLValidationError: # map input type to a mapping of epoch to file count file_counts_str = "" - for input_type in self["inputs"].keys(): + for input_type in inputs.keys(): file_counts_str += f"{input_type}:\n" - for epoch in sorted(self["inputs"][input_type].keys()): + for epoch in sorted(inputs[input_type].keys()): file_counts_str += ( - f" {epoch}: {len(self['inputs'][input_type][epoch])}\n" + f" {epoch}: {len(inputs[input_type][epoch])}\n" ) file_counts_str = file_counts_str[:-1] raise PipelineConfigError( @@ -473,14 +484,19 @@ def validate(self, user: User = None): ) # ensure at least two inputs are provided - check = [n_files_by_input_type[input_type] < 2 for input_type in self["inputs"].keys()] + check = [n_files_by_input_type[input_type] < 2 for input_type in inputs.keys()] if any(check): raise PipelineConfigError( "Number of image files must to be larger than 1" ) # ensure background files are provided if source monitoring is requested - if self["source_monitoring"]["monitor"]: + try: + monitor = self["source_monitoring"]["monitor"] + except KeyError: + monitor = False + + if monitor: inputs_schema = yaml.Map( { k: yaml.UniqueSeq(yaml.Str()) @@ -496,8 +512,8 @@ def validate(self, user: User = None): ) # ensure the input files all exist - for input_type in self["inputs"].keys(): - for epoch, file_list in self["inputs"][input_type].items(): + for input_type in inputs.keys(): + for epoch, file_list in inputs[input_type].items(): for file in file_list: if not os.path.exists(file): raise PipelineConfigError(f"{file} does not exist.") @@ -509,8 +525,8 @@ def check_prev_config_diff(self) -> bool: settings are the same (the requirement for add mode). Otherwise False is returned. Returns: - True if images are different but general settings are the same, otherwise - False is returned. + `True` if images are different but general settings are the same, + otherwise `False` is returned. """ prev_config = PipelineConfig.from_file( os.path.join(self["run"]["path"], "config_prev.yaml"), @@ -532,3 +548,54 @@ def check_prev_config_diff(self) -> bool: if images_changed and settings_check: return False return True + + def image_opts(self) -> Dict[str, Any]: + """ + Get the config options required for image ingestion only. + Namely: + - selavy_local_rms_fill_value + - condon_errors + - ra_uncertainty + - dec_uncertainty + + Returns: + The relevant key value pairs + """ + keys = [ + "selavy_local_rms_fill_value", + "condon_errors", + "ra_uncertainty", + "dec_uncertainty" + ] + return {key: self["measurements"][key] for key in keys} + + +class ImageIngestConfig(PipelineConfig): + """Image ingest configuration derived from PipelineConfig. + + Attributes: + SCHEMA: class attribute containing the YAML schema for the image ingest config. + TEMPLATE_PATH: class attribute containing the path to the default Jinja2 image ingest + config template file. + + Raises: + PipelineConfigError: the input YAML config violates the schema. + """ + # path to default image ingest config template + TEMPLATE_PATH: str = os.path.join( + settings.BASE_DIR, "vast_pipeline", "ingest_config_template.yaml.j2" + ) + _VALID_ASSOC_METHODS = None + SCHEMA = yaml.Map( + { + "inputs": yaml.Map(PipelineConfig._SCHEMA_INPUTS), + "measurements": yaml.Map( + { + "condon_errors": yaml.Bool(), + "selavy_local_rms_fill_value": yaml.Float(), + "ra_uncertainty": yaml.Float(), + "dec_uncertainty": yaml.Float(), + } + ), + } + ) diff --git a/vast_pipeline/pipeline/errors.py b/vast_pipeline/pipeline/errors.py index 141eb62cd..2b527740d 100644 --- a/vast_pipeline/pipeline/errors.py +++ b/vast_pipeline/pipeline/errors.py @@ -2,6 +2,8 @@ Defines errors for the pipeline to return. """ +from typing import Optional + class PipelineError(Exception): """ Generic pipeline error @@ -10,7 +12,7 @@ class PipelineError(Exception): msg (str): The full error string to return. """ - def __init__(self, msg: str=None) -> None: + def __init__(self, msg: Optional[str] = None) -> None: """ Initialises the error. @@ -51,7 +53,7 @@ class PipelineConfigError(PipelineError): """ Error for issue in the pipeline configuration """ - def __init__(self, msg=None): + def __init__(self, msg: Optional[str] = None): """ Initialises the config error. @@ -65,7 +67,7 @@ class PipelineInitError(PipelineError): """ Error for issue in the pipeline initialisation """ - def __init__(self, msg=None): + def __init__(self, msg: Optional[str] = None): """ Initialises the init error. diff --git a/vast_pipeline/pipeline/finalise.py b/vast_pipeline/pipeline/finalise.py index b412b76d8..54f928fcb 100644 --- a/vast_pipeline/pipeline/finalise.py +++ b/vast_pipeline/pipeline/finalise.py @@ -5,19 +5,19 @@ from astropy import units as u from astropy.coordinates import SkyCoord -from typing import List, Dict +from typing import List, Dict, Tuple from vast_pipeline.models import Run from vast_pipeline.utils.utils import StopWatch, optimize_floats, optimize_ints from vast_pipeline.pipeline.loading import ( make_upload_associations, make_upload_sources, make_upload_related_sources, - make_upload_measurement_pairs, update_sources + update_sources ) +from vast_pipeline.pipeline.pairs import calculate_measurement_pair_metrics from vast_pipeline.pipeline.utils import ( - parallel_groupby, calculate_measurement_pair_metrics + parallel_groupby, get_df_memory_usage, log_total_memory_usage ) - logger = logging.getLogger(__name__) @@ -49,21 +49,34 @@ def calculate_measurement_pair_aggregate_metrics( Returns: Measurement pair aggregate metrics indexed by the source ID, `source`. - The metric columns are named: `vs_abs_significant_max_{flux_type}` and - `m_abs_significant_max_{flux_type}`. + The metric columns are named: `vs_abs_significant_max_{flux_type}` + and `m_abs_significant_max_{flux_type}`. """ - pair_agg_metrics = measurement_pairs_df.set_index("source").iloc[ - measurement_pairs_df.query(f"abs(vs_{flux_type}) >= @min_vs") - .groupby("source") - .agg(m_abs_max_idx=(f"m_{flux_type}", lambda x: x.abs().idxmax()),) - .astype(np.int32)["m_abs_max_idx"] # cast row indices to int and select them - .reset_index(drop=True) # keep only the row indices - ][[f"vs_{flux_type}", f"m_{flux_type}"]] + check_df = measurement_pairs_df.query(f"abs(vs_{flux_type}) >= @min_vs") + + # This check is performed due to a bug that was occuring after updating the + # pandas dependancy (1.4) when performing the tests. The bug was that the + # grouby and agg stage below was being performed on an empty series in the + # basic association test and causing a failure. Hence this only performs + # the groupby if the original query dataframe is not empty. + if check_df.empty: + pair_agg_metrics = pd.DataFrame( + columns=[f"vs_{flux_type}", f"m_{flux_type}", "source"] + ) + else: + pair_agg_metrics = measurement_pairs_df.iloc[ + check_df + .groupby("source") + .agg(m_abs_max_idx=(f"m_{flux_type}", lambda x: x.abs().idxmax()),) + # cast row indices to int and select them + .astype(np.int32)["m_abs_max_idx"] + .reset_index(drop=True) # keep only the row indices + ][[f"vs_{flux_type}", f"m_{flux_type}", "source"]] pair_agg_metrics = pair_agg_metrics.abs().rename(columns={ f"vs_{flux_type}": f"vs_abs_significant_max_{flux_type}", f"m_{flux_type}": f"m_abs_significant_max_{flux_type}", - }) + }).set_index('source') return pair_agg_metrics @@ -71,11 +84,12 @@ def final_operations( sources_df: pd.DataFrame, p_run: Run, new_sources_df: pd.DataFrame, + calculate_pairs: bool, source_aggregate_pair_metrics_min_abs_vs: float, add_mode: bool, done_source_ids: List[int], previous_parquets: Dict[str, str] -) -> int: +) -> Tuple[int, int]: """ Performs the final operations of the pipeline: - Calculates the statistics for the final sources. @@ -94,6 +108,9 @@ def final_operations( new_sources_df: The new sources dataframe, only contains the 'new_source_high_sigma' column (source_id is the index). + calculate_pairs: + Whether to calculate the measurement pairs and their 2-epoch + metrics, Vs and m. source_aggregate_pair_metrics_min_abs_vs: Only measurement pairs where the Vs metric exceeds this value are selected for the aggregate pair metrics that are stored in @@ -105,8 +122,10 @@ def final_operations( in the previous run in add mode. Returns: - The number of sources contained in the pipeline (used in the next steps - of main.py). + The number of sources contained in the pipeline run (used in the next + steps of main.py). + The number of new sources contained in the pipeline run (used in the + next steps of main.py). """ timer = StopWatch() @@ -115,8 +134,14 @@ def final_operations( 'Calculating statistics for %i sources...', sources_df.source.unique().shape[0] ) + log_total_memory_usage() + srcs_df = parallel_groupby(sources_df) + + mem_usage = get_df_memory_usage(srcs_df) logger.info('Groupby-apply time: %.2f seconds', timer.reset()) + logger.debug(f"Initial srcs_df memory: {mem_usage}MB") + log_total_memory_usage() # add new sources srcs_df["new"] = srcs_df.index.isin(new_sources_df.index) @@ -129,6 +154,10 @@ def final_operations( ) srcs_df["new_high_sigma"] = srcs_df["new_high_sigma"].fillna(0.0) + mem_usage = get_df_memory_usage(srcs_df) + logger.debug(f"srcs_df memory after adding new sources: {mem_usage}MB") + log_total_memory_usage() + # calculate nearest neighbour srcs_skycoord = SkyCoord( srcs_df['wavg_ra'].values, @@ -143,34 +172,59 @@ def final_operations( # add the separation distance in degrees srcs_df['n_neighbour_dist'] = d2d.deg + mem_usage = get_df_memory_usage(srcs_df) + logger.debug(f"srcs_df memory after nearest-neighbour: {mem_usage}MB") + log_total_memory_usage() + # create measurement pairs, aka 2-epoch metrics - timer.reset() - measurement_pairs_df = calculate_measurement_pair_metrics(sources_df) - logger.info('Measurement pair metrics time: %.2f seconds', timer.reset()) - - # calculate measurement pair metric aggregates for sources by finding the row indices - # of the aggregate max of the abs(m) metric for each flux type. - pair_agg_metrics = pd.merge( - calculate_measurement_pair_aggregate_metrics( - measurement_pairs_df, source_aggregate_pair_metrics_min_abs_vs, flux_type="peak", - ), - calculate_measurement_pair_aggregate_metrics( - measurement_pairs_df, source_aggregate_pair_metrics_min_abs_vs, flux_type="int", - ), - how="outer", - left_index=True, - right_index=True, - ) + if calculate_pairs: + timer.reset() + measurement_pairs_df = calculate_measurement_pair_metrics(sources_df) + logger.info( + 'Measurement pair metrics time: %.2f seconds', + timer.reset()) + mem_usage = get_df_memory_usage(measurement_pairs_df) + logger.debug(f"measurment_pairs_df memory: {mem_usage}MB") + log_total_memory_usage() + + # calculate measurement pair metric aggregates for sources by finding + # the row indices of the aggregate max of the abs(m) metric for each + # flux type. + pair_agg_metrics = pd.merge( + calculate_measurement_pair_aggregate_metrics( + measurement_pairs_df, + source_aggregate_pair_metrics_min_abs_vs, + flux_type="peak", + ), + calculate_measurement_pair_aggregate_metrics( + measurement_pairs_df, + source_aggregate_pair_metrics_min_abs_vs, + flux_type="int", + ), + how="outer", + left_index=True, + right_index=True, + ) - # join with sources and replace agg metrics NaNs with 0 as the DataTables API JSON - # serialization doesn't like them - srcs_df = srcs_df.join(pair_agg_metrics).fillna(value={ - "vs_abs_significant_max_peak": 0.0, - "m_abs_significant_max_peak": 0.0, - "vs_abs_significant_max_int": 0.0, - "m_abs_significant_max_int": 0.0, - }) - logger.info("Measurement pair aggregate metrics time: %.2f seconds", timer.reset()) + # join with sources and replace agg metrics NaNs with 0 as the + # DataTables API JSON serialization doesn't like them + srcs_df = srcs_df.join(pair_agg_metrics).fillna(value={ + "vs_abs_significant_max_peak": 0.0, + "m_abs_significant_max_peak": 0.0, + "vs_abs_significant_max_int": 0.0, + "m_abs_significant_max_int": 0.0, + }) + logger.info( + "Measurement pair aggregate metrics time: %.2f seconds", + timer.reset()) + mem_usage = get_df_memory_usage(srcs_df) + logger.debug(f"srcs_df memory after calculate_pairs: {mem_usage}MB") + log_total_memory_usage() + else: + logger.info( + "Skipping measurement pair metric calculation as specified in " + "the run configuration." + ) # upload sources to DB, column 'id' with DB id is contained in return if add_mode: @@ -179,18 +233,39 @@ def final_operations( # upload new ones first (new id's are fetched) src_done_mask = srcs_df.index.isin(done_source_ids) srcs_df_upload = srcs_df.loc[~src_done_mask].copy() + + mem_usage = get_df_memory_usage(srcs_df_upload) + logger.debug(f"srcs_df_upload initial memory: {mem_usage}MB") + log_total_memory_usage() + srcs_df_upload = make_upload_sources(srcs_df_upload, p_run, add_mode) + + mem_usage = get_df_memory_usage(srcs_df_upload) + logger.debug(f"srcs_df_upload memory after upload: {mem_usage}MB") + log_total_memory_usage() + # And now update srcs_df_update = srcs_df.loc[src_done_mask].copy() logger.info( f"Updating {srcs_df_update.shape[0]} sources with new metrics.") + mem_usage = get_df_memory_usage(srcs_df_update) + logger.debug(f"srcs_df_update memory: {mem_usage}MB") + log_total_memory_usage() + srcs_df = update_sources(srcs_df_update, batch_size=1000) + mem_usage = get_df_memory_usage(srcs_df_update) + logger.debug(f"srcs_df_update memory: {mem_usage}MB") + log_total_memory_usage() # Add back together if not srcs_df_upload.empty: - srcs_df = srcs_df.append(srcs_df_upload) + srcs_df = pd.concat([srcs_df, srcs_df_upload]) else: srcs_df = make_upload_sources(srcs_df, p_run, add_mode) + mem_usage = get_df_memory_usage(srcs_df) + logger.debug(f"srcs_df memory after uploading sources: {mem_usage}MB") + log_total_memory_usage() + # gather the related df, upload to db and save to parquet file # the df will look like # @@ -206,13 +281,17 @@ def final_operations( related_df = ( srcs_df.loc[srcs_df["related_list"] != -1, ["id", "related_list"]] .explode("related_list") - .rename(columns={"id": "from_source_id", "related_list": "to_source_id"}) + .rename(columns={"id": "from_source_id", + "related_list": "to_source_id" + }) ) # for the column 'from_source_id', replace relation source ids with db id - related_df["to_source_id"] = related_df["to_source_id"].map(srcs_df["id"].to_dict()) + related_df["to_source_id"] = related_df["to_source_id"].map( + srcs_df["id"].to_dict()) # drop relationships with the same source - related_df = related_df[related_df["from_source_id"] != related_df["to_source_id"]] + related_df = related_df[related_df["from_source_id"] + != related_df["to_source_id"]] # write symmetrical relations to parquet related_df.to_parquet( @@ -229,7 +308,7 @@ def final_operations( ) related_df = ( - related_df.append(old_relations, ignore_index=True) + pd.concat([related_df, old_relations], ignore_index=True) .drop_duplicates(keep=False) ) logger.debug(f'Add mode: #{related_df.shape[0]} relations to upload.') @@ -241,24 +320,31 @@ def final_operations( # write sources to parquet file srcs_df = srcs_df.drop(["related_list", "img_list"], axis=1) ( - srcs_df.set_index('id') # set the index to db ids, dropping the source idx + # set the index to db ids, dropping the source idx + srcs_df.set_index('id') .to_parquet(os.path.join(p_run.path, 'sources.parquet')) ) - # update measurments with sources to get associations + # update measurements with sources to get associations sources_df = ( sources_df.drop('related', axis=1) .merge(srcs_df.rename(columns={'id': 'source_id'}), on='source') ) + mem_usage = get_df_memory_usage(sources_df) + logger.debug(f"sources_df memory after srcs_df merge: {mem_usage}MB") + log_total_memory_usage() + if add_mode: # Load old associations so the already uploaded ones can be removed old_assoications = ( pd.read_parquet(previous_parquets['associations']) .rename(columns={'meas_id': 'id'}) ) - sources_df_upload = sources_df.append( - old_assoications, ignore_index=True) + sources_df_upload = pd.concat( + [sources_df, old_assoications], + ignore_index=True + ) sources_df_upload = sources_df_upload.drop_duplicates( ['source_id', 'id', 'd2d', 'dr'], keep=False ) @@ -275,53 +361,30 @@ def final_operations( ['source_id', 'meas_id', 'd2d', 'dr'] ].to_parquet(os.path.join(p_run.path, 'associations.parquet')) - # get the Source object primary keys for the measurement pairs - measurement_pairs_df = measurement_pairs_df.join( - srcs_df.id.rename("source_id"), on="source" - ) - - if add_mode: - # Load old associations so the already uploaded ones can be removed - old_measurement_pairs = ( - pd.read_parquet(previous_parquets['measurement_pairs']) - ).rename(columns={'meas_id_a': 'id_a', 'meas_id_b': 'id_b'}) - - measurement_pairs_df_upload = measurement_pairs_df.append( - old_measurement_pairs, ignore_index=True) - - measurement_pairs_df_upload = ( - measurement_pairs_df_upload.drop_duplicates( - ['id_a', 'id_b', 'source_id'], keep=False) - ) - - logger.debug( - f'Add mode: #{measurement_pairs_df_upload.shape[0]}' - ' measurement pairs to upload.' + if calculate_pairs: + # get the Source object primary keys for the measurement pairs + measurement_pairs_df = measurement_pairs_df.join( + srcs_df.id.rename("source_id"), on="source" ) - else: - measurement_pairs_df_upload = measurement_pairs_df - # create the measurement pair objects and upload to DB - measurement_pairs_df = make_upload_measurement_pairs( - measurement_pairs_df_upload) - - if add_mode: - measurement_pairs_df = old_measurement_pairs.append( - measurement_pairs_df) - - # optimize measurement pair DataFrame and save to parquet file - measurement_pairs_df = optimize_ints( - optimize_floats( - measurement_pairs_df.drop(columns=["source"]).rename( - columns={"id_a": "meas_id_a", "id_b": "meas_id_b"} + # optimize measurement pair DataFrame and save to parquet file + measurement_pairs_df = optimize_ints( + optimize_floats( + measurement_pairs_df.drop(columns=["source"]).rename( + columns={"id_a": "meas_id_a", "id_b": "meas_id_b"} + ) ) ) - ) - measurement_pairs_df.to_parquet( - os.path.join(p_run.path, "measurement_pairs.parquet"), index=False - ) + measurement_pairs_df.to_parquet( + os.path.join(p_run.path, "measurement_pairs.parquet"), index=False + ) + + logger.info( + "Total final operations time: %.2f seconds", + timer.reset_init()) - logger.info("Total final operations time: %.2f seconds", timer.reset_init()) + nr_sources = srcs_df["id"].count() + nr_new_sources = srcs_df['new'].sum() # calculate and return total number of extracted sources - return srcs_df["id"].count() + return (nr_sources, nr_new_sources) diff --git a/vast_pipeline/pipeline/forced_extraction.py b/vast_pipeline/pipeline/forced_extraction.py index e4d090cc3..69b8af9ce 100644 --- a/vast_pipeline/pipeline/forced_extraction.py +++ b/vast_pipeline/pipeline/forced_extraction.py @@ -13,17 +13,14 @@ from django.conf import settings from django.db import transaction from pyarrow.parquet import read_schema -from typing import List, Tuple, Dict +from typing import Any, List, Tuple, Dict, Optional from vast_pipeline.models import Image, Measurement, Run -from vast_pipeline.image.utils import on_sky_sep from vast_pipeline.pipeline.loading import make_upload_measurements from forced_phot import ForcedPhot -from .utils import ( - cross_join, parallel_groupby_coord -) from ..utils.utils import StopWatch +from vast_pipeline.image.utils import open_fits logger = logging.getLogger(__name__) @@ -65,13 +62,15 @@ def remove_forced_meas(run_path: str) -> None: def get_data_from_parquet( - file: str, p_run_path: str, add_mode: bool = False,) -> Dict: + file_and_image_id: Tuple[str, int], p_run_path: str, add_mode: bool = False +) -> Dict: ''' Get the prefix, max id and image id from the measurements parquets Args: - file: - a string with the path of the measurements parquet file + file_and_image_id: + a tuple containing the path of the measurements parquet file and + the image ID. p_run_path: Pipeline run path to get forced parquet in case of add mode. add_mode: @@ -80,8 +79,9 @@ def get_data_from_parquet( Returns: Dictionary with prefix string, an interger max_id and a string with the - id of the image + id of the image. ''' + file, image_id = file_and_image_id if add_mode: image_name = file.split("/")[-2] forced_parquet = os.path.join( @@ -92,20 +92,54 @@ def get_data_from_parquet( file = forced_parquet # get max component id from parquet file df = pd.read_parquet(file, columns=['island_id', 'image_id']) - prefix = df['island_id'].iloc[0].rsplit('_', maxsplit=1)[0] + '_' - max_id = ( - df['island_id'].str.rsplit('_', n=1) - .str.get(-1) - .astype(int) - .values.max() + 1 - ) - return {'prefix': prefix, 'max_id': max_id, 'id': df['image_id'].iloc[0]} + if len(df) > 0: + prefix = df['island_id'].iloc[0].rsplit('_', maxsplit=1)[0] + '_' + max_id = ( + df['island_id'].str.rsplit('_', n=1) + .str.get(-1) + .astype(int) + .values.max() + 1 + ) + else: + prefix = "island_" + max_id = 1 + return {'prefix': prefix, 'max_id': max_id, 'id': image_id} + + +def _forcedphot_preload(image: str, + background: str, + noise: str, + memmap: Optional[bool] = False + ): + """ + Load the relevant image, background and noisemap files. + + Args: + image: a string with the path of the image file + background: a string with the path of the background map + noise: a string with the path of the noise map + + Returns: + A tuple containing the HDU lists + """ + + image_hdul = open_fits(image, memmap=memmap) + background_hdul = open_fits(background, memmap=memmap) + noise_hdul = open_fits(noise, memmap=memmap) + + return image_hdul, background_hdul, noise_hdul def extract_from_image( - df: pd.DataFrame, image: str, background: str, noise: str, - edge_buffer: float, cluster_threshold: float, allow_nan: bool - ) -> Dict: + df: pd.DataFrame, + image: str, + background: str, + noise: str, + edge_buffer: float, + cluster_threshold: float, + allow_nan: bool, + **kwargs, +) -> Dict: """ Extract the flux, its erros and chi squared data from the image files (image FIT, background and noise files) and return a dictionary @@ -130,8 +164,10 @@ def extract_from_image( Returns: Dictionary with input dataframe with added columns (flux_int, - flux_int_err, chi_squared_fit) and image name. + flux_int_err, chi_squared_fit) and image name. """ + timer = StopWatch() + # create the skycoord obj to pass to the forced extraction # see usage https://github.com/dlakaplan/forced_phot P_islands = SkyCoord( @@ -139,8 +175,16 @@ def extract_from_image( df['wavg_dec'].values, unit=(u.deg, u.deg) ) + # load the image, background and noisemaps into memory + # a dedicated function may seem unneccesary, but will be useful if we + # split the load to a separate thread. + forcedphot_input = _forcedphot_preload(image, + background, + noise, + memmap=False + ) + FP = ForcedPhot(*forcedphot_input) - FP = ForcedPhot(image, background, noise) flux, flux_err, chisq, DOF, cluster_id = FP.measure( P_islands, cluster_threshold=cluster_threshold, @@ -151,6 +195,8 @@ def extract_from_image( df['flux_int_err'] = flux_err * 1.e3 df['chi_squared_fit'] = chisq + logger.debug(f"Time to measure FP for {image}: {timer.reset()}s") + return {'df': df, 'image': df['image_name'].iloc[0]} @@ -158,7 +204,7 @@ def finalise_forced_dfs( df: pd.DataFrame, prefix: str, max_id: int, beam_bmaj: float, beam_bmin: float, beam_bpa: float, id: int, datetime: datetime.datetime, image: str - ) -> pd.DataFrame: +) -> pd.DataFrame: """ Compute populate leftover columns for the dataframe with forced photometry data given the input parameters @@ -186,7 +232,7 @@ def finalise_forced_dfs( Returns: Input dataframe with added columns island_id, component_id, - name, bmaj, bmin, pa, image_id, time. + name, bmaj, bmin, pa, image_id, time. """ # make up the measurements name from the image island_id and component_id df['island_id'] = np.char.add( @@ -215,7 +261,7 @@ def parallel_extraction( df: pd.DataFrame, df_images: pd.DataFrame, df_sources: pd.DataFrame, min_sigma: float, edge_buffer: float, cluster_threshold: float, allow_nan: bool, add_mode: bool, p_run_path: str - ) -> pd.DataFrame: +) -> pd.DataFrame: """ Parallelize forced extraction with Dask @@ -247,12 +293,12 @@ def parallel_extraction( Returns: Dataframe with forced extracted measurements data, columns are - 'source_tmp_id', 'ra', 'dec', 'image', 'flux_peak', 'island_id', - 'component_id', 'name', 'flux_int', 'flux_int_err' + 'source_tmp_id', 'ra', 'dec', 'image', 'flux_peak', 'island_id', + 'component_id', 'name', 'flux_int', 'flux_int_err' """ # explode the lists in 'img_diff' column (this will make a copy of the df) out = ( - df.rename(columns={'img_diff':'image', 'source':'source_tmp_id'}) + df.rename(columns={'img_diff': 'image', 'source': 'source_tmp_id'}) # merge the rms_min column from df_images .merge( df_images[['rms_min']], @@ -277,8 +323,8 @@ def parallel_extraction( out['max_snr'] = out['flux_peak'].values / out['image_rms_min'].values out = out[out['max_snr'] > min_sigma].reset_index(drop=True) logger.debug("Min forced sigma dropped %i sources", - predrop_shape - out.shape[0] - ) + predrop_shape - out.shape[0] + ) # drop some columns that are no longer needed and the df should look like # out @@ -297,19 +343,27 @@ def parallel_extraction( # get the unique images to extract from unique_images_to_extract = out['image_name'].unique().tolist() + # create a list of dictionaries with image file paths and dataframes # with data related to each images - image_data_func = lambda x: { - 'image': df_images.at[x, 'path'], - 'background': df_images.at[x, 'background_path'], - 'noise': df_images.at[x, 'noise_path'], - 'df': out[out['image_name'] == x] - } + def image_data_func(image_name: str) -> Dict[str, Any]: + # `out` refers to the `out` declared in nearest enclosing scope + nonlocal out + return { + 'image_id': df_images.at[image_name, 'id'], + 'image': df_images.at[image_name, 'path'], + 'background': df_images.at[image_name, 'background_path'], + 'noise': df_images.at[image_name, 'noise_path'], + 'df': out[out['image_name'] == image_name] + } list_to_map = list(map(image_data_func, unique_images_to_extract)) # create a list of all the measurements parquet files to extract data from, # such as prefix and max_id list_meas_parquets = list(map( - lambda el: df_images.at[el, 'measurements_path'], + lambda image_name: ( + df_images.at[image_name, 'measurements_path'], + df_images.at[image_name, 'id'], + ), unique_images_to_extract )) del out, unique_images_to_extract, image_data_func @@ -369,7 +423,7 @@ def parallel_extraction( pd.concat(intermediate_df, axis=0, sort=False) .rename( columns={ - 'wavg_ra':'ra', 'wavg_dec':'dec', 'image_name': 'image' + 'wavg_ra': 'ra', 'wavg_dec': 'dec', 'image_name': 'image' } ) ) @@ -378,7 +432,7 @@ def parallel_extraction( def write_group_to_parquet( - df: pd.DataFrame, fname: str, add_mode: bool) -> None: + df: pd.DataFrame, fname: str, add_mode: bool) -> None: ''' Write a dataframe correpondent to a single group/image to a parquet file. @@ -397,7 +451,7 @@ def write_group_to_parquet( out_df = df.drop(['d2d', 'dr', 'source', 'image'], axis=1) if os.path.isfile(fname) and add_mode: exist_df = pd.read_parquet(fname) - out_df = exist_df.append(out_df) + out_df = pd.concat([exist_df, out_df]) out_df.to_parquet(fname, index=False) @@ -405,7 +459,7 @@ def write_group_to_parquet( def parallel_write_parquet( - df: pd.DataFrame, run_path: str, add_mode: bool = False) -> None: + df: pd.DataFrame, run_path: str, add_mode: bool = False) -> None: ''' Parallelize writing parquet files for forced measurements. @@ -421,9 +475,10 @@ def parallel_write_parquet( None ''' images = df['image'].unique().tolist() - get_fname = lambda n: os.path.join( + + def get_fname(n): return os.path.join( run_path, - 'forced_measurements_' + n.replace('.','_') + '.parquet' + 'forced_measurements_' + n.replace('.', '_') + '.parquet' ) dfs = list(map(lambda x: (df[df['image'] == x], get_fname(x)), images)) n_cpu = cpu_count() - 1 @@ -479,8 +534,8 @@ def forced_extraction( run (used in add image mode). Returns: - The sources_df with the extracted sources added and n_forced is the - total number of forced measurements present in the run. + The `sources_df` with the extracted sources added. + The total number of forced measurements present in the run. """ logger.info( 'Starting force extraction step.' @@ -533,14 +588,15 @@ def forced_extraction( # 3. A new relation has been created and they need the forced # measuremnts filled in (actually covered by 2.) - extr_df = ( - extr_df[~extr_df['img_diff'].isin(done_images_df['name'])] - .append(extr_df[ - (~extr_df['source'].isin(done_source_ids)) - & (extr_df['img_diff'].isin(done_images_df.name)) - ]) - .sort_index() - ) + extr_df = pd.concat( + [ + extr_df[~extr_df['img_diff'].isin(done_images_df['name'])], + extr_df[ + (~extr_df['source'].isin(done_source_ids)) + & (extr_df['img_diff'].isin(done_images_df.name)) + ] + ] + ).sort_index() logger.info( f"{extr_df.shape[0]} new measurements to force extract" @@ -640,8 +696,11 @@ def forced_extraction( extr_df = extr_df.rename(columns={'time': 'datetime'}) # append new meas into main df and proceed with source groupby etc - sources_df = sources_df.append( - extr_df.loc[:, extr_df.columns.isin(sources_df.columns)], + sources_df = pd.concat( + [ + sources_df, + extr_df.loc[:, extr_df.columns.isin(sources_df.columns)] + ], ignore_index=True ) diff --git a/vast_pipeline/pipeline/loading.py b/vast_pipeline/pipeline/loading.py index 3ce4a25c7..cf0fde141 100644 --- a/vast_pipeline/pipeline/loading.py +++ b/vast_pipeline/pipeline/loading.py @@ -5,7 +5,7 @@ from typing import List, Optional, Dict, Tuple, Generator, Iterable from itertools import islice -from django.db import transaction, connection, models +from django.db import transaction, connection, models, reset_queries from vast_pipeline.image.main import SelavyImage from vast_pipeline.pipeline.model_generator import ( @@ -13,15 +13,14 @@ source_models_generator, related_models_generator, association_models_generator, - measurement_pair_models_generator, ) from vast_pipeline.models import ( - Association, Measurement, Source, RelatedSource, - MeasurementPair, Run, Image + Association, Band, Measurement, SkyRegion, Source, RelatedSource, + Run, Image ) -from vast_pipeline.pipeline.config import PipelineConfig from vast_pipeline.pipeline.utils import ( - get_create_img, get_create_img_band + get_create_img, get_create_img_band, + get_df_memory_usage, log_total_memory_usage ) from vast_pipeline.utils.utils import StopWatch @@ -33,7 +32,8 @@ def bulk_upload_model( djmodel: models.Model, generator: Iterable[Generator[models.Model, None, None]], - batch_size: int=10_000, return_ids: bool=False + batch_size: int = 10_000, + return_ids: bool = False, ) -> List[int]: ''' Bulk upload a list of generator objects of django models to db. @@ -53,6 +53,8 @@ def bulk_upload_model( None or a list of the database IDs of the uploaded objects. ''' + reset_queries() + bulk_ids = [] while True: items = list(islice(generator, batch_size)) @@ -69,8 +71,8 @@ def bulk_upload_model( def make_upload_images( - paths: Dict[str, Dict[str, str]], config: PipelineConfig, pipeline_run: Run -) -> Tuple[List[Image], pd.DataFrame]: + paths: Dict[str, Dict[str, str]], image_config: Dict +) -> Tuple[List[Image], List[SkyRegion], List[Band]]: ''' Carry the first part of the pipeline, by uploading all the images to the image table and populated band and skyregion objects. @@ -81,14 +83,13 @@ def make_upload_images( the images in the pipeline run. The primary keys are `selavy`, 'noise' and 'background' with the secondary key being the image name. - config (config): - The config object of the pipeline run. - pipeline_run: - The pipeline run object. + image_config: + Dictionary of configuration options for the image ingestion. Returns: - A list of image objects that have been uploaded along with a DataFrame - containing the information of the sky regions associated with the run. + A list of Image objects that have been uploaded. + A list of SkyRegion objects that have been uploaded. + A list of Band objects that have been uploaded. ''' timer = StopWatch() images = [] @@ -100,7 +101,7 @@ def make_upload_images( image = SelavyImage( path, paths, - config + image_config ) logger.info('Reading image %s ...', image.name) @@ -112,79 +113,43 @@ def make_upload_images( # 1.2 create image and skyregion entry in DB with transaction.atomic(): - img, skyreg, exists_f = get_create_img( - pipeline_run, band.id, image - ) + img, exists_f = get_create_img(band.id, image) + skyreg = img.skyreg + + # add image and skyregion to respective lists + images.append(img) + if skyreg not in skyregions: + skyregions.append(skyreg) + + if exists_f: + logger.info("Image %s already processed", img.name) + continue - # add image and skyregion to respective lists - images.append(img) - if skyreg not in skyregions: - skyregions.append(skyreg) - if exists_f: + # 1.3 get the image measurements and save them in DB + measurements = image.read_selavy(img) logger.info( - 'Image %s already processed, grab measurements', - img.name + "Processed measurements dataframe of shape: (%i, %i)", + measurements.shape[0], + measurements.shape[1], ) - # grab the measurements and skip to process next image - measurements = ( - pd.Series( - Measurement.objects.filter(forced=False, image__id=img.id), - name='meas_dj' - ) - .to_frame() - ) - measurements['id'] = measurements['meas_dj'].apply(lambda x: x.id) - continue - - # 1.3 get the image measurements and save them in DB - measurements = image.read_selavy(img) - logger.info( - 'Processed measurements dataframe of shape: (%i, %i)', - measurements.shape[0], measurements.shape[1] - ) - # upload measurements, a column with the db is added to the df - measurements = make_upload_measurements(measurements) + # upload measurements, a column with the db is added to the df + measurements = make_upload_measurements(measurements) - # save measurements to parquet file in pipeline run folder - base_folder = os.path.dirname(img.measurements_path) - if not os.path.exists(base_folder): - os.makedirs(base_folder) + # save measurements to parquet file in pipeline run folder + base_folder = os.path.dirname(img.measurements_path) + if not os.path.exists(base_folder): + os.makedirs(base_folder) - measurements.to_parquet( - img.measurements_path, - index=False - ) - del measurements, image, band, img - - # write images parquet file under pipeline run folder - images_df = pd.DataFrame(map(lambda x: x.__dict__, images)) - images_df = images_df.drop('_state', axis=1) - images_df.to_parquet( - os.path.join(config["run"]["path"], 'images.parquet'), - index=False - ) - # write skyregions parquet file under pipeline run folder - skyregs_df = pd.DataFrame(map(lambda x: x.__dict__, skyregions)) - skyregs_df = skyregs_df.drop('_state', axis=1) - skyregs_df.to_parquet( - os.path.join(config["run"]["path"], 'skyregions.parquet'), - index=False - ) - # write skyregions parquet file under pipeline run folder - bands_df = pd.DataFrame(map(lambda x: x.__dict__, bands)) - bands_df = bands_df.drop('_state', axis=1) - bands_df.to_parquet( - os.path.join(config["run"]["path"], 'bands.parquet'), - index=False - ) + measurements.to_parquet(img.measurements_path, index=False) + del measurements, image, band, img logger.info( 'Total images upload/loading time: %.2f seconds', timer.reset_init() ) - return images, skyregs_df + return images, skyregions, bands def make_upload_sources( @@ -206,6 +171,12 @@ def make_upload_sources( Returns: The input dataframe with the 'id' column added. ''' + + logger.debug("Uploading sources...") + mem_usage = get_df_memory_usage(sources_df) + logger.debug(f"sources_df memory usage: {mem_usage}MB") + log_total_memory_usage() + # create sources in DB with transaction.atomic(): if (add_mode is False and @@ -245,6 +216,9 @@ def make_upload_related_sources(related_df: pd.DataFrame) -> None: None. """ logger.info('Populate "related" field of sources...') + mem_usage = get_df_memory_usage(related_df) + logger.debug(f"related_df memory usage: {mem_usage}MB") + log_total_memory_usage() bulk_upload_model(RelatedSource, related_models_generator(related_df)) @@ -261,9 +235,18 @@ def make_upload_associations(associations_df: pd.DataFrame) -> None: None. """ logger.info('Upload associations...') - bulk_upload_model( - Association, association_models_generator(associations_df) - ) + + mem_usage = get_df_memory_usage(associations_df) + logger.debug(f"associations_df memory usage: {mem_usage}MB") + log_total_memory_usage() + + assoc_chunk_size = 100000 + for i in range(0, len(associations_df), assoc_chunk_size): + bulk_upload_model( + Association, + association_models_generator( + associations_df[i:i + assoc_chunk_size]) + ) def make_upload_measurements(measurements_df: pd.DataFrame) -> pd.DataFrame: @@ -278,6 +261,12 @@ def make_upload_measurements(measurements_df: pd.DataFrame) -> pd.DataFrame: Returns: Original DataFrame with the database ID attached to each row. """ + + logger.info("Upload measurements...") + mem_usage = get_df_memory_usage(measurements_df) + logger.debug(f"measurements_df memory usage: {mem_usage}MB") + log_total_memory_usage() + meas_dj_ids = bulk_upload_model( Measurement, measurement_models_generator(measurements_df), @@ -289,31 +278,6 @@ def make_upload_measurements(measurements_df: pd.DataFrame) -> pd.DataFrame: return measurements_df -def make_upload_measurement_pairs( - measurement_pairs_df: pd.DataFrame -) -> pd.DataFrame: - """ - Uploads the measurement pairs from the supplied measurement pairs - DataFrame. - - Args: - measurement_pairs_df: - DataFrame containing the measurement pairs information from the - pipeline. - - Returns: - Original DataFrame with the database ID attached to each row. - """ - meas_pair_dj_ids = bulk_upload_model( - MeasurementPair, - measurement_pair_models_generator(measurement_pairs_df), - return_ids=True - ) - measurement_pairs_df["id"] = meas_pair_dj_ids - - return measurement_pairs_df - - def update_sources( sources_df: pd.DataFrame, batch_size: int = 10_000 ) -> pd.DataFrame: @@ -347,7 +311,7 @@ def update_sources( sources_df['id'] = sources_df.index.values - batches = np.ceil(len(sources_df)/batch_size) + batches = np.ceil(len(sources_df) / batch_size) dfs = np.array_split(sources_df, batches) with connection.cursor() as cursor: for df_batch in dfs: @@ -392,8 +356,8 @@ def SQL_update( # get names table = model._meta.db_table - new_columns = ', '.join('new_'+c for c in columns) - set_columns = ', '.join(c+'=new_'+c for c in columns) + new_columns = ', '.join('new_' + c for c in columns) + set_columns = ', '.join(c + '=new_' + c for c in columns) # get index values and new values column_headers = [index] diff --git a/vast_pipeline/pipeline/main.py b/vast_pipeline/pipeline/main.py index 6d12bfd3d..a0425ccca 100644 --- a/vast_pipeline/pipeline/main.py +++ b/vast_pipeline/pipeline/main.py @@ -17,6 +17,7 @@ from django.db import transaction from vast_pipeline.models import Run +from vast_pipeline.pipeline.utils import add_run_to_img from .association import association, parallel_association from .config import PipelineConfig from .new_sources import new_sources @@ -26,7 +27,10 @@ from .utils import ( get_src_skyregion_merged_df, group_skyregions, - get_parallel_assoc_image_df + get_parallel_assoc_image_df, + write_parquets, + get_df_memory_usage, + log_total_memory_usage ) from .errors import MaxPipelineRunsError @@ -36,29 +40,33 @@ class Pipeline(): - """Instance of a pipeline. All the methods runs the pipeline opearations, such as - association. + """Instance of a pipeline. All the methods runs the pipeline opearations, + such as association. Attributes: name (str): The name of the pipeline run. config (PipelineConfig): The pipeline run configuration. - img_paths: A dict mapping input image paths to their selavy/noise/background - counterpart path. e.g. `img_paths["selavy"][]` contains the - selavy catalogue file path for ``. + img_paths: A dict mapping input image paths to their + selavy/noise/background counterpart path. e.g. + `img_paths["selavy"][]` contains the selavy catalogue + file path for ``. img_epochs: A dict mapping input image names to their provided epoch. - add_mode: A boolean indicating if this run is adding new images to a previously - executed pipeline run. - previous_parquets: A dict mapping that provides the paths to parquet files for - previous executions of this pipeline run. + add_mode: A boolean indicating if this run is adding new images to a + previously executed pipeline run. + previous_parquets: A dict mapping that provides the paths to parquet + files for previous executions of this pipeline run. """ - def __init__(self, name: str, config_path: str, validate_config: bool = True): - """Initialise an instance of Pipeline with a name and configuration file path. + + def __init__(self, name: str, config_path: str, + validate_config: bool = True): + """Initialise an instance of Pipeline with a name and configuration + file path. Args: name (str): The name of the pipeline run. config_path (str): The path to a YAML run configuration file. - validate_config (bool, optional): Validate the run configuration immediately. - Defaults to True. + validate_config (bool, optional): Validate the run configuration + immediately. Defaults to True. """ self.name: str = name self.config: PipelineConfig = PipelineConfig.from_file( @@ -68,8 +76,11 @@ def __init__(self, name: str, config_path: str, validate_config: bool = True): 'selavy': {}, 'noise': {}, 'background': {}, - } # maps input image paths to their selavy/noise/background counterpart path - self.img_epochs: Dict[str, str] = {} # maps image names to their provided epoch + } + # maps input image paths to their selavy/noise/background + # counterpart path + # maps image names to their provided epoch + self.img_epochs: Dict[str, str] = {} self.add_mode: bool = False self.previous_parquets: Dict[str, str] @@ -81,21 +92,23 @@ def match_images_to_data(self) -> None: Returns: None """ - for key in sorted(self.config["inputs"]["image"].keys()): + inputs = self.config["inputs"] + for key in sorted(inputs["image"].keys()): for x, y in zip( - self.config["inputs"]["image"][key], - self.config["inputs"]["selavy"][key], + inputs["image"][key], + inputs["selavy"][key], ): self.img_paths["selavy"][x] = y self.img_epochs[os.path.basename(x)] = key for x, y in zip( - self.config["inputs"]["image"][key], self.config["inputs"]["noise"][key] + inputs["image"][key], + inputs["noise"][key] ): self.img_paths["noise"][x] = y if "background" in self.config["inputs"]: for x, y in zip( - self.config["inputs"]["image"][key], - self.config["inputs"]["background"][key], + inputs["image"][key], + inputs["background"][key], ): self.img_paths["background"][x] = y @@ -127,12 +140,20 @@ def process_pipeline(self, p_run: Run) -> None: self.match_images_to_data() # upload/retrieve image data - images, skyregs_df = make_upload_images( + images, skyregions, bands = make_upload_images( self.img_paths, - self.config, - p_run + self.config.image_opts() ) + # associate the pipeline run with each image + with transaction.atomic(): + for img in images: + add_run_to_img(p_run, img) + + # write parquet files and retrieve skyregions as a dataframe + skyregs_df = write_parquets( + images, skyregions, bands, self.config["run"]["path"]) + # STEP #2: measurements association # order images by time images.sort(key=operator.attrgetter('datetime')) @@ -224,6 +245,10 @@ def process_pipeline(self, p_run: Run) -> None: done_images_df ) + mem_usage = get_df_memory_usage(sources_df) + logger.debug(f"Step 2: sources_df memory usage: {mem_usage}MB") + log_total_memory_usage() + # Obtain the number of selavy measurements for the run # n_selavy_measurements = sources_df. nr_selavy_measurements = sources_df['id'].unique().shape[0] @@ -276,21 +301,29 @@ def process_pipeline(self, p_run: Run) -> None: done_images_df, done_source_ids ) + mem_usage = get_df_memory_usage(sources_df) + logger.debug(f"Step 5: sources_df memory usage: {mem_usage}MB") + log_total_memory_usage() del missing_sources_df + log_total_memory_usage() + # STEP #6: finalise the df getting unique sources, calculating # metrics and upload data to database - nr_sources = final_operations( + nr_sources, nr_new_sources = final_operations( sources_df, p_run, new_sources_df, + self.config["variability"]["pair_metrics"], self.config["variability"]["source_aggregate_pair_metrics_min_abs_vs"], self.add_mode, done_source_ids, self.previous_parquets ) + log_total_memory_usage() + # calculate number processed images nr_img_processed = len(images) @@ -302,6 +335,7 @@ def process_pipeline(self, p_run: Run) -> None: p_run.n_forced_measurements = ( nr_forced_measurements if self.config["source_monitoring"]["monitor"] else 0 ) + p_run.n_new_sources = nr_new_sources p_run.save() pass @@ -323,7 +357,7 @@ def check_current_runs() -> None: raise MaxPipelineRunsError @staticmethod - def set_status(pipe_run: Run, status: str=None) -> None: + def set_status(pipe_run: Run, status: str = None) -> None: """ Function to change the status of a pipeline run model object and save to the database. @@ -335,7 +369,7 @@ def set_status(pipe_run: Run, status: str=None) -> None: Returns: None """ - #TODO: This function gives no feedback if the status is not accepted? + # TODO: This function gives no feedback if the status is not accepted? choices = [x[0] for x in Run._meta.get_field('status').choices] if status and status in choices and pipe_run.status != status: with transaction.atomic(): diff --git a/vast_pipeline/pipeline/model_generator.py b/vast_pipeline/pipeline/model_generator.py index c084caa01..c81da32ba 100644 --- a/vast_pipeline/pipeline/model_generator.py +++ b/vast_pipeline/pipeline/model_generator.py @@ -7,7 +7,7 @@ from typing import Iterable, Generator from vast_pipeline.utils.utils import deg2hms, deg2dms from vast_pipeline.models import ( - Association, Measurement, MeasurementPair, Source, RelatedSource, Run + Association, Measurement, Source, RelatedSource, Run ) @@ -28,7 +28,7 @@ def measurement_models_generator( Returns: An iterable generator object containing the yielded Measurement - objects. + objects. """ for i, row in meas_df.iterrows(): one_m = Measurement() @@ -56,9 +56,11 @@ def source_models_generator( An iterable generator object containing the yielded Source objects. """ for i, row in src_df.iterrows(): + # generate an IAU compliant source name, see + # https://cdsweb.u-strasbg.fr/Dic/iau-spec.html name = ( - f"J{deg2hms(row['wavg_ra'], precision=1)}" - f"{deg2dms(row['wavg_dec'], precision=0)}" + f"J{deg2hms(row['wavg_ra'], precision=1, truncate=True)}" + f"{deg2dms(row['wavg_dec'], precision=0, truncate=True)}" ).replace(":", "") src = Source() src.run_id = pipeline_run.id @@ -85,6 +87,7 @@ def association_models_generator( Returns: An iterable generator object containing the yielded Association objects. """ + logger.debug(f"Building {len(assoc_df)} association generators") for i, row in assoc_df.iterrows(): yield Association( meas_id=row['id'], @@ -92,6 +95,7 @@ def association_models_generator( d2d=row['d2d'], dr=row['dr'], ) + logger.debug(f"Built {len(assoc_df)} association generators") def related_models_generator( @@ -111,30 +115,3 @@ def related_models_generator( """ for i, row in related_df.iterrows(): yield RelatedSource(**row.to_dict()) - - -def measurement_pair_models_generator( - measurement_pairs_df: pd.DataFrame, -) -> Iterable[Generator[MeasurementPair, None, None]]: - """ - Creates a generator of MeasurementPair objects from an input pipeline - measurement pair dataframe. - - Args: - measurement_pairs_df: - The DataFrame of measurement pairs. - - Returns: - An iterable of MeasurementPair objects, one for each row of - `measurement_pairs_df`. - """ - for i, row in measurement_pairs_df.iterrows(): - yield MeasurementPair( - source_id=row["source_id"], - measurement_a_id=row["id_a"], - measurement_b_id=row["id_b"], - vs_peak=row["vs_peak"], - vs_int=row["vs_int"], - m_peak=row["m_peak"], - m_int=row["m_int"], - ) diff --git a/vast_pipeline/pipeline/new_sources.py b/vast_pipeline/pipeline/new_sources.py index 90048c58c..14d2b3dfb 100644 --- a/vast_pipeline/pipeline/new_sources.py +++ b/vast_pipeline/pipeline/new_sources.py @@ -1,4 +1,3 @@ -import os import logging import pandas as pd import numpy as np @@ -7,15 +6,16 @@ from psutil import cpu_count from astropy import units as u from astropy.coordinates import SkyCoord -from astropy.io import fits from astropy.wcs import WCS from astropy.wcs.utils import ( - skycoord_to_pixel, proj_plane_pixel_scales ) from vast_pipeline.models import Image, Run -from vast_pipeline.utils.utils import StopWatch + +from vast_pipeline.utils.utils import StopWatch, calculate_n_partitions +from vast_pipeline.pipeline.utils import get_df_memory_usage +from vast_pipeline.image.utils import open_fits logger = logging.getLogger(__name__) @@ -48,8 +48,8 @@ def gen_array_coords_from_wcs(coords: SkyCoord, wcs: WCS) -> np.ndarray: Returns: Array containing the x and y array coordinates of the input sky - coordinates, e.g.: - np.array([[x1, x2, x3], [y1, y2, y3]]) + coordinates, e.g.: + np.array([[x1, x2, x3], [y1, y2, y3]]) """ array_coords = wcs.world_to_array_index(coords) array_coords = np.array([ @@ -79,15 +79,27 @@ def get_image_rms_measurements( Returns: The group dataframe with the 'img_diff_true_rms' column added. The - column will contain 'NaN' entires for sources that fail. + column will contain 'NaN' entires for sources that fail. """ + if len(group) == 0: + # input dataframe is empty, nothing to do + logger.debug(f"No image RMS measurements to get, returning") + return group image = group.iloc[0]['img_diff_rms_path'] - with fits.open(image) as hdul: + logger.debug(f"{image} - num. meas. to get: {len(group)}") + partition_mem = get_df_memory_usage(group) + logger.debug(f"{image} - partition memory usage: {partition_mem}MB") + + get_rms_timer = StopWatch() + + with open_fits(image) as hdul: header = hdul[0].header wcs = WCS(header, naxis=2) data = hdul[0].data.squeeze() + logger.debug(f"{image} - Time to load fits: {get_rms_timer.reset()}s") + # Here we mimic the forced fits behaviour, # sources within 3 half BMAJ widths of the image # edges are ignored. The user buffer is also @@ -100,7 +112,7 @@ def get_image_rms_measurements( npix = round( (nbeam / 2. * bmaj.to('arcsec') / - pixelscale).value + pixelscale).value ) npix = int(round(npix * edge_buffer)) @@ -154,7 +166,7 @@ def get_image_rms_measurements( nan_valid = [] # Get slices of each source and check NaN is not included. - for i,j in zip(array_coords[0], array_coords[1]): + for i, j in zip(array_coords[0], array_coords[1]): sl = tuple(( slice(i - acceptable_no_nan_dist, i + acceptable_no_nan_dist), slice(j - acceptable_no_nan_dist, j + acceptable_no_nan_dist) @@ -202,8 +214,9 @@ def parallel_get_rms_measurements( Returns: The original input dataframe with the 'img_diff_true_rms' column - added. The column will contain 'NaN' entires for sources that fail. + added. The column will contain 'NaN' entires for sources that fail. """ + out = df[[ 'source', 'wavg_ra', 'wavg_dec', 'img_diff_rms_path' @@ -218,9 +231,11 @@ def parallel_get_rms_measurements( } n_cpu = cpu_count() - 1 + logger.debug(f"Running association with {n_cpu} CPUs") + n_partitions = calculate_n_partitions(out, n_cpu) out = ( - dd.from_pandas(out, n_cpu) + dd.from_pandas(out, npartitions=n_partitions) .groupby('img_diff_rms_path') .apply( get_image_rms_measurements, @@ -229,8 +244,25 @@ def parallel_get_rms_measurements( ).compute(num_workers=n_cpu, scheduler='processes') ) - df = df.merge( - out[['source', 'img_diff_true_rms']], + # We don't need all of the RMS measurements, just the lowest. Keeping all + # of them results in huge memory usage when merging. However, there is an + # existing bug: https://github.com/askap-vast/vast-pipeline/issues/713 + # that means that we actually want the _highest_ in order to reproduce the + # current behaviour. Fixing the bug is beyond the scope of this PR because + # it means rewriting tests and test data. + + df_to_merge = (df.drop_duplicates('source') + .drop(['img_diff_rms_path'], axis=1) + ) + + out_to_merge = (out.sort_values( + by=['source', 'img_diff_true_rms'], ascending=False + ) + .drop_duplicates('source') + ) + + df = df_to_merge.merge( + out_to_merge[['source', 'img_diff_true_rms']], left_on='source', right_on='source', how='left' ) @@ -243,43 +275,17 @@ def new_sources( min_sigma: float, edge_buffer: float, p_run: Run ) -> pd.DataFrame: """ - Processes the new sources detected to check that they are valid new sources. - This involves checking to see that the source *should* be seen at all in - the images where it is not detected. For valid new sources the snr - value the source would have in non-detected images is also calculated. + Processes the new sources detected to check that they are valid new + sources. This involves checking to see that the source *should* be seen at + all in the images where it is not detected. For valid new sources the + snr value the source would have in non-detected images is also calculated. Args: sources_df: The sources found from the association step. missing_sources_df: The dataframe containing the 'missing detections' for each source. - +----------+----------------------------------+-----------+------------+ - | source | img_list | wavg_ra | wavg_dec | - |----------+----------------------------------+-----------+------------+ - | 278 | ['VAST_0127-73A.EPOCH01.I.fits'] | 22.2929 | -71.8717 | - | 702 | ['VAST_0127-73A.EPOCH01.I.fits'] | 28.8125 | -69.3547 | - | 776 | ['VAST_0127-73A.EPOCH01.I.fits'] | 31.8223 | -70.4674 | - | 844 | ['VAST_0127-73A.EPOCH01.I.fits'] | 17.3152 | -72.346 | - | 934 | ['VAST_0127-73A.EPOCH01.I.fits'] | 9.75754 | -72.9629 | - +----------+----------------------------------+-----------+------------+ - ------------------------------------------------------------------+ - skyreg_img_list | - ------------------------------------------------------------------+ - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ------------------------------------------------------------------+ - ----------------------------------+ - img_diff | - ----------------------------------| - ['VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH08.I.fits'] | - ----------------------------------+ + See the source code comments for the layout of this dataframe. min_sigma: The minimum sigma value acceptable when compared to the minimum rms of the respective image. @@ -291,27 +297,59 @@ def new_sources( Returns: The original input dataframe with the 'img_diff_true_rms' column - added. The column will contain 'NaN' entires for sources that fail. - Columns: - source - source id, int. - img_list - list of images, List. - wavg_ra - weighted average RA, float. - wavg_dec - weighted average Dec, float. - skyreg_img_list - list of sky regions of images in img_list, List. - img_diff - The images missing from coverage, List. - primary - What should be the first image, str. - detection - The first detection image, str. - detection_time - Datetime of detection, datetime.datetime. - img_diff_time - Datetime of img_diff list, datetime.datetime. - img_diff_rms_min - Minimum rms of diff images, float. - img_diff_rms_median - Median rms of diff images, float. - img_diff_rms_path - rms path of diff images, str. - flux_peak - Flux peak of source (detection), float. - diff_sigma - SNR in differnce images (compared to minimum), float. - img_diff_true_rms - The true rms value from the diff images, float. - new_high_sigma - peak flux / true rms value, float. + added. The column will contain 'NaN' entires for sources that fail. + Columns: + source - source id, int. + img_list - list of images, List. + wavg_ra - weighted average RA, float. + wavg_dec - weighted average Dec, float. + skyreg_img_list - list of sky regions of images in img_list, + List. + img_diff - The images missing from coverage, List. + primary - What should be the first image, str. + detection - The first detection image, str. + detection_time - Datetime of detection, datetime.datetime. + img_diff_time - Datetime of img_diff list, datetime.datetime. + img_diff_rms_min - Minimum rms of diff images, float. + img_diff_rms_median - Median rms of diff images, float. + img_diff_rms_path - rms path of diff images, str. + flux_peak - Flux peak of source (detection), float. + diff_sigma - SNR in differnce images (compared to minimum), + float. + img_diff_true_rms - The true rms value from the diff images, + float. + new_high_sigma - peak flux / true rms value, float. """ + # Missing sources df layout + # +----------+----------------------------------+-----------+------------+ + # | source | img_list | wavg_ra | wavg_dec | + # |----------+----------------------------------+-----------+------------+ + # | 278 | ['VAST_0127-73A.EPOCH01.I.fits'] | 22.2929 | -71.8717 | + # | 702 | ['VAST_0127-73A.EPOCH01.I.fits'] | 28.8125 | -69.3547 | + # | 776 | ['VAST_0127-73A.EPOCH01.I.fits'] | 31.8223 | -70.4674 | + # | 844 | ['VAST_0127-73A.EPOCH01.I.fits'] | 17.3152 | -72.346 | + # | 934 | ['VAST_0127-73A.EPOCH01.I.fits'] | 9.75754 | -72.9629 | + # +----------+----------------------------------+-----------+------------+ + # ------------------------------------------------------------------+ + # skyreg_img_list | + # ------------------------------------------------------------------+ + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ------------------------------------------------------------------+ + # ----------------------------------+ + # img_diff | + # ----------------------------------| + # ['VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH08.I.fits'] | + # ----------------------------------+ timer = StopWatch() + debug_timer = StopWatch() logger.info("Starting new source analysis.") @@ -349,7 +387,7 @@ def new_sources( left_on='detection', right_on='name', how='left' - ).rename(columns={'datetime':'detection_time'}) + ).rename(columns={'datetime': 'detection_time'}) new_sources_df = new_sources_df.merge( images_df[[ @@ -360,12 +398,16 @@ def new_sources( right_on='name', how='left' ).rename(columns={ - 'datetime':'img_diff_time', + 'datetime': 'img_diff_time', 'rms_min': 'img_diff_rms_min', 'rms_median': 'img_diff_rms_median', 'noise_path': 'img_diff_rms_path' }) + logger.debug(f"Time to reset & merge image info into new_sources_df: " + f"{debug_timer.reset()}s" + ) + # Select only those images that come before the detection image # in time. new_sources_df = new_sources_df[ @@ -379,6 +421,10 @@ def new_sources( how='left' ).drop(columns=['image']) + logger.debug(f"Time to merge detection fluxes into new_sources_df: " + f"{debug_timer.reset()}s" + ) + # calculate the sigma of the source if it was placed in the # minimum rms region of the previous images new_sources_df['diff_sigma'] = ( @@ -391,6 +437,10 @@ def new_sources( new_sources_df['diff_sigma'] >= min_sigma ] + logger.debug(f"Time to do new_sources_df threshold calcs: " + f"{debug_timer.reset()}s" + ) + # Now have list of sources that should have been seen before given # previous images minimum rms values. @@ -407,9 +457,18 @@ def new_sources( # measure the actual rms in the previous images at # the source location. + + # PR#713: This part of the code should be rewritten to reflect the new + # behaviour of parallel_get_rms_measurements. That function should be + # renamed to something like parallel_get_new_high_sigma and all of the + # subsequent code in this function moved into it. + + logger.debug("Getting rms measurements...") + new_sources_df = parallel_get_rms_measurements( new_sources_df, edge_buffer=edge_buffer ) + logger.debug(f"Time to get rms measurements: {debug_timer.reset()}s") # this removes those that are out of range new_sources_df['img_diff_true_rms'] = ( @@ -426,21 +485,24 @@ def new_sources( ) # We only care about the highest true sigma - new_sources_df = new_sources_df.sort_values( - by=['source', 'true_sigma'] - ) + # new_sources_df = new_sources_df.sort_values( + # by=['source', 'true_sigma'] + # ) # keep only the highest for each source, rename for the daatabase new_sources_df = ( new_sources_df - .drop_duplicates('source') + # .drop_duplicates('source') .set_index('source') - .rename(columns={'true_sigma':'new_high_sigma'}) + .rename(columns={'true_sigma': 'new_high_sigma'}) ) - # moving forward only the new_high_sigma columns is needed, drop all others. + # moving forward only the new_high_sigma columns is needed, drop all + # others. new_sources_df = new_sources_df[['new_high_sigma']] + logger.debug(f"Time to to do final cleanup steps {debug_timer.reset()}s") + logger.info( 'Total new source analysis time: %.2f seconds', timer.reset_init() ) diff --git a/vast_pipeline/pipeline/pairs.py b/vast_pipeline/pipeline/pairs.py new file mode 100644 index 000000000..a7c5eb102 --- /dev/null +++ b/vast_pipeline/pipeline/pairs.py @@ -0,0 +1,188 @@ +from itertools import combinations +import logging + +import dask.dataframe as dd +import numpy as np +import pandas as pd +from psutil import cpu_count + +from vast_pipeline.utils.utils import calculate_n_partitions + + +logger = logging.getLogger(__name__) + + +def calculate_vs_metric( + flux_a: float, flux_b: float, flux_err_a: float, flux_err_b: float +) -> float: + """Calculate the Vs variability metric which is the t-statistic that the provided + fluxes are variable. See Section 5 of Mooley et al. (2016) for details, + DOI: 10.3847/0004-637X/818/2/105. + + Args: + flux_a (float): flux value "A". + flux_b (float): flux value "B". + flux_err_a (float): error of `flux_a`. + flux_err_b (float): error of `flux_b`. + + Returns: + float: the Vs metric for flux values "A" and "B". + """ + return (flux_a - flux_b) / np.hypot(flux_err_a, flux_err_b) + + +def calculate_m_metric(flux_a: float, flux_b: float) -> float: + """Calculate the m variability metric which is the modulation index between two fluxes. + This is proportional to the fractional variability. + See Section 5 of Mooley et al. (2016) for details, DOI: 10.3847/0004-637X/818/2/105. + + Args: + flux_a (float): flux value "A". + flux_b (float): flux value "B". + + Returns: + float: the m metric for flux values "A" and "B". + """ + return 2 * ((flux_a - flux_b) / (flux_a + flux_b)) + + +def calculate_measurement_pair_metrics(df: pd.DataFrame) -> pd.DataFrame: + """Generate a DataFrame of measurement pairs and their 2-epoch variability metrics + from a DataFrame of measurements. For more information on the variability metrics, see + Section 5 of Mooley et al. (2016), DOI: 10.3847/0004-637X/818/2/105. + + Args: + df (pd.DataFrame): Input measurements. Must contain columns: id, source, flux_int, + flux_int_err, flux_peak, flux_peak_err, has_siblings. + + Returns: + Measurement pairs and 2-epoch metrics. Will contain columns: + source - the source ID + id_a, id_b - the measurement IDs + flux_int_a, flux_int_b - measurement integrated fluxes in mJy + flux_int_err_a, flux_int_err_b - measurement integrated flux errors in mJy + flux_peak_a, flux_peak_b - measurement peak fluxes in mJy/beam + flux_peak_err_a, flux_peak_err_b - measurement peak flux errors in mJy/beam + vs_peak, vs_int - variability t-statistic + m_peak, m_int - variability modulation index + """ + n_cpu = cpu_count() - 1 + logger.debug(f"Running association with {n_cpu} CPUs") + n_partitions = calculate_n_partitions(df.set_index('source'), n_cpu) + + """Create a DataFrame containing all measurement ID combinations per source. + Resultant DataFrame will have a MultiIndex(["source", RangeIndex]) where "source" is + the source ID and RangeIndex is an unnamed temporary ID for each measurement pair, + unique only together with source. + DataFrame will have columns [0, 1], each containing a measurement ID. e.g. + 0 1 + source + 1 0 1 9284 + 1 1 17597 + 2 1 26984 + 3 9284 17597 + 4 9284 26984 + ... ... ... + 11105 2 11845 19961 + 11124 0 3573 12929 + 1 3573 21994 + 2 12929 21994 + 11128 0 6216 23534 + """ + measurement_combinations = ( + dd.from_pandas(df, npartitions=n_partitions) + .groupby("source")["id"] + .apply( + lambda x: pd.DataFrame(list(combinations(x, 2))), meta={0: "i", 1: "i"},) + .compute(num_workers=n_cpu, scheduler="processes") + ) + + """Drop the RangeIndex from the MultiIndex as it isn't required and rename the columns. + Example resultant DataFrame: + source id_a id_b + 0 1 1 9284 + 1 1 1 17597 + 2 1 1 26984 + 3 1 9284 17597 + 4 1 9284 26984 + ... ... ... ... + 33640 11105 11845 19961 + 33641 11124 3573 12929 + 33642 11124 3573 21994 + 33643 11124 12929 21994 + 33644 11128 6216 23534 + Where source is the source ID, id_a and id_b are measurement IDs. + """ + measurement_combinations = measurement_combinations.reset_index( + level=1, drop=True + ).rename(columns={0: "id_a", 1: "id_b"}).astype(int).reset_index() + + # Dask has a tendency to swap which order the measurement pairs are + # defined in, even if the dataframe is pre-sorted. We want the pairs to be + # in date order (a < b) so the code below corrects any that are not. + measurement_combinations = measurement_combinations.join( + df[['source', 'id', 'datetime']].set_index(['source', 'id']), + on=['source', 'id_a'], + ) + + measurement_combinations = measurement_combinations.join( + df[['source', 'id', 'datetime']].set_index(['source', 'id']), + on=['source', 'id_b'], lsuffix='_a', rsuffix='_b' + ) + + to_correct_mask = ( + measurement_combinations['datetime_a'] + > measurement_combinations['datetime_b'] + ) + + if np.any(to_correct_mask): + logger.debug('Correcting measurement pairs order') + ( + measurement_combinations.loc[to_correct_mask, 'id_a'], + measurement_combinations.loc[to_correct_mask, 'id_b'] + ) = np.array([ + measurement_combinations.loc[to_correct_mask, 'id_b'].values, + measurement_combinations.loc[to_correct_mask, 'id_a'].values + ]) + + measurement_combinations = measurement_combinations.drop( + ['datetime_a', 'datetime_b'], axis=1 + ) + + # add the measurement fluxes and errors + association_fluxes = df.set_index(["source", "id"])[ + ["flux_int", "flux_int_err", "flux_peak", "flux_peak_err", "image"] + ].rename(columns={"image": "image_name"}) + measurement_combinations = measurement_combinations.join( + association_fluxes, + on=["source", "id_a"], + ).join( + association_fluxes, + on=["source", "id_b"], + lsuffix="_a", + rsuffix="_b", + ) + + # calculate 2-epoch metrics + measurement_combinations["vs_peak"] = calculate_vs_metric( + measurement_combinations.flux_peak_a, + measurement_combinations.flux_peak_b, + measurement_combinations.flux_peak_err_a, + measurement_combinations.flux_peak_err_b, + ) + measurement_combinations["vs_int"] = calculate_vs_metric( + measurement_combinations.flux_int_a, + measurement_combinations.flux_int_b, + measurement_combinations.flux_int_err_a, + measurement_combinations.flux_int_err_b, + ) + measurement_combinations["m_peak"] = calculate_m_metric( + measurement_combinations.flux_peak_a, + measurement_combinations.flux_peak_b, + ) + measurement_combinations["m_int"] = calculate_m_metric( + measurement_combinations.flux_int_a, + measurement_combinations.flux_int_b, + ) + + return measurement_combinations diff --git a/vast_pipeline/pipeline/utils.py b/vast_pipeline/pipeline/utils.py index ca38569cd..2b0409c5e 100644 --- a/vast_pipeline/pipeline/utils.py +++ b/vast_pipeline/pipeline/utils.py @@ -13,37 +13,36 @@ import astropy.units as u import dask import dask.dataframe as dd +import psutil -from typing import List, Optional, Dict, Tuple -from astropy.io import fits +from typing import Any, List, Optional, Dict, Tuple, Union from astropy.coordinates import SkyCoord, Angle -from astropy.io import fits from django.conf import settings from django.contrib.auth.models import User from psutil import cpu_count -from itertools import chain, combinations +from itertools import chain +from vast_pipeline.image.main import FitsImage, SelavyImage +from vast_pipeline.image.utils import open_fits from vast_pipeline.utils.utils import ( - deg2hms, deg2dms, eq_to_cart, StopWatch, - optimize_ints, optimize_floats + eq_to_cart, StopWatch, optimize_ints, optimize_floats, + calculate_n_partitions ) from vast_pipeline.models import ( - Band, Image, Measurement, Run, Source, SkyRegion + Band, Image, Run, SkyRegion ) -from vast_pipeline.image.utils import on_sky_sep logger = logging.getLogger(__name__) dask.config.set({"multiprocessing.context": "fork"}) -def get_create_skyreg(p_run: Run, image: Image) -> SkyRegion: +def get_create_skyreg(image: Image) -> SkyRegion: ''' This creates a Sky Region object in Django ORM given the related image object. Args: - p_run: The pipeline run Django ORM object. image: The image Django ORM object. Returns: @@ -52,39 +51,33 @@ def get_create_skyreg(p_run: Run, image: Image) -> SkyRegion: # In the calculations below, it is assumed the image has square # pixels (this pipeline has been designed for ASKAP images, so it # should always be square). It will likely give wrong results if not - skyr = SkyRegion.objects.filter( + skyregions = SkyRegion.objects.filter( centre_ra=image.ra, centre_dec=image.dec, xtr_radius=image.fov_bmin ) - if skyr: - skyr = skyr.get() + if skyregions: + skyr = skyregions.get() logger.info('Found sky region %s', skyr) - if p_run not in skyr.run.all(): - logger.info('Adding %s to sky region %s', p_run, skyr) - skyr.run.add(p_run) - return skyr - - x, y, z = eq_to_cart(image.ra, image.dec) - skyr = SkyRegion( - centre_ra=image.ra, - centre_dec=image.dec, - width_ra=image.physical_bmin, - width_dec=image.physical_bmaj, - xtr_radius=image.fov_bmin, - x=x, - y=y, - z=z, - ) - skyr.save() - logger.info('Created sky region %s', skyr) - skyr.run.add(p_run) - logger.info('Adding %s to sky region %s', p_run, skyr) + else: + x, y, z = eq_to_cart(image.ra, image.dec) + skyr = SkyRegion( + centre_ra=image.ra, + centre_dec=image.dec, + width_ra=image.physical_bmin, + width_dec=image.physical_bmaj, + xtr_radius=image.fov_bmin, + x=x, + y=y, + z=z, + ) + skyr.save() + logger.info('Created sky region %s', skyr) return skyr -def get_create_img_band(image: Image) -> Band: +def get_create_img_band(image: FitsImage) -> Band: ''' Return the existing Band row for the given FitsImage. An image is considered to belong to a band if its frequency is within some @@ -116,79 +109,63 @@ def get_create_img_band(image: Image) -> Band: return band -def get_create_img( - p_run: Run, band_id: int, image: Image -) -> Tuple[Image, SkyRegion, bool]: +def get_create_img(band_id: int, image: SelavyImage) -> Tuple[Image, bool]: """ - Function to fetch or create the Image and Sky Region objects for the images - in the pipeline run. + Function to fetch or create the Image and Sky Region objects for an image. Args: - p_run: The pipeline run Django ORM object. band_id: The integer database id value of the frequency band of the image. - image: The image Django ORM object. + image: The image object. Returns: - The resulting image django ORM object, the sky region Django ORM - object and a bool value denoting if the image already existed in the - database. + The resulting image django ORM object. + `True` the image already existed in the database, `False` if not. """ - img = Image.objects.filter(name__exact=image.name) - if img.exists(): - img = img.get() + images = Image.objects.filter(name__exact=image.name) + exists = images.exists() + if exists: + img: Image = images.get() # Add background path if not originally provided if image.background_path and not img.background_path: img.background_path = image.background_path img.save() - skyreg = get_create_skyreg(p_run, img) - # check and add the many to many if not existent - if not Image.objects.filter( - id=img.id, run__id=p_run.id - ).exists(): - img.run.add(p_run) - - return (img, skyreg, True) - - # at this stage, measurement parquet file is not created but - # assume location - img_folder_name = image.name.replace('.', '_') - measurements_path = os.path.join( - settings.PIPELINE_WORKING_DIR, - 'images', - img_folder_name, - 'measurements.parquet' + else: + # at this stage, measurement parquet file is not created but + # assume location + img_folder_name = image.name.replace('.', '_') + measurements_path = os.path.join( + settings.PIPELINE_WORKING_DIR, + 'images', + img_folder_name, + 'measurements.parquet' ) - img = Image( - band_id=band_id, - measurements_path=measurements_path - ) - # set the attributes and save the image, - # by selecting only valid (not hidden) attributes - # FYI attributs and/or method starting with _ are hidden - # and with __ can't be modified/called - for fld in img._meta.get_fields(): - if getattr(fld, 'attname', None) and ( - getattr(image, fld.attname, None) is not None - ): - setattr(img, fld.attname, getattr(image, fld.attname)) - - # get create the sky region and associate with image - skyreg = get_create_skyreg(p_run, img) - img.skyreg = skyreg - - img.rms_median, img.rms_min, img.rms_max = get_rms_noise_image_values( - img.noise_path - ) + img = Image( + band_id=band_id, + measurements_path=measurements_path + ) + + # set the attributes and save the image, + # by selecting only valid (not hidden) attributes + # FYI attributs and/or method starting with _ are hidden + # and with __ can't be modified/called + for fld in img._meta.get_fields(): + if getattr(fld, 'attname', None) and ( + getattr(image, fld.attname, None) is not None): + setattr(img, fld.attname, getattr(image, fld.attname)) - img.save() - img.run.add(p_run) + img.rms_median, img.rms_min, img.rms_max = get_rms_noise_image_values( + img.noise_path) - return (img, skyreg, False) + # get create the sky region and associate with image + img.skyreg = get_create_skyreg(img) + img.save() + + return (img, exists) def get_create_p_run( - name: str, path: str, description: str=None, user: User=None + name: str, path: str, description: str = None, user: User = None ) -> Tuple[Run, bool]: ''' Get or create a pipeline run in db, return the run django object and @@ -202,8 +179,8 @@ def get_create_p_run( user: The Django user that launched the pipeline run. Returns: - The pipeline run object and a boolean object representing whether - the pipeline run already existed ('True') or not ('False'). + The pipeline run object. + Whether the pipeline run already existed ('True') or not ('False'). ''' p_run = Run.objects.filter(name__exact=name) if p_run: @@ -218,6 +195,30 @@ def get_create_p_run( return p_run, False +def add_run_to_img(pipeline_run: Run, img: Image) -> None: + """ + Add a pipeline run to an Image (and corresponding SkyRegion) in the db + + Args: + pipeline_run: + Pipeline run object you want to add. + img: + Image object you want to add to. + + Returns: + None + """ + skyreg = img.skyreg + # check and add the many to many if not existent + if not Image.objects.filter(id=img.id, run__id=pipeline_run.id).exists(): + logger.info('Adding %s to image %s', pipeline_run, img.name) + img.run.add(pipeline_run) + + if pipeline_run not in skyreg.run.all(): + logger.info('Adding %s to sky region %s', pipeline_run, skyreg) + skyreg.run.add(pipeline_run) + + def remove_duplicate_measurements( sources_df: pd.DataFrame, dup_lim: Optional[Angle] = None, @@ -252,8 +253,6 @@ def remove_duplicate_measurements( 'Using duplicate crossmatch radius of %.2f arcsec.', dup_lim.arcsec ) - min_source = sources_df['source'].min() - # sort by the distance from the image centre so we know # that the first source is always the one to keep sources_df = sources_df.sort_values(by='dist_from_centre') @@ -265,7 +264,7 @@ def remove_duplicate_measurements( ) # perform search around sky to get all self matches - idxc, idxcatalog, d2d_around, _ = sources_sc.search_around_sky( + idxc, idxcatalog, *_ = sources_sc.search_around_sky( sources_sc, dup_lim ) @@ -346,7 +345,7 @@ def _load_measurements( Returns: The measurements of the image with some extra values set ready for - association . + association. """ image_centre = SkyCoord( image.ra, @@ -406,7 +405,7 @@ def prep_skysrc_df( Returns: The measurements of the image(s) with some extra values set ready for - association and duplicates removed if necessary. + association and duplicates removed if necessary. ''' cols = [ 'id', @@ -432,8 +431,13 @@ def prep_skysrc_df( if len(images) > 1: for img in images[1:]: - df = df.append( - _load_measurements(img, cols, df.source.max(), ini_df=ini_df), + df = pd.concat( + [ + df, + _load_measurements( + img, cols, df.source.max(), ini_df=ini_df + ) + ], ignore_index=True ) @@ -485,7 +489,7 @@ def add_new_one_to_many_relations( Returns: The new related field for the source in question, containing the - appended ids. + appended ids. """ if source_ids is None: source_ids = pd.DataFrame() @@ -499,7 +503,7 @@ def add_new_one_to_many_relations( out = row[related_col] out.append(row[source_col]) else: - out = [row[source_col],] + out = [row[source_col], ] else: # the original case to append all the new ids. source_ids = source_ids.loc[row[source_col]].iloc[0] @@ -526,7 +530,7 @@ def add_new_many_to_one_relations(row: pd.Series) -> List[int]: Returns: The new related field for the source in question, containing the - appended ids. + appended ids. """ out = row['new_relations'].copy() @@ -546,7 +550,6 @@ def cross_join(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame: Returns: The resultant merged DataFrame. - """ return ( left.assign(key=1) @@ -556,7 +559,7 @@ def cross_join(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame: def get_eta_metric( - row: Dict[str, float], df: pd.DataFrame, peak: bool=False + row: Dict[str, float], df: pd.DataFrame, peak: bool = False ) -> float: ''' Calculates the eta variability metric of a source. @@ -572,7 +575,6 @@ def get_eta_metric( Returns: The calculated eta value. - ''' if row['n_meas'] == 1: return 0. @@ -580,7 +582,7 @@ def get_eta_metric( suffix = 'peak' if peak else 'int' weights = 1. / df[f'flux_{suffix}_err'].values**2 fluxes = df[f'flux_{suffix}'].values - eta = (row['n_meas'] / (row['n_meas']-1)) * ( + eta = (row['n_meas'] / (row['n_meas'] - 1)) * ( (weights * fluxes**2).mean() - ( (weights * fluxes).mean()**2 / weights.mean() ) @@ -600,7 +602,6 @@ def groupby_funcs(df: pd.DataFrame) -> pd.Series: Returns: Pandas series containing the calculated metrics of the source. - ''' # calculated average ra, dec, fluxes and metrics d = {} @@ -610,7 +611,7 @@ def groupby_funcs(df: pd.DataFrame) -> pd.Series: d['n_meas_sel'] = d['n_meas'] - d['n_meas_forced'] d['n_sibl'] = df['has_siblings'].sum() if d['n_meas_forced'] > 0: - non_forced_sel = df['forced'] != True + non_forced_sel = ~df['forced'] d['wavg_ra'] = ( df.loc[non_forced_sel, 'interim_ew'].sum() / df.loc[non_forced_sel, 'weight_ew'].sum() @@ -707,7 +708,10 @@ def parallel_groupby(df: pd.DataFrame) -> pd.DataFrame: 'related_list': 'O' } n_cpu = cpu_count() - 1 - out = dd.from_pandas(df, n_cpu) + logger.debug(f"Running association with {n_cpu} CPUs") + n_partitions = calculate_n_partitions(df, n_cpu) + + out = dd.from_pandas(df.set_index('source'), npartitions=n_partitions) out = ( out.groupby('source') .apply( @@ -717,7 +721,8 @@ def parallel_groupby(df: pd.DataFrame) -> pd.DataFrame: .compute(num_workers=n_cpu, scheduler='processes') ) - out['n_rel'] = out['related_list'].apply(lambda x: 0 if x == -1 else len(x)) + out['n_rel'] = out['related_list'].apply( + lambda x: 0 if x == -1 else len(x)) return out @@ -734,7 +739,7 @@ def calc_ave_coord(grp: pd.DataFrame) -> pd.Series: Returns: A pandas series containing the average coordinate along with the - image and epoch lists. + image and epoch lists. """ d = {} grp = grp.sort_values(by='datetime') @@ -757,7 +762,7 @@ def parallel_groupby_coord(df: pd.DataFrame) -> pd.DataFrame: Returns: The resulting average coordinate values and unique image and epoch - lists for each unique source (group). + lists for each unique source (group). """ col_dtype = { 'img_list': 'O', @@ -766,7 +771,10 @@ def parallel_groupby_coord(df: pd.DataFrame) -> pd.DataFrame: 'wavg_dec': 'f', } n_cpu = cpu_count() - 1 - out = dd.from_pandas(df, n_cpu) + logger.debug(f"Running association with {n_cpu} CPUs") + n_partitions = calculate_n_partitions(df, n_cpu) + + out = dd.from_pandas(df.set_index('source'), npartitions=n_partitions) out = ( out.groupby('source') .apply(calc_ave_coord, meta=col_dtype) @@ -786,7 +794,9 @@ def get_rms_noise_image_values(rms_path: str) -> Tuple[float, float, float]: rms_path: The system path to the RMS FITS image. Returns: - The median, minimum and maximum values of the RMS image. + The median value of the RMS image. + The minimum value of the RMS image. + The maximum value of the RMS image. Raises: IOError: Raised when the RMS FITS file cannot be found. @@ -794,21 +804,21 @@ def get_rms_noise_image_values(rms_path: str) -> Tuple[float, float, float]: logger.debug('Extracting Image RMS values from Noise file...') med_val = min_val = max_val = 0. try: - with fits.open(rms_path) as f: + with open_fits(rms_path) as f: data = f[0].data - data = data[np.logical_not(np.isnan(data))] - data = data[data != 0] + data = data[np.isfinite(data) & (data > 0.)] med_val = np.median(data) * 1e+3 min_val = np.min(data) * 1e+3 max_val = np.max(data) * 1e+3 del data except Exception: raise IOError(f'Could not read this RMS FITS file: {rms_path}') + logger.debug('Image RMS Min: %.3g Max: %.3g Median: %.3g', min_val, max_val, med_val) return med_val, min_val, max_val -def get_image_list_diff(row: pd.Series) -> List[str]: +def get_image_list_diff(row: pd.Series) -> Union[List[str], int]: """ Calculate the difference between the ideal coverage image list of a source and the actual observed image list. Also checks whether an epoch does in @@ -819,8 +829,8 @@ def get_image_list_diff(row: pd.Series) -> List[str]: row: The row from the sources dataframe that is being iterated over. Returns: - A list of the images missing from the observed image list. Will be - returned as '-1' integer value if there are no missing images. + A list of the images missing from the observed image list. + A '-1' integer value if there are no missing images. """ out = list( filter(lambda arg: arg not in row['img_list'], row['skyreg_img_list']) @@ -845,7 +855,7 @@ def get_image_list_diff(row: pd.Series) -> List[str]: ] if not out: - out = -1 + return -1 return out @@ -861,10 +871,10 @@ def get_names_and_epochs(grp: pd.DataFrame) -> pd.Series: Returns: Pandas series containing the list object that contains the lists of the - image names, epochs and datetimes. + image names, epochs and datetimes. """ d = {} - d['skyreg_img_epoch_list'] = [[[x,], y, z] for x,y,z in zip( + d['skyreg_img_epoch_list'] = [[[x, ], y, z] for x, y, z in zip( grp['name'].values.tolist(), grp['epoch'].values.tolist(), grp['datetime'].values.tolist() @@ -908,44 +918,46 @@ def get_src_skyregion_merged_df( sky region objects for the run loaded into a dataframe. Returns: - DataFrame containing missing image information. Output format: - +----------+----------------------------------+-----------+------------+ - | source | img_list | wavg_ra | wavg_dec | - |----------+----------------------------------+-----------+------------+ - | 278 | ['VAST_0127-73A.EPOCH01.I.fits'] | 22.2929 | -71.8717 | - | 702 | ['VAST_0127-73A.EPOCH01.I.fits'] | 28.8125 | -69.3547 | - | 844 | ['VAST_0127-73A.EPOCH01.I.fits'] | 17.3152 | -72.346 | - | 934 | ['VAST_0127-73A.EPOCH01.I.fits'] | 9.75754 | -72.9629 | - | 1290 | ['VAST_0127-73A.EPOCH01.I.fits'] | 20.8455 | -76.8269 | - +----------+----------------------------------+-----------+------------+ - ------------------------------------------------------------------+ - skyreg_img_list | - ------------------------------------------------------------------+ - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | - ------------------------------------------------------------------+ - ----------------------------------+------------------------------+ - img_diff | primary | - ----------------------------------+------------------------------+ - ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | - ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | - ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | - ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | - ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | - ----------------------------------+------------------------------+ - ------------------------------+--------------+ - detection | in_primary | - ------------------------------+--------------| - VAST_0127-73A.EPOCH01.I.fits | True | - VAST_0127-73A.EPOCH01.I.fits | True | - VAST_0127-73A.EPOCH01.I.fits | True | - VAST_0127-73A.EPOCH01.I.fits | True | - VAST_0127-73A.EPOCH01.I.fits | True | - ------------------------------+--------------+ + DataFrame containing missing image information (see source code for + dataframe format). """ + # Output format: + # +----------+----------------------------------+-----------+------------+ + # | source | img_list | wavg_ra | wavg_dec | + # |----------+----------------------------------+-----------+------------+ + # | 278 | ['VAST_0127-73A.EPOCH01.I.fits'] | 22.2929 | -71.8717 | + # | 702 | ['VAST_0127-73A.EPOCH01.I.fits'] | 28.8125 | -69.3547 | + # | 844 | ['VAST_0127-73A.EPOCH01.I.fits'] | 17.3152 | -72.346 | + # | 934 | ['VAST_0127-73A.EPOCH01.I.fits'] | 9.75754 | -72.9629 | + # | 1290 | ['VAST_0127-73A.EPOCH01.I.fits'] | 20.8455 | -76.8269 | + # +----------+----------------------------------+-----------+------------+ + # ------------------------------------------------------------------+ + # skyreg_img_list | + # ------------------------------------------------------------------+ + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ['VAST_0127-73A.EPOCH01.I.fits', 'VAST_0127-73A.EPOCH08.I.fits'] | + # ------------------------------------------------------------------+ + # ----------------------------------+------------------------------+ + # img_diff | primary | + # ----------------------------------+------------------------------+ + # ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | + # ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | + # ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | + # ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | + # ['VAST_0127-73A.EPOCH08.I.fits'] | VAST_0127-73A.EPOCH01.I.fits | + # ----------------------------------+------------------------------+ + # ------------------------------+--------------+ + # detection | in_primary | + # ------------------------------+--------------| + # VAST_0127-73A.EPOCH01.I.fits | True | + # VAST_0127-73A.EPOCH01.I.fits | True | + # VAST_0127-73A.EPOCH01.I.fits | True | + # VAST_0127-73A.EPOCH01.I.fits | True | + # VAST_0127-73A.EPOCH01.I.fits | True | + # ------------------------------+--------------+ logger.info("Creating ideal source coverage df...") merged_timer = StopWatch() @@ -979,36 +991,37 @@ def get_src_skyregion_merged_df( del sources_df - # create dataframe with all skyregions and sources combinations - src_skyrg_df = cross_join( - srcs_df.drop(['epoch_list', 'img_list'], axis=1).reset_index(), - skyreg_df.drop('skyreg_img_epoch_list', axis=1) + # crossmatch sources with sky regions up to the max sky region radius + skyreg_coords = SkyCoord( + ra=skyreg_df.centre_ra, dec=skyreg_df.centre_dec, unit="deg" ) - - skyreg_df = skyreg_df.drop( - ['centre_ra', 'centre_dec', 'xtr_radius'], - axis=1 - ).set_index('id') - - src_skyrg_df['sep'] = np.rad2deg( - on_sky_sep( - np.deg2rad(src_skyrg_df['wavg_ra'].values), - np.deg2rad(src_skyrg_df['centre_ra'].values), - np.deg2rad(src_skyrg_df['wavg_dec'].values), - np.deg2rad(src_skyrg_df['centre_dec'].values), - ) + srcs_coords = SkyCoord( + ra=srcs_df.wavg_ra, + dec=srcs_df.wavg_dec, + unit="deg") + skyreg_idx, srcs_idx, sep, _ = srcs_coords.search_around_sky( + skyreg_coords, skyreg_df.xtr_radius.max() * u.deg ) + skyreg_df = skyreg_df.drop( + columns=[ + "centre_ra", + "centre_dec"]).set_index("id") # select rows where separation is less than sky region radius # drop not more useful columns and groupby source id # compute list of images src_skyrg_df = ( - src_skyrg_df.loc[ - src_skyrg_df.sep < src_skyrg_df.xtr_radius, - ['source', 'id', 'sep'] - ].merge(skyreg_df, left_on='id', right_index=True) - .drop('id', axis=1) - .explode('skyreg_img_epoch_list') + pd.DataFrame( + { + "source": srcs_df.iloc[srcs_idx].index, + "id": skyreg_df.iloc[skyreg_idx].index, + "sep": sep.to("deg").value, + } + ) + .merge(skyreg_df, left_on="id", right_index=True) + .query("sep < xtr_radius") + .drop(columns=["id", "xtr_radius"]) + .explode("skyreg_img_epoch_list") ) del skyreg_df @@ -1036,12 +1049,12 @@ def get_src_skyregion_merged_df( # annoyingly epoch needs to be not a list to drop duplicates # but then we need to sum the epochs into a list src_skyrg_df['skyreg_epoch'] = src_skyrg_df['skyreg_epoch'].apply( - lambda x: [x,] + lambda x: [x, ] ) src_skyrg_df = ( src_skyrg_df.groupby('source') - .agg('sum') # sum because we need to preserve order + .sum(numeric_only=False) # sum because we need to preserve order ) # merge into main df and compare the images @@ -1110,7 +1123,7 @@ def _get_skyregion_relations( Returns: A list of other sky regions (including self) that are within the - 'xtr_radius' of the sky region in the row. + 'xtr_radius' of the sky region in the row. ''' target = SkyCoord( row['centre_ra'], @@ -1151,13 +1164,13 @@ def group_skyregions(df: pd.DataFrame) -> pd.DataFrame: Returns: The sky region group of each skyregion id. - +----+----------------+ - | | skyreg_group | - |----+----------------| - | 2 | 1 | - | 3 | 1 | - | 1 | 2 | - +----+----------------+ + +----+----------------+ + | | skyreg_group | + |----+----------------| + | 2 | 1 | + | 3 | 1 | + | 1 | 2 | + +----+----------------+ """ sr_coords = SkyCoord( df['centre_ra'], @@ -1173,7 +1186,7 @@ def group_skyregions(df: pd.DataFrame) -> pd.DataFrame: axis=1 ) - skyreg_groups = {} + skyreg_groups: Dict[int, List[Any]] = {} master_done = [] # keep track of all checked ids in master done @@ -1223,7 +1236,7 @@ def group_skyregions(df: pd.DataFrame) -> pd.DataFrame: skyreg_group_ids = {} for i in skyreg_groups: for j in skyreg_groups[i]: - skyreg_group_ids[j]=i + skyreg_group_ids[j] = i skyreg_group_ids = pd.DataFrame.from_dict( skyreg_group_ids, orient='index' @@ -1252,20 +1265,22 @@ def get_parallel_assoc_image_df( +----+----------------+ Returns: - Dataframe containing the merged images and skyreg_id and skyreg_group. - +----+-------------------------------+-------------+----------------+ - | | image | skyreg_id | skyreg_group | - |----+-------------------------------+-------------+----------------| - | 0 | VAST_2118+00A.EPOCH01.I.fits | 2 | 1 | - | 1 | VAST_2118-06A.EPOCH01.I.fits | 3 | 1 | - | 2 | VAST_0127-73A.EPOCH01.I.fits | 1 | 2 | - | 3 | VAST_2118-06A.EPOCH03x.I.fits | 3 | 1 | - | 4 | VAST_2118-06A.EPOCH02.I.fits | 3 | 1 | - | 5 | VAST_2118-06A.EPOCH05x.I.fits | 3 | 1 | - | 6 | VAST_2118-06A.EPOCH06x.I.fits | 3 | 1 | - | 7 | VAST_0127-73A.EPOCH08.I.fits | 1 | 2 | - +----+-------------------------------+-------------+----------------+ + Dataframe containing the merged images and skyreg_id and skyreg_group + (see source code for output format). """ + # Output format + # +----+-------------------------------+-------------+----------------+ + # | | image | skyreg_id | skyreg_group | + # |----+-------------------------------+-------------+----------------| + # | 0 | VAST_2118+00A.EPOCH01.I.fits | 2 | 1 | + # | 1 | VAST_2118-06A.EPOCH01.I.fits | 3 | 1 | + # | 2 | VAST_0127-73A.EPOCH01.I.fits | 1 | 2 | + # | 3 | VAST_2118-06A.EPOCH03x.I.fits | 3 | 1 | + # | 4 | VAST_2118-06A.EPOCH02.I.fits | 3 | 1 | + # | 5 | VAST_2118-06A.EPOCH05x.I.fits | 3 | 1 | + # | 6 | VAST_2118-06A.EPOCH06x.I.fits | 3 | 1 | + # | 7 | VAST_0127-73A.EPOCH08.I.fits | 1 | 2 | + # +----+-------------------------------+-------------+----------------+ skyreg_ids = [i.skyreg_id for i in images] images_df = pd.DataFrame({ @@ -1343,6 +1358,10 @@ def create_measurements_arrow_file(p_run: Run) -> None: .rename(columns={'source_id': 'source'}) ) + # drop timezone from datetime for vaex compatibility + # TODO: Look to keep the timezone if/when vaex is compatible. + measurements['time'] = measurements['time'].dt.tz_localize(None) + logger.debug('Optimising dataframes.') measurements = optimize_ints(optimize_floats(measurements)) @@ -1355,8 +1374,8 @@ def create_measurements_arrow_file(p_run: Run) -> None: local = pa.fs.LocalFileSystem() with local.open_output_stream(outname) as file: - with pa.RecordBatchFileWriter(file, measurements.schema) as writer: - writer.write_table(measurements) + with pa.RecordBatchFileWriter(file, measurements.schema) as writer: + writer.write_table(measurements) def create_measurement_pairs_arrow_file(p_run: Run) -> None: @@ -1392,183 +1411,8 @@ def create_measurement_pairs_arrow_file(p_run: Run) -> None: local = pa.fs.LocalFileSystem() with local.open_output_stream(outname) as file: - with pa.RecordBatchFileWriter(file, measurement_pairs_df.schema) as writer: - writer.write_table(measurement_pairs_df) - - -def calculate_vs_metric( - flux_a: float, flux_b: float, flux_err_a: float, flux_err_b: float -) -> float: - """Calculate the Vs variability metric which is the t-statistic that the provided - fluxes are variable. See Section 5 of Mooley et al. (2016) for details, - DOI: 10.3847/0004-637X/818/2/105. - - Args: - flux_a (float): flux value "A". - flux_b (float): flux value "B". - flux_err_a (float): error of `flux_a`. - flux_err_b (float): error of `flux_b`. - - Returns: - float: the Vs metric for flux values "A" and "B". - """ - return (flux_a - flux_b) / np.hypot(flux_err_a, flux_err_b) - - -def calculate_m_metric(flux_a: float, flux_b: float) -> float: - """Calculate the m variability metric which is the modulation index between two fluxes. - This is proportional to the fractional variability. - See Section 5 of Mooley et al. (2016) for details, DOI: 10.3847/0004-637X/818/2/105. - - Args: - flux_a (float): flux value "A". - flux_b (float): flux value "B". - - Returns: - float: the m metric for flux values "A" and "B". - """ - return 2 * ((flux_a - flux_b) / (flux_a + flux_b)) - - -def calculate_measurement_pair_metrics(df: pd.DataFrame) -> pd.DataFrame: - """Generate a DataFrame of measurement pairs and their 2-epoch variability metrics - from a DataFrame of measurements. For more information on the variability metrics, see - Section 5 of Mooley et al. (2016), DOI: 10.3847/0004-637X/818/2/105. - - Args: - df (pd.DataFrame): Input measurements. Must contain columns: id, source, flux_int, - flux_int_err, flux_peak, flux_peak_err, has_siblings. - - Returns: - pd.DataFrame: Measurement pairs and 2-epoch metrics. Will contain columns: - source - the source ID - id_a, id_b - the measurement IDs - flux_int_a, flux_int_b - measurement integrated fluxes in mJy - flux_int_err_a, flux_int_err_b - measurement integrated flux errors in mJy - flux_peak_a, flux_peak_b - measurement peak fluxes in mJy/beam - flux_peak_err_a, flux_peak_err_b - measurement peak flux errors in mJy/beam - vs_peak, vs_int - variability t-statistic - m_peak, m_int - variability modulation index - """ - n_cpu = cpu_count() - 1 - - """Create a DataFrame containing all measurement ID combinations per source. - Resultant DataFrame will have a MultiIndex(["source", RangeIndex]) where "source" is - the source ID and RangeIndex is an unnamed temporary ID for each measurement pair, - unique only together with source. - DataFrame will have columns [0, 1], each containing a measurement ID. e.g. - 0 1 - source - 1 0 1 9284 - 1 1 17597 - 2 1 26984 - 3 9284 17597 - 4 9284 26984 - ... ... ... - 11105 2 11845 19961 - 11124 0 3573 12929 - 1 3573 21994 - 2 12929 21994 - 11128 0 6216 23534 - """ - measurement_combinations = ( - dd.from_pandas(df, n_cpu) - .groupby("source")["id"] - .apply( - lambda x: pd.DataFrame(list(combinations(x, 2)) - ), meta={0: "i", 1: "i"},) - .compute(num_workers=n_cpu, scheduler="processes") - ) - - """Drop the RangeIndex from the MultiIndex as it isn't required and rename the columns. - Example resultant DataFrame: - source id_a id_b - 0 1 1 9284 - 1 1 1 17597 - 2 1 1 26984 - 3 1 9284 17597 - 4 1 9284 26984 - ... ... ... ... - 33640 11105 11845 19961 - 33641 11124 3573 12929 - 33642 11124 3573 21994 - 33643 11124 12929 21994 - 33644 11128 6216 23534 - Where source is the source ID, id_a and id_b are measurement IDs. - """ - measurement_combinations = measurement_combinations.reset_index( - level=1, drop=True - ).rename(columns={0: "id_a", 1: "id_b"}).astype(int).reset_index() - - # Dask has a tendency to swap which order the measurement pairs are - # defined in, even if the dataframe is pre-sorted. We want the pairs to be - # in date order (a < b) so the code below corrects any that are not. - measurement_combinations = measurement_combinations.join( - df[['source', 'id', 'datetime']].set_index(['source', 'id']), - on=['source', 'id_a'], - ) - - measurement_combinations = measurement_combinations.join( - df[['source', 'id', 'datetime']].set_index(['source', 'id']), - on=['source', 'id_b'], lsuffix='_a', rsuffix='_b' - ) - - to_correct_mask = ( - measurement_combinations['datetime_a'] - > measurement_combinations['datetime_b'] - ) - - if np.any(to_correct_mask): - logger.debug('Correcting measurement pairs order') - ( - measurement_combinations.loc[to_correct_mask, 'id_a'], - measurement_combinations.loc[to_correct_mask, 'id_b'] - ) = np.array([ - measurement_combinations.loc[to_correct_mask, 'id_b'].values, - measurement_combinations.loc[to_correct_mask, 'id_a'].values - ]) - - measurement_combinations = measurement_combinations.drop( - ['datetime_a', 'datetime_b'], axis=1 - ) - - # add the measurement fluxes and errors - association_fluxes = df.set_index(["source", "id"])[ - ["flux_int", "flux_int_err", "flux_peak", "flux_peak_err", "image"] - ].rename(columns={"image": "image_name"}) - measurement_combinations = measurement_combinations.join( - association_fluxes, - on=["source", "id_a"], - ).join( - association_fluxes, - on=["source", "id_b"], - lsuffix="_a", - rsuffix="_b", - ) - - # calculate 2-epoch metrics - measurement_combinations["vs_peak"] = calculate_vs_metric( - measurement_combinations.flux_peak_a, - measurement_combinations.flux_peak_b, - measurement_combinations.flux_peak_err_a, - measurement_combinations.flux_peak_err_b, - ) - measurement_combinations["vs_int"] = calculate_vs_metric( - measurement_combinations.flux_int_a, - measurement_combinations.flux_int_b, - measurement_combinations.flux_int_err_a, - measurement_combinations.flux_int_err_b, - ) - measurement_combinations["m_peak"] = calculate_m_metric( - measurement_combinations.flux_peak_a, - measurement_combinations.flux_peak_b, - ) - measurement_combinations["m_int"] = calculate_m_metric( - measurement_combinations.flux_int_a, - measurement_combinations.flux_int_b, - ) - - return measurement_combinations + with pa.RecordBatchFileWriter(file, measurement_pairs_df.schema) as writer: + writer.write_table(measurement_pairs_df) def backup_parquets(p_run_path: str) -> None: @@ -1596,6 +1440,29 @@ def backup_parquets(p_run_path: str) -> None: shutil.copyfile(i, backup_name) +def create_temp_config_file(p_run_path: str) -> None: + """ + Creates the temp config file which is saved at the beginning of each run. + + It is to avoid issues created by users changing the config while the run + is running. + + Args: + p_run_path: + The path of the pipeline run of the config to be copied. + + Returns: + None + """ + config_name = 'config.yaml' + temp_config_name = 'config_temp.yaml' + + shutil.copyfile( + os.path.join(p_run_path, config_name), + os.path.join(p_run_path, temp_config_name) + ) + + def reconstruct_associtaion_dfs( images_df_done: pd.DataFrame, previous_parquet_paths: Dict[str, str] @@ -1614,15 +1481,20 @@ def reconstruct_associtaion_dfs( and 'measurement_pairs'. Returns: - The reconstructed sources_df and skyc1_srs dataframes. + The reconstructed `sources_df` dataframe. + The reconstructed `skyc1_srs` dataframes. """ prev_associations = pd.read_parquet(previous_parquet_paths['associations']) + logger.debug(images_df_done) + logger.debug(images_df_done['image_dj']) + # Get the parquet paths from the image objects img_meas_paths = ( images_df_done['image_dj'].apply(lambda x: x.measurements_path) .to_list() ) + logger.debug(img_meas_paths) # Obtain the pipeline run path in order to fetch forced measurements. run_path = previous_parquet_paths['sources'].replace( @@ -1749,7 +1621,7 @@ def reconstruct_associtaion_dfs( relation_ids = sources_df[ sources_df.source.isin(prev_relations.index.values)].drop_duplicates( 'source', keep='last' - ).index.values + ).index.values # Make sure we attach the correct source id source_ids = sources_df.loc[relation_ids].source.values sources_df['related'] = np.nan @@ -1770,7 +1642,7 @@ def reconstruct_associtaion_dfs( # Create the unique skyc1_srcs dataframe. skyc1_srcs = ( - sources_df[sources_df['forced'] == False] + sources_df[~sources_df['forced']] .sort_values(by='id') .drop('related', axis=1) .drop_duplicates('source') @@ -1780,7 +1652,7 @@ def reconstruct_associtaion_dfs( # which does not have the relation information) skyc1_srcs = skyc1_srcs.merge( prev_relations, how='left', left_on='source', right_index=True -) + ) # Need to break the pointer relationship between the related sources ( # deep=True copy does not truly copy mutable type objects) @@ -1811,3 +1683,89 @@ def reconstruct_associtaion_dfs( ], axis=1).reset_index(drop=True) return sources_df, skyc1_srcs + + +def write_parquets( + images: List[Image], + skyregions: List[SkyRegion], + bands: List[Band], + run_path: str +) -> pd.DataFrame: + """ + This function saves images, skyregions and bands to parquet files. + It also returns a DataFrame containing containing the information + of the sky regions associated with the current run. + + Args: + images: list of image Django ORM objects. + skyregions: list sky region Django ORM objects. + bands: list of band Django ORM objects. + run_path: directory to save parquets to. + + Returns: + Sky regions as pandas DataFrame. + """ + # write images parquet file under pipeline run folder + images_df = pd.DataFrame(map(lambda x: x.__dict__, images)) + images_df = images_df.drop('_state', axis=1) + images_df.to_parquet( + os.path.join(run_path, 'images.parquet'), + index=False + ) + # write skyregions parquet file under pipeline run folder + skyregs_df = pd.DataFrame(map(lambda x: x.__dict__, skyregions)) + skyregs_df = skyregs_df.drop('_state', axis=1) + skyregs_df.to_parquet( + os.path.join(run_path, 'skyregions.parquet'), + index=False + ) + # write skyregions parquet file under pipeline run folder + bands_df = pd.DataFrame(map(lambda x: x.__dict__, bands)) + bands_df = bands_df.drop('_state', axis=1) + bands_df.to_parquet( + os.path.join(run_path, 'bands.parquet'), + index=False + ) + + return skyregs_df + + +def get_total_memory_usage(): + """ + This function gets the current memory usage and returns a string. + + Returns: + A float containing the current resource usage. + """ + mem = psutil.virtual_memory()[3] # resource usage in bytes + mem = mem / 1024**3 # resource usage in GB + + return mem + + +def log_total_memory_usage(): + """ + This function gets the current memory usage and logs it. + + Returns: + None + """ + mem = get_total_memory_usage() + + logger.debug(f"Current memory usage: {mem:.3f}GB") + + +def get_df_memory_usage(df): + """ + This function calculates the memory usage of a pandas dataframe and + logs it. + + Args: + df: The pandas dataframe to calculate the memory usage of. + + Returns: + The pandas dataframe memory usage in MB + """ + mem = df.memory_usage(deep=True).sum() / 1e6 + + return mem diff --git a/vast_pipeline/plots.py b/vast_pipeline/plots.py index c8248d0c9..9068a6390 100644 --- a/vast_pipeline/plots.py +++ b/vast_pipeline/plots.py @@ -1,23 +1,39 @@ """Contains plotting code used by the web server.""" +import colorcet as cc +import holoviews as hv +import networkx as nx +import numpy as np +import pandas as pd -from datetime import timedelta +from astropy.stats import sigma_clip, mad_std from bokeh.models import ( ColumnDataSource, + Span, + BoxAnnotation, CustomJS, DataRange1d, Range1d, Whisker, LabelSet, HoverTool, + TapTool, + Slider, + Button, + RadioButtonGroup, + Scatter, + WheelZoomTool, + ColorBar ) from bokeh.models.formatters import DatetimeTickFormatter -from bokeh.layouts import row, Row +from bokeh.layouts import row, Row, gridplot, Spacer, column from bokeh.plotting import figure -from bokeh.transform import factor_cmap +from bokeh.transform import factor_cmap, linear_cmap +from datetime import timedelta +from django.conf import settings from django.db.models import F -from django.db.models.functions import Abs -import pandas as pd -import networkx as nx +from holoviews.operation.datashader import datashade, spread +from scipy.stats import norm +from typing import Tuple from vast_pipeline.models import Measurement, Source @@ -32,7 +48,7 @@ def plot_lightcurve( Args: source (Source): Source object. - vs_abs_min (float, optional): MeasurementPair objects with an absolute vs metric + vs_abs_min (float, optional): pairs of Measurement objects with an absolute vs metric greater than `vs_abs_min` and m metric greater than `m_abs_min` will be connected in the metric graph. Defaults to 4.3. m_abs_min (float, optional): See `vs_abs_min`. Defaults to 0.26. @@ -62,23 +78,22 @@ def plot_lightcurve( "flux_err_upper", "flux_err_lower", "forced", + "name" ) .order_by("taustart_ts") ) - candidate_measurement_pairs_qs = ( - source.measurementpair_set.annotate( - m_abs=Abs(f"m_{metric_suffix}"), vs_abs=Abs(f"vs_{metric_suffix}") - ) - .filter(vs_abs__gte=vs_abs_min, m_abs__gte=m_abs_min) - .values("measurement_a_id", "measurement_b_id", "vs_abs", "m_abs") - ) - candidate_measurement_pairs_df = pd.DataFrame(candidate_measurement_pairs_qs) # lightcurve required cols: taustart_ts, flux, flux_err_upper, flux_err_lower, forced lightcurve = pd.DataFrame(measurements_qs) # remap method values to labels to make a better legend - lightcurve["method"] = lightcurve.forced.map({True: "Forced", False: "Selavy"}) - source = ColumnDataSource(lightcurve) + lightcurve["method"] = lightcurve.forced.map( + {True: "Forced", False: "Selavy"} + ) + lightcurve['cutout'] = lightcurve['id'].apply( + lambda x: f'/cutout/{x}/normal/?img_type=png' + ) + lc_source = ColumnDataSource(lightcurve) + method_mapper = factor_cmap( "method", palette="Colorblind3", factors=["Selavy", "Forced"] ) @@ -108,7 +123,7 @@ def plot_lightcurve( nonselection_alpha=1.0, hover_color="red", alpha=1.0, - source=source, + source=lc_source, legend_group="method", ) fig_lc.add_layout( @@ -116,7 +131,7 @@ def plot_lightcurve( base="taustart_ts", upper="flux_err_upper", lower="flux_err_lower", - source=source, + source=lc_source, ) ) fig_lc.xaxis.axis_label = "Datetime" @@ -145,7 +160,11 @@ def plot_lightcurve( sizing_mode="fixed", ) hover_tool_lc_callback = None - if len(candidate_measurement_pairs_df) > 0: + measurement_pairs = source.get_measurement_pairs() + if len(measurement_pairs) > 0: + candidate_measurement_pairs_df = pd.DataFrame(measurement_pairs).query( + f"m_{metric_suffix}.abs() >= {m_abs_min} and vs_{metric_suffix}.abs() >= {vs_abs_min}" + ).reset_index() g = nx.Graph() for _row in candidate_measurement_pairs_df.itertuples(index=False): g.add_edge(_row.measurement_a_id, _row.measurement_b_id) @@ -177,7 +196,7 @@ def plot_lightcurve( ) node_positions_df["lc_index"] = node_positions_df.index.map( {v: k for k, v in lightcurve.id.to_dict().items()} - ).values + ).values.astype(str) node_source = ColumnDataSource(node_positions_df) edge_source = ColumnDataSource(candidate_measurement_pairs_df) @@ -212,7 +231,10 @@ def plot_lightcurve( lightcurve_data.selected.indices = lightcurve_indices; """ hover_tool_edges = HoverTool( - tooltips=None, + tooltips=[ + (f"Vs {metric_suffix}", f"@vs_{metric_suffix}"), + (f"m {metric_suffix}", f"@m_{metric_suffix}"), + ], renderers=[edge_renderer], callback=CustomJS( args={ @@ -252,13 +274,37 @@ def plot_lightcurve( # create hover tool for lightcurve hover_tool_lc = HoverTool( - tooltips=[ - ("Index", "@index"), - ("Date", "@taustart_ts{%F}"), - (f"Flux {metric_suffix}", "@flux mJy"), - ], + # tooltips=[ + # ("Index", "@index"), + # ("Date", "@taustart_ts{%F}"), + # (f"Flux {metric_suffix}", "@flux mJy"), + # ('Cutout', "@cutout") + # ], + tooltips=""" +
    +
    + @cutout +
    +
    +
    Date:
    +
    @taustart_ts{%F}
    +
    +
    +
    Flux:
    +
    @flux mJy
    +
    +
    +
    Index:
    +
    @index
    +
    +
    + """, formatters={"@taustart_ts": "datetime", }, - mode="vline", + mode="mouse", callback=hover_tool_lc_callback, ) fig_lc.add_tools(hover_tool_lc) @@ -266,3 +312,378 @@ def plot_lightcurve( plot_row = row(fig_lc, fig_graph, sizing_mode="stretch_width") plot_row.css_classes.append("mx-auto") return plot_row + + +def fit_eta_v( + df: pd.DataFrame, use_peak_flux: bool = False +) -> Tuple[float, float, float, float]: + """ + Fits the eta and v distributions with Gaussians. Used from + within the 'run_eta_v_analysis' method. + + Args: + df: DataFrame containing the sources from the pipeline run. A + `pandas.core.frame.DataFrame` instance. + use_peak_flux: Use peak fluxes for the analysis instead of + integrated fluxes, defaults to 'False'. + + Returns: + The mean of the eta fit. + The sigma of the eta fit. + The mean of the v fit. + The sigma of the v fit. + """ + + if use_peak_flux: + eta_label = 'eta_peak' + v_label = 'v_peak' + else: + eta_label = 'eta_int' + v_label = 'v_int' + + eta_log = np.log10(df[eta_label]) + v_log = np.log10(df[v_label]) + + eta_log_clipped = sigma_clip( + eta_log, masked=False, stdfunc=mad_std, sigma=3 + ) + v_log_clipped = sigma_clip( + v_log, masked=False, stdfunc=mad_std, sigma=3 + ) + + eta_fit_mean, eta_fit_sigma = norm.fit(eta_log_clipped) + v_fit_mean, v_fit_sigma = norm.fit(v_log_clipped) + + return (eta_fit_mean, eta_fit_sigma, v_fit_mean, v_fit_sigma) + + +def plot_eta_v_bokeh( + source: Source, + eta_sigma: float, + v_sigma: float, + use_peak_flux: bool = True +) -> gridplot: + """ + Adapted from code written by Andrew O'Brien. + Produces the eta, V candidates plot + (see Rowlinson et al., 2018, + https://ui.adsabs.harvard.edu/abs/2019A%26C....27..111R/abstract). + Returns a bokeh version. + + Args: + source: The source model object containing the result of the query. + eta_sigma: The log10 eta_cutoff from the analysis. + v_sigma: The log10 v_cutoff from the analysis. + use_peak_flux: Use peak fluxes for the analysis instead of + integrated fluxes, defaults to 'True'. + + Returns: + Bokeh grid object containing figure. + """ + + df = pd.DataFrame(source.values( + "id", "name", "eta_peak", "eta_int", "v_peak", "v_int", "n_meas_sel" + )) + + ( + eta_fit_mean, eta_fit_sigma, + v_fit_mean, v_fit_sigma + ) = fit_eta_v(df, use_peak_flux=use_peak_flux) + + eta_cutoff_log10 = eta_fit_mean + eta_sigma * eta_fit_sigma + v_cutoff_log10 = v_fit_mean + v_sigma * v_fit_sigma + + eta_cutoff = 10 ** eta_cutoff_log10 + v_cutoff = 10 ** v_cutoff_log10 + + # generate fitted curve data for plotting + eta_x = np.linspace( + norm.ppf(0.001, loc=eta_fit_mean, scale=eta_fit_sigma), + norm.ppf(0.999, loc=eta_fit_mean, scale=eta_fit_sigma), + ) + eta_y = norm.pdf(eta_x, loc=eta_fit_mean, scale=eta_fit_sigma) + + v_x = np.linspace( + norm.ppf(0.001, loc=v_fit_mean, scale=v_fit_sigma), + norm.ppf(0.999, loc=v_fit_mean, scale=v_fit_sigma), + ) + v_y = norm.pdf(v_x, loc=v_fit_mean, scale=v_fit_sigma) + + if use_peak_flux: + x_label = 'eta_peak' + y_label = 'v_peak' + title = 'Peak Flux' + else: + x_label = 'eta_int' + y_label = 'v_int' + title = "Int. Flux" + + # PLOTTING NOTE! + # Datashader does not play nice with setting the axis to log-log, in fact + # it just doesn't work as of writing. + # See https://github.com/holoviz/holoviews/issues/2195. + # So this is why the actual log10 values are plotted instead on a linear + # axis. This could be revisited if the probelm with datashader and + # holoviews is resolved. + for i in [x_label, y_label]: + df[f"{i}_log10"] = np.log10(df[i]) + + PLOT_WIDTH = 700 + PLOT_HEIGHT = PLOT_WIDTH + + x_axis_label = "log \u03B7" + y_axis_label = "log V" + + cmap = linear_cmap( + "n_meas_sel", + cc.kb, + df["n_meas_sel"].min(), + df["n_meas_sel"].max(), + ) + + cb_title = "Number of Selavy Measurements" + + if df.shape[0] > settings.ETA_V_DATASHADER_THRESHOLD: + + hv.extension('bokeh') + + # create dfs for bokeh and datashader + mask = ((df[x_label] >= eta_cutoff) & (df[y_label] >= v_cutoff)) + + bokeh_df = df.loc[mask] + ds_df = df.loc[~mask] + + # create datashader version first + points = spread( + datashade( + hv.Points(ds_df[[f"{x_label}_log10", f"{y_label}_log10"]]), + cmap="Blues" + ), + px=1, + shape='square' + ).opts(height=PLOT_HEIGHT, width=PLOT_WIDTH) + + fig = hv.render(points) + + fig.xaxis.axis_label = x_axis_label + fig.yaxis.axis_label = y_axis_label + fig.aspect_scale = 1 + fig.sizing_mode = 'stretch_width' + fig.output_backend = "webgl" + # update the y axis default range + if bokeh_df.shape[0] > 0: + fig.y_range.end = bokeh_df[f'{y_label}_log10'].max() + 0.2 + + cb_title += " (interactive points only)" + else: + bokeh_df = df + + fig = figure( + output_backend="webgl", + plot_width=PLOT_WIDTH, + plot_height=PLOT_HEIGHT, + aspect_scale=1, + x_axis_label=x_axis_label, + y_axis_label=y_axis_label, + sizing_mode="stretch_width", + ) + + # activate scroll wheel zoom by default + fig.toolbar.active_scroll = fig.select_one(WheelZoomTool) + + source = ColumnDataSource(data=bokeh_df) + + bokeh_points = Scatter( + x=f"{x_label}_log10", + y=f"{y_label}_log10", + fill_color=cmap, + line_color=cmap, + marker="circle", + size=5 + ) + + bokeh_g1 = fig.add_glyph(source_or_glyph=source, glyph=bokeh_points) + + hover = HoverTool( + renderers=[bokeh_g1], + tooltips=[ + ("source", "@name"), + ("\u03B7", f"@{x_label}"), + ("V", f"@{y_label}"), + ("id", "@id") + ], + mode='mouse' + ) + + fig.add_tools(hover) + + color_bar = ColorBar( + color_mapper=cmap['transform'], + title=cb_title + ) + + fig.add_layout(color_bar, 'below') + + # axis histograms + # filter out any forced-phot points for these + x_hist = figure( + plot_width=PLOT_WIDTH, + plot_height=100, + x_range=fig.x_range, + y_axis_type=None, + x_axis_type="linear", + x_axis_location="above", + sizing_mode="stretch_width", + title="VAST eta-V {}".format(title), + tools="", + output_backend="webgl", + ) + x_hist_data, x_hist_edges = np.histogram( + df[f"{x_label}_log10"], density=True, bins=50, + ) + x_hist.quad( + top=x_hist_data, + bottom=0, + left=x_hist_edges[:-1], + right=x_hist_edges[1:], + ) + x_hist.line(eta_x, eta_y, color="black") + x_hist_sigma_span = Span( + location=eta_cutoff_log10, + dimension="height", + line_color="black", + line_dash="dashed", + ) + x_hist.add_layout(x_hist_sigma_span) + fig.add_layout(x_hist_sigma_span) + + y_hist = figure( + plot_height=PLOT_HEIGHT, + plot_width=100, + y_range=fig.y_range, + x_axis_type=None, + y_axis_type="linear", + y_axis_location="right", + sizing_mode="stretch_height", + tools="", + output_backend="webgl", + ) + y_hist_data, y_hist_edges = np.histogram( + (df[f"{y_label}_log10"]), density=True, bins=50, + ) + y_hist.quad( + right=y_hist_data, + left=0, + top=y_hist_edges[:-1], + bottom=y_hist_edges[1:], + ) + y_hist.line(v_y, v_x, color="black") + y_hist_sigma_span = Span( + location=v_cutoff_log10, + dimension="width", + line_color="black", + line_dash="dashed", + ) + y_hist.add_layout(y_hist_sigma_span) + fig.add_layout(y_hist_sigma_span) + + variable_region = BoxAnnotation( + left=eta_cutoff_log10, + bottom=v_cutoff_log10, + fill_color="orange", + fill_alpha=0.3, + level="underlay", + ) + fig.add_layout(variable_region) + + eta_slider = Slider( + start=0, + end=10, + step=0.1, + value=eta_sigma, + title="\u03B7 sigma value", + sizing_mode='stretch_width' + ) + v_slider = Slider( + start=0, + end=10, + step=0.1, + value=v_sigma, + title="V sigma value", + sizing_mode='stretch_width' + ) + + labels = ['Peak', 'Integrated'] + active = 0 if use_peak_flux else 1 + flux_choice_radio = RadioButtonGroup( + labels=labels, + active=active, + sizing_mode='stretch_width' + ) + + button = Button( + label="Apply", + button_type="primary", + sizing_mode='stretch_width' + ) + button.js_on_click( + CustomJS( + args=dict( + eta_slider=eta_slider, + v_slider=v_slider, + button=button, + flux_choice_radio=flux_choice_radio + ), + code=""" + button.label = "Loading..." + var e = eta_slider.value; + var v = v_slider.value; + const peak = ["peak", "int"]; + var fluxType = peak[flux_choice_radio.active]; + getEtaVPlot(e, v, fluxType); + """ + ) + ) + + grid = gridplot( + [ + [x_hist, Spacer(width=100, height=100)], + [fig, y_hist], + ] + ) + + plot_column = column( + grid, + flux_choice_radio, + eta_slider, + v_slider, + button, + sizing_mode='stretch_width' + ) + + plot_column.css_classes.append("mx-auto") + + source = ColumnDataSource(data=bokeh_df) + callback = CustomJS( + args=dict(source=source, flux_choice_radio=flux_choice_radio), + code=""" + const d1 = source.data; + const i = cb_data.source.selected.indices[0]; + const id = d1['id'][i]; + const peak = ["peak", "int"]; + var fluxType = peak[flux_choice_radio.active]; + + $(document).ready(function () { + update_card(id); + getLightcurvePlot(id, fluxType); + }); + """ + ) + + tap = TapTool(callback=callback, renderers=[bokeh_g1]) + fig.tools.append(tap) + + plot_row = row(plot_column, sizing_mode="stretch_width") + plot_row.css_classes.append("mx-auto") + + return plot_row diff --git a/vast_pipeline/serializers.py b/vast_pipeline/serializers.py index f2af6d874..0670fd221 100644 --- a/vast_pipeline/serializers.py +++ b/vast_pipeline/serializers.py @@ -193,7 +193,7 @@ class ExternalSearchSerializer(serializers.Serializer): help_text="Result origin database, e.g. SIMBAD or NED." ) separation_arcsec = serializers.FloatField() - otype = serializers.CharField(help_text="Object type, e.g. QSO.") + otype = serializers.CharField(allow_blank=True, help_text="Object type, e.g. QSO.") otype_long = serializers.CharField( allow_blank=True, help_text="Longer form of object type, e.g. quasar. Only supplied for SIMBAD results.", diff --git a/vast_pipeline/signals.py b/vast_pipeline/signals.py new file mode 100644 index 000000000..e00912f85 --- /dev/null +++ b/vast_pipeline/signals.py @@ -0,0 +1,51 @@ +"""Functions that are executed upon receiving an application signal.""" +import logging +from typing import Type + +from django.db.models import Count +from django.db.models.signals import pre_delete +from django.dispatch import receiver +from vast_pipeline.models import Run, Image, SkyRegion + + +logger = logging.getLogger(__name__) + + +@receiver(pre_delete, sender=Run, dispatch_uid="delete_orphans_for_run") +def delete_orphans_for_run(sender: Type[Run], instance: Run, using: str, **kwargs) -> None: + """Delete any Image and SkyRegion objects that would be orphaned by deleting the + given Run. Expects to recieve the arguments sent by the pre_delete signal. See + . + + Args: + sender: + Model class that sent the signal. + instance: + Model instance to be deleted. + using: + Database alias. + + Returns: + None + """ + image_orphans = ( + Image.objects.annotate(num_runs=Count("run")) + .filter(run=instance, num_runs=1) + ) + n_obj_deleted, deleted_dict = image_orphans.delete() + logger.info( + "Deleted %d objects: %s", + n_obj_deleted, + ", ".join([f"{model}: {n}" for model, n in deleted_dict.items()]), + ) + + skyreg_orphans = ( + SkyRegion.objects.annotate(num_runs=Count("run")) + .filter(run=instance, num_runs=1) + ) + n_obj_deleted, deleted_dict = skyreg_orphans.delete() + logger.info( + "Deleted %d objects: %s", + n_obj_deleted, + ", ".join([f"{model}: {n}" for model, n in deleted_dict.items()]), + ) diff --git a/vast_pipeline/tests/data/epoch-invalid.fits b/vast_pipeline/tests/data/epoch-invalid.fits new file mode 100644 index 000000000..d615dfde1 --- /dev/null +++ b/vast_pipeline/tests/data/epoch-invalid.fits @@ -0,0 +1,846 @@ +SIMPLE = T /Standard FITS BITPIX = -32 /Floating point (32 bit) NAXIS = 4 NAXIS1 = 256 NAXIS2 = 256 NAXIS3 = 1 NAXIS4 = 1 EXTEND = T BSCALE = 1.000000000000E+00 /PHYSICAL = PIXEL*BSCALE + BZERO BZERO = 0.000000000000E+00 BMAJ = 4.166666666667E-03 BMIN = 2.777777777778E-03 BPA = 0.000000000000E+00 BTYPE = 'Intensity' OBJECT = ' ' BUNIT = 'Jy/beam ' /Brightness (pixel) unit EQUINOX = 2.000000000000E+03 RADESYS = 'FK5 ' LONPOLE = 1.800000000000E+02 LATPOLE = -3.001611111111E+01 PC1_1 = 1.000000000000E+00 PC2_1 = 0.000000000000E+00 PC3_1 = 0.000000000000E+00 PC4_1 = 0.000000000000E+00 PC1_2 = 0.000000000000E+00 PC2_2 = 1.000000000000E+00 PC3_2 = 0.000000000000E+00 PC4_2 = 0.000000000000E+00 PC1_3 = 0.000000000000E+00 PC2_3 = 0.000000000000E+00 PC3_3 = 1.000000000000E+00 PC4_3 = 0.000000000000E+00 PC1_4 = 0.000000000000E+00 PC2_4 = 0.000000000000E+00 PC3_4 = 0.000000000000E+00 PC4_4 = 1.000000000000E+00 CTYPE1 = 'RA---SIN' CRVAL1 = 1.500208333333E+02 CDELT1 = -5.555555555556E-04 CRPIX1 = 1.290000000000E+02 CUNIT1 = 'deg ' CTYPE2 = 'DEC--SIN' CRVAL2 = -3.001611111111E+01 CDELT2 = 5.555555555556E-04 CRPIX2 = 1.290000000000E+02 CUNIT2 = 'deg ' CTYPE3 = 'FREQ ' CRVAL3 = 8.880000000000E+08 CDELT3 = 2.880000000000E+08 CRPIX3 = 1.000000000000E+00 CUNIT3 = 'Hz ' CTYPE4 = 'STOKES ' CRVAL4 = 1.000000000000E+00 CDELT4 = 1.000000000000E+00 CRPIX4 = 1.000000000000E+00 CUNIT4 = ' ' PV2_1 = 0.000000000000E+00 PV2_2 = 0.000000000000E+00 RESTFRQ = 1.420405751786E+09 /Rest Frequency (Hz) SPECSYS = 'LSRK ' /Spectral reference frame ALTRVAL = 1.123701652014E+08 /Alternate frequency reference value ALTRPIX = 1.000000000000E+00 /Alternate frequency reference pixel VELREF = 257 /1 LSR, 2 HEL, 3 OBS, +256 Radio COMMENT casacore non-standard usage: 4 LSD, 5 GEO, 6 SOU, 7 GAL TELESCOP= 'ASKAP ' OBSERVER= 'Karl Jansky' DATE-OBS= '2020-08-07T04:33:55.479' TIMESYS = 'UTC ' OBSGEO-X= 2.225142180269E+06 OBSGEO-Y= -5.440307370349E+06 OBSGEO-Z= -2.481029851874E+06 DATE = '2020-08-03T05:20:46.083137' /Date FITS file was written ORIGIN = 'CASA 5.6.2-2' HISTORY CASA START LOGTABLE HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::ImageFactory::createImage' HISTORY Created Paged image 'Gaussian.im' of shape [256, 256, 1, 1] with float vHISTORY >alued pixels. HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::fromshape' HISTORY Ran ia.fromshape HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::fromshape' HISTORY ia.fromshape(outfile="Gaussian.im", shape=[256,256,1,1], csys={...}, linHISTORY >ear=false, overwrite=true, log=true, type="f") HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::setcoordsys' HISTORY Ran ia.setcoordsys HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::setcoordsys' HISTORY ia.setcoordsys(csys={...}) HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::setbrightnessunit' HISTORY Ran ia.setbrightnessunit HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::setbrightnessunit' HISTORY ia.setbrightnessunit(unit="Jy/pixel") HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::modify' HISTORY Ran ia.modify HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::modify' HISTORY ia.modify(model={...}, region=[], mask=[], subtract=false, list=true, stHISTORY >retch=false) HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::addnoise' HISTORY Ran ia.addnoise HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::addnoise' HISTORY ia.addnoise(type="normal", pars=[0.000000,0.000000], region=[], zeroit=fHISTORY >alse, seeds=[]) HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::_convolve2d' HISTORY Ran ia.convolve2d HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::image::_convolve2d' HISTORY ia.convolve2d(outfile="sim_convolve.im", axes=[0,1], type="gaussian", maHISTORY >jor="15arcsec", minor="10arcsec", pa="0deg", scale=-1.000000, region=[]HISTORY > mask=[], overwrite=true, stretch=false, targetres=false, beam={...}) HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::Image2DConvolver::_logBeamInfo' HISTORY Original /Users/adam/testing/vast-tools-testing/pipeline-test-data/GaussHISTORY >ian.im has no beam HISTORY 2020-08-03T05:20:46 INFO SRCCODE='::Image2DConvolver::_logBeamInfo' HISTORY Output sim_convolve.im resolution major: 15 arcsec, minor: 10 arcsec, paHISTORY >: 0 deg HISTORY CASA END LOGTABLE END :1ʮ:&9ޣ8t:6:u։:P::::^n:[Q:ۼ9~98b9: :X'90R9h9Rl9~9s9928\Թ +䜹 +uܹSW8G59c+99*99 :9F:8:vn::^:|::::G:0:::1:&:G:o:UE9Iꔺuٺbͨ6H:U:L::9N*Y^W72.9R[,949ҸhDh*>P÷ gKǺh Vغ.谺u_ifH)ںU 8"9%9̭9 9k99M:9kS:{: :k;;`:U::0U9 7Hbv`U/9W9i9@9}bJ;ARɺSXP)@x89q:2:4^:#<:4::99/89w:!2p:::-:V:97/۸nXaZ_&Oc}w}H!4@}Zej幓E|˩589i&9:>:: ,: +:.9U99.U8ڐ8΢v9xD9w8ضlOQvƺJ-|+pYd̡W-it! ߹ʧ9b%w99b1Ǻ .EreJ9 :A +:vj:bY:99 }s9]: ;6:G8:@Õ9 W3޺ hjYF" 첹};|3ҺȌ nU[UPQsj678Ά}e{B=Ŭ S,cvSl/3e }躧e[ƺz;!9.:O:P::1:λ:T:'v:g':#9_9߫ :g:::9l9լ9ײp9զv99]a7FB 3B')@i<y8%9i^9,9:9-p99@::v_:cZ::(p:f-:`:[:UY:#:::::p:a94>/<1MI:n +x:eP:'#9(x;~Z8 9J68sH8qL@ߴ&bTQGy +-b-fS9/qKۺ Ib8 9؉9 99J9E9BJ 9|}:h::;D; 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b/vast_pipeline/tests/data/epoch-invalid.noiseMap.fits new file mode 100644 index 000000000..b504a5d7e --- /dev/null +++ b/vast_pipeline/tests/data/epoch-invalid.noiseMap.fits @@ -0,0 +1,201 @@ +SIMPLE = T /Standard FITS BITPIX = -32 /Floating point (32 bit) NAXIS = 4 NAXIS1 = 256 NAXIS2 = 256 NAXIS3 = 1 NAXIS4 = 1 BSCALE = 1.000000000000E+00 /PHYSICAL = PIXEL*BSCALE + BZERO BZERO = 0.000000000000E+00 BUNIT = 'Jy/beam ' / Brightness (pixel) unit EQUINOX = 2.000000000000E+03 RADESYS = 'FK5 ' LONPOLE = 1.800000000000E+02 LATPOLE = -3.001611111111E+01 PC01_01 = 1.000000000000E+00 PC02_01 = 0.000000000000E+00 PC03_01 = 0.000000000000E+00 PC04_01 = 0.000000000000E+00 PC01_02 = 0.000000000000E+00 PC02_02 = 1.000000000000E+00 PC03_02 = 0.000000000000E+00 PC04_02 = 0.000000000000E+00 PC01_03 = 0.000000000000E+00 PC02_03 = 0.000000000000E+00 PC03_03 = 1.000000000000E+00 PC04_03 = 0.000000000000E+00 PC01_04 = 0.000000000000E+00 PC02_04 = 0.000000000000E+00 PC03_04 = 0.000000000000E+00 PC04_04 = 1.000000000000E+00 CTYPE1 = 'RA---SIN' CRVAL1 = 1.500208333333E+02 CDELT1 = -5.555555555556E-04 CRPIX1 = 1.290000000000E+02 CUNIT1 = 'deg ' CTYPE2 = 'DEC--SIN' CRVAL2 = -3.001611111111E+01 CDELT2 = 5.555555555556E-04 CRPIX2 = 1.290000000000E+02 CUNIT2 = 'deg ' CTYPE3 = 'FREQ ' CRVAL3 = 8.880000000000E+08 CDELT3 = 2.880000000000E+08 CRPIX3 = 1.000000000000E+00 CUNIT3 = 'Hz ' CTYPE4 = 'STOKES ' CRVAL4 = 1.000000000000E+00 CDELT4 = 1.000000000000E+00 CRPIX4 = 1.000000000000E+00 CUNIT4 = ' ' PV2_1 = 0.000000000000E+00 PV2_2 = 0.000000000000E+00 RESTFRQ = 1.420405751786E+09 /Rest Frequency (Hz) SPECSYS = 'LSRK ' /Spectral reference frame ALTRVAL = 1.797423749789E+08 /Alternate frequency reference value ALTRPIX = 1.000000000000E+00 /Alternate frequency reference pixel VELREF = 1 /1 LSR, 2 HEL, 3 OBS, +256 Radio COMMENT casacore non-standard usage: 4 LSD, 5 GEO, 6 SOU, 7 GAL TELESCOP= 'ALMA ' OBSERVER= 'Karl Jansky' DATE-OBS= '2020-08-07T04:33:55.479' TIMESYS = 'UTC ' OBSGEO-X= 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+#island_idcomponent_idcomponent_namera_hms_contdec_dms_contra_deg_contdec_deg_contra_errdec_errfreqflux_peakflux_peak_errflux_intflux_int_errmaj_axismin_axispos_angmaj_axis_errmin_axis_errpos_ang_errmaj_axis_deconvmin_axis_deconvpos_ang_deconvmaj_axis_deconv_errmin_axis_deconv_errpos_ang_deconv_errchi_squared_fitrms_fit_gaussspectral_indexspectral_curvaturespectral_index_errspectral_curvature_errrms_imagehas_siblingsfit_is_estimatespectral_index_from_TTflag_c4comment +#----[deg][deg][arcsec][arcsec][MHz][mJy/beam][mJy/beam][mJy][mJy][arcsec][arcsec][deg][arcsec][arcsec][deg][arcsec][arcsec][deg][arcsec][arcsec][deg]--[mJy/beam]--------[mJy/beam] diff --git a/vast_pipeline/tests/data/epoch01.selavy.components.csv b/vast_pipeline/tests/data/epoch01.selavy.components.csv new file mode 100644 index 000000000..3b463b6ee --- /dev/null +++ b/vast_pipeline/tests/data/epoch01.selavy.components.csv @@ -0,0 +1,6 @@ +id,catalogue_id,first_sbid,other_sbids,project_id,island_id,component_id,component_name,ra_hms_cont,dec_dms_cont,ra_deg_cont,dec_deg_cont,ra_err,dec_err,freq,flux_peak,flux_peak_err,flux_int,flux_int_err,maj_axis,min_axis,pos_ang,maj_axis_err,min_axis_err,pos_ang_err,maj_axis_deconv,min_axis_deconv,maj_axis_deconv_err,pos_ang_deconv,min_axis_deconv_err,pos_ang_deconv_err,chi_squared_fit,rms_fit_gauss,spectral_index,spectral_index_err,spectral_curvature,spectral_curvature_err,rms_image,has_siblings,fit_is_estimate,spectral_index_from_tt,flag_c4,comment,quality_level,released_date +10596726,340,11711,,18,SB11711_island_9,SB11711_component_9b,J222253-452721,22:22:53.3,-45:27:21,335.722276,-45.456022,0.05,0.03,887.5,110.481,0.641,116.161,0.822,12.42,11.4,0.47,0.05,0.02,2.39,3.83,0.0,0.14,-5.23,0.0,1.07,1610.928,2590.791,-0.57,0.0,-99.0,0.0,0.377,1,0,,0,,NOT_VALIDATED,2020-11-18T08:43:42.979Z +10596727,340,11711,,18,island_1,component_1a,J095956-295840,09:59:56.0,-29:58:40,149.983325,-29.977778,0.00,0.00,179.7,2002.257017,0.170448,2001.231634,0.381031,15.00,10.00, 0.04,0.00,0.00,0.01,0.14,0.00, 51.24,3202.82, 0.00, 6.97, 95.309319,586.580753,-99.00,-99.00,0.00,0.00,0.936440,0,0,1,0 +10596728,340,11711,,18,island_2,component_2a,J100004-300057,10:00:04.9,-30:00:57,150.020532,-30.016111,0.00,0.00,179.7,2000.762424,0.179811,1999.266155,0.401901,15.00, 9.99,179.99,0.00,0.00,0.01,0.00,0.00,-52.02, 0.00, 0.00,59.22,102.390732,613.544226,-99.00,-99.00,0.00,0.00,0.944152,0,0,1,0 +10596729,340,11711,,18,island_3,component_3a,J095956-300057,09:59:56.0,-30:00:57,149.983319,-30.016111,0.00,0.00,179.7,1998.241478,0.297340,2000.053047,0.665245,15.01,10.00, 0.03,0.00,0.00,0.01,0.62,0.04, 10.30, 140.66,30513.15, 4.90,275.405090,972.835660,-99.00,-99.00,0.00,0.00,0.986237,0,0,1,0 +10596730,340,11711,,18,island_4,component_4a,J100000-300000,10:00:00.0,-30:00:00,150.000000,-29.999999,0.00,0.00,179.7, 998.430347,0.194839, 994.944043,0.434854,14.99, 9.97,179.98,0.00,0.00,0.02,0.00,0.00,-17.40, 0.00, 0.00,14.48, 79.812363,586.531162,-99.00,-99.00,0.00,0.00,1.066238,0,0,1,0 diff --git a/vast_pipeline/tests/data/epoch01.selavy.components.xml b/vast_pipeline/tests/data/epoch01.selavy.components.xml new file mode 100644 index 000000000..d91c174ca --- /dev/null +++ b/vast_pipeline/tests/data/epoch01.selavy.components.xml @@ -0,0 +1,91 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    island_1 component_1a J095956-295840 09:59:56.0 -29:58:40 149.983325 -29.977778 0.00 0.00 179.7 2002.257017 0.170448 2001.231634 0.381031 15.00 10.00 0.04 0.00 0.00 0.01 0.14 0.00 51.24 3202.82 0.00 6.97 95.309319 586.580753 -99.00 -99.00 0.00 0.00 0.936440 0 0 1 0
    island_2 component_2a J100004-300057 10:00:04.9 -30:00:57 150.020532 -30.016111 0.00 0.00 179.7 2000.762424 0.179811 1999.266155 0.401901 15.00 9.99 179.99 0.00 0.00 0.01 0.00 0.00 -52.02 0.00 0.00 59.22 102.390732 613.544226 -99.00 -99.00 0.00 0.00 0.944152 0 0 1 0
    island_3 component_3a J095956-300057 09:59:56.0 -30:00:57 149.983319 -30.016111 0.00 0.00 179.7 1998.241478 0.297340 2000.053047 0.665245 15.01 10.00 0.03 0.00 0.00 0.01 0.62 0.04 10.30 140.66 30513.15 4.90 275.405090 972.835660 -99.00 -99.00 0.00 0.00 0.986237 0 0 1 0
    island_4 component_4a J100000-300000 10:00:00.0 -30:00:00 150.000000 -29.999999 0.00 0.00 179.7 998.430347 0.194839 994.944043 0.434854 14.99 9.97 179.98 0.00 0.00 0.02 0.00 0.00 -17.40 0.00 0.00 14.48 79.812363 586.531162 -99.00 -99.00 0.00 0.00 1.066238 0 0 1 0
    +     +     diff --git a/vast_pipeline/tests/pipeline-runs/basic-association-invalid-catalog/config.yaml b/vast_pipeline/tests/pipeline-runs/basic-association-invalid-catalog/config.yaml new file mode 100644 index 000000000..c0a05fb4c --- /dev/null +++ b/vast_pipeline/tests/pipeline-runs/basic-association-invalid-catalog/config.yaml @@ -0,0 +1,44 @@ +run: + path: vast_pipeline/tests/pipeline-runs/basic-association-invalid-catalog + suppress_astropy_warnings: true +inputs: + image: + - vast_pipeline/tests/data/epoch01.fits + - vast_pipeline/tests/data/epoch02.fits + - vast_pipeline/tests/data/epoch03.fits + - vast_pipeline/tests/data/epoch04.fits + - vast_pipeline/tests/data/epoch-invalid.fits + selavy: + - vast_pipeline/tests/data/epoch01.selavy.components.txt + - vast_pipeline/tests/data/epoch02.selavy.components.txt + - vast_pipeline/tests/data/epoch03.selavy.components.txt + - vast_pipeline/tests/data/epoch04.selavy.components.txt + - vast_pipeline/tests/data/epoch-invalid.selavy.components.txt + noise: + - vast_pipeline/tests/data/epoch01.noiseMap.fits + - vast_pipeline/tests/data/epoch02.noiseMap.fits + - vast_pipeline/tests/data/epoch03.noiseMap.fits + - vast_pipeline/tests/data/epoch04.noiseMap.fits + - vast_pipeline/tests/data/epoch-invalid.noiseMap.fits + background: + - vast_pipeline/tests/data/epoch01.meanMap.fits + - vast_pipeline/tests/data/epoch02.meanMap.fits + - vast_pipeline/tests/data/epoch03.meanMap.fits + - vast_pipeline/tests/data/epoch04.meanMap.fits + - vast_pipeline/tests/data/epoch-invalid.meanMap.fits +source_monitoring: + monitor: false +source_association: + method: basic + radius: 10.0 +new_sources: + min_sigma: 5.0 +measurements: + source_finder: selavy + flux_fractional_error: 0.0 + condon_errors: true + selavy_local_rms_fill_value: 0.2 + ra_uncertainty: 1 + dec_uncertainty: 1 +variability: + source_aggregate_pair_metrics_min_abs_vs: 4.3 diff --git a/vast_pipeline/tests/pipeline-runs/basic-association-no-pairs/config.yaml b/vast_pipeline/tests/pipeline-runs/basic-association-no-pairs/config.yaml new file mode 100644 index 000000000..96af3c4c7 --- /dev/null +++ b/vast_pipeline/tests/pipeline-runs/basic-association-no-pairs/config.yaml @@ -0,0 +1,41 @@ +run: + path: vast_pipeline/tests/pipeline-runs/basic-association-no-pairs + suppress_astropy_warnings: true +inputs: + image: + - vast_pipeline/tests/data/epoch01.fits + - vast_pipeline/tests/data/epoch02.fits + - vast_pipeline/tests/data/epoch03.fits + - vast_pipeline/tests/data/epoch04.fits + selavy: + - vast_pipeline/tests/data/epoch01.selavy.components.txt + - vast_pipeline/tests/data/epoch02.selavy.components.txt + - vast_pipeline/tests/data/epoch03.selavy.components.txt + - vast_pipeline/tests/data/epoch04.selavy.components.txt + noise: + - vast_pipeline/tests/data/epoch01.noiseMap.fits + - vast_pipeline/tests/data/epoch02.noiseMap.fits + - vast_pipeline/tests/data/epoch03.noiseMap.fits + - vast_pipeline/tests/data/epoch04.noiseMap.fits + background: + - vast_pipeline/tests/data/epoch01.meanMap.fits + - vast_pipeline/tests/data/epoch02.meanMap.fits + - vast_pipeline/tests/data/epoch03.meanMap.fits + - vast_pipeline/tests/data/epoch04.meanMap.fits +source_monitoring: + monitor: false +source_association: + method: basic + radius: 10.0 +new_sources: + min_sigma: 5.0 +measurements: + source_finder: selavy + flux_fractional_error: 0.0 + condon_errors: true + selavy_local_rms_fill_value: 0.2 + ra_uncertainty: 1 + dec_uncertainty: 1 +variability: + pair_metrics: false + source_aggregate_pair_metrics_min_abs_vs: 4.3 diff --git a/vast_pipeline/tests/regression-data/download.sh b/vast_pipeline/tests/regression-data/download.sh old mode 100644 new mode 100755 index 94b3e9593..f57e79b51 --- a/vast_pipeline/tests/regression-data/download.sh +++ b/vast_pipeline/tests/regression-data/download.sh @@ -10,13 +10,13 @@ echo TMPFILE=`mktemp` TMPDIR=`mktemp -d` -wget "https://cloudstor.aarnet.edu.au/plus/s/xjh0aRr1EGY6Bt3/download" -O ${TMPFILE} +wget "http://www.physics.usyd.edu.au/~ddob1600/vast-pipeline/pipeline-test-data.zip" -O ${TMPFILE} echo "Unzipping archive to ${TMPDIR} ..." unzip -q -d ${TMPDIR} ${TMPFILE} echo "Moving data to relevant directory..." -mv ${TMPDIR}/VAST_PIPELINE_TEST_DATASET/* $(pwd)/. +mv ${TMPDIR}/pipeline-test-data/* $(pwd)/. echo "Deleting temporary files..." rm -r ${TMPFILE} ${TMPDIR} diff --git a/vast_pipeline/tests/test_clearpiperun.py b/vast_pipeline/tests/test_clearpiperun.py new file mode 100644 index 000000000..6dc853bae --- /dev/null +++ b/vast_pipeline/tests/test_clearpiperun.py @@ -0,0 +1,132 @@ +import os +from typing import Dict + +from django.conf import settings as s +from django.test import TestCase, override_settings +from django.utils import timezone +from django.core.management import call_command + +from vast_pipeline.models import Run, Image, Band, SkyRegion + + +TEST_ROOT = os.path.join(s.BASE_DIR, "vast_pipeline", "tests") + + +@override_settings( + PIPELINE_WORKING_DIR=os.path.join(TEST_ROOT, "pipeline-runs"), +) +class ClearPipelineRunTest(TestCase): + band: Band + run_1: Run + run_2: Run + skyregs: Dict[str, SkyRegion] + images: Dict[str, Image] + + @classmethod + def setUpTestData(cls): + default_image_kwargs = dict( + measurements_path="", + polarisation="I", + path="", + datetime=timezone.now(), + jd=0.0, + ra=0.0, + dec=0.0, + fov_bmaj=0.0, + fov_bmin=0.0, + physical_bmaj=0.0, + physical_bmin=0.0, + radius_pixels=0.0, + beam_bmaj=0.0, + beam_bmin=0.0, + beam_bpa=0.0, + rms_median=0.0, + rms_min=0.0, + rms_max=0.0, + ) + default_skyreg_kwargs = dict( + centre_ra=0.0, + centre_dec=0.0, + width_ra=0.0, + width_dec=0.0, + xtr_radius=0.0, + x=0.0, + y=0.0, + z=0.0, + ) + # Create test objects: + # - 2 Runs + # - 3 SkyRegions + # - 4 Images + # run_1 contains 3 images: image_a1, image_b1, image_a2. + # run_2 contains 2 images: image_b1, image_c1. + # The letter suffix indicates the sky region, e.g. image_a1 is in sky region a. + + # create a band + cls.band = Band.objects.create(name="band", frequency=887, bandwidth=128) + # create runs + cls.run_1 = Run.objects.create(name="run_1", path="run_1") + cls.run_2 = Run.objects.create(name="run_2", path="run_2") + # create sky regions + cls.skyregs = { + k: SkyRegion.objects.create(**default_skyreg_kwargs) + for k in ("a", "b", "c") + } + # create some images + cls.images = { + f"{k}1": Image.objects.create( + band=cls.band, + skyreg=skyreg, + name=f"image_{k}1", + **default_image_kwargs, + ) + for k, skyreg in cls.skyregs.items() + } + cls.images["a2"] = Image.objects.create( + band=cls.band, + skyreg=cls.skyregs["a"], + name="image_a2", + **default_image_kwargs, + ) + cls.images["a1"].run.add(cls.run_1) + cls.images["a2"].run.add(cls.run_1) + cls.images["b1"].run.add(cls.run_1) + cls.images["b1"].run.add(cls.run_2) + cls.images["c1"].run.add(cls.run_2) + + cls.skyregs["a"].run.add(cls.run_1) + cls.skyregs["b"].run.add(cls.run_1) + cls.skyregs["b"].run.add(cls.run_2) + cls.skyregs["c"].run.add(cls.run_2) + + def test_data(self): + """Check that the object relationships exist as expected. + """ + self.assertEqual(Image.objects.filter(run=self.run_1).count(), 3) + self.assertEqual(Image.objects.filter(run=self.run_2).count(), 2) + + self.assertEqual(SkyRegion.objects.filter(run=self.run_1).count(), 2) + self.assertEqual(SkyRegion.objects.filter(run=self.run_2).count(), 2) + + def test_clearpiperun(self): + """Delete a run and check that the appropriate objects are also deleted. + """ + # delete run_1 + call_command("clearpiperun", self.run_1.name) + + # ensure the images and sky regions that were only used in run_1 are deleted + with self.assertRaises(Image.DoesNotExist): + _ = Image.objects.get(name="image_a1") + with self.assertRaises(Image.DoesNotExist): + _ = Image.objects.get(name="image_a2") + with self.assertRaises(SkyRegion.DoesNotExist): + _ = SkyRegion.objects.get(pk=self.skyregs["a"].pk) + # ensure the images and sky regions that were used in both run_1 and run_2 remain + _ = Image.objects.get(name="image_b1") + _ = SkyRegion.objects.get(pk=self.skyregs["b"].pk) + + # delete run_2 + call_command("clearpiperun", self.run_2.name) + # ensure no images nor sky regions remain + self.assertEqual(Image.objects.count(), 0) + self.assertEqual(SkyRegion.objects.count(), 0) diff --git a/vast_pipeline/tests/test_coord_strings.py b/vast_pipeline/tests/test_coord_strings.py new file mode 100644 index 000000000..c7087707f --- /dev/null +++ b/vast_pipeline/tests/test_coord_strings.py @@ -0,0 +1,280 @@ +from django.test import SimpleTestCase + +from astropy.coordinates import Longitude, Latitude + +from vast_pipeline.utils.utils import deg2hms, deg2dms + + +class ExternalQueryTest(SimpleTestCase): + def setUp(self): + self.precision_test_inputs = [ + { + "input": { + "class": Longitude, + "value": "23:16:09.951", + "unit": "hourangle", + }, + "tests": [ + { + "params": {"truncate": False, "precision": 2}, + "output": "23:16:09.95", + }, + { + "params": {"truncate": False, "precision": 1}, + "output": "23:16:10.0", + }, + { + "params": {"truncate": False, "precision": 0}, + "output": "23:16:10", + }, + { + "params": {"truncate": True, "precision": 2}, + "output": "23:16:09.95", + }, + { + "params": {"truncate": True, "precision": 1}, + "output": "23:16:09.9", + }, + { + "params": {"truncate": True, "precision": 0}, + "output": "23:16:09", + }, + ], + }, + { + "input": { + "class": Longitude, + "value": "02:01:59.999", + "unit": "hourangle", + }, + "tests": [ + { + "params": {"truncate": False, "precision": 2}, + "output": "02:02:00.00", + }, + { + "params": {"truncate": False, "precision": 1}, + "output": "02:02:00.0", + }, + { + "params": {"truncate": False, "precision": 0}, + "output": "02:02:00", + }, + { + "params": {"truncate": True, "precision": 2}, + "output": "02:01:59.99", + }, + { + "params": {"truncate": True, "precision": 1}, + "output": "02:01:59.9", + }, + { + "params": {"truncate": True, "precision": 0}, + "output": "02:01:59", + }, + ], + }, + { + "input": { + "class": Latitude, + "value": "-22:57:28.8905", + "unit": "deg", + }, + "tests": [ + { + "params": {"truncate": False, "precision": 3}, + "output": "-22:57:28.891", + }, + { + "params": {"truncate": False, "precision": 2}, + "output": "-22:57:28.89", + }, + { + "params": {"truncate": False, "precision": 1}, + "output": "-22:57:28.9", + }, + { + "params": {"truncate": False, "precision": 0}, + "output": "-22:57:29", + }, + { + "params": {"truncate": True, "precision": 3}, + "output": "-22:57:28.890", + }, + { + "params": {"truncate": True, "precision": 2}, + "output": "-22:57:28.89", + }, + { + "params": {"truncate": True, "precision": 1}, + "output": "-22:57:28.8", + }, + { + "params": {"truncate": True, "precision": 0}, + "output": "-22:57:28", + }, + ], + }, + { + "input": { + "class": Latitude, + "value": "75:32:28.9994", + "unit": "deg", + }, + "tests": [ + { + "params": {"truncate": False, "precision": 3}, + "output": "+75:32:28.999", + }, + { + "params": {"truncate": False, "precision": 2}, + "output": "+75:32:29.00", + }, + { + "params": {"truncate": False, "precision": 1}, + "output": "+75:32:29.0", + }, + { + "params": {"truncate": False, "precision": 0}, + "output": "+75:32:29", + }, + { + "params": {"truncate": True, "precision": 3}, + "output": "+75:32:28.999", + }, + { + "params": {"truncate": True, "precision": 2}, + "output": "+75:32:28.99", + }, + { + "params": {"truncate": True, "precision": 1}, + "output": "+75:32:28.9", + }, + { + "params": {"truncate": True, "precision": 0}, + "output": "+75:32:28", + }, + ], + }, + { + "input": { + "class": Latitude, + "value": "75:32:28.5", + "unit": "deg", + }, + "tests": [ + { + "params": {"truncate": False, "precision": 3}, + "output": "+75:32:28.500", + }, + { + "params": {"truncate": False, "precision": 2}, + "output": "+75:32:28.50", + }, + { + "params": {"truncate": False, "precision": 1}, + "output": "+75:32:28.5", + }, + { + "params": {"truncate": False, "precision": 0}, + "output": "+75:32:29", + }, + { + "params": {"truncate": True, "precision": 3}, + "output": "+75:32:28.500", + }, + { + "params": {"truncate": True, "precision": 2}, + "output": "+75:32:28.50", + }, + { + "params": {"truncate": True, "precision": 1}, + "output": "+75:32:28.5", + }, + { + "params": {"truncate": True, "precision": 0}, + "output": "+75:32:28", + }, + ], + }, + { + "input": { + "class": Latitude, + "value": "75:32:28", + "unit": "deg", + }, + "tests": [ + { + "params": {"truncate": False, "precision": 3}, + "output": "+75:32:28.000", + }, + { + "params": {"truncate": False, "precision": 2}, + "output": "+75:32:28.00", + }, + { + "params": {"truncate": False, "precision": 1}, + "output": "+75:32:28.0", + }, + { + "params": {"truncate": False, "precision": 0}, + "output": "+75:32:28", + }, + { + "params": {"truncate": True, "precision": 3}, + "output": "+75:32:28.000", + }, + { + "params": {"truncate": True, "precision": 2}, + "output": "+75:32:28.00", + }, + { + "params": {"truncate": True, "precision": 1}, + "output": "+75:32:28.0", + }, + { + "params": {"truncate": True, "precision": 0}, + "output": "+75:32:28", + }, + ], + }, + ] + + def test_string_precision(self): + """Test various precision roundings.""" + for test_input in self.precision_test_inputs: + test_func = ( + deg2hms if test_input["input"]["class"] == Longitude else deg2dms + ) + for test in test_input["tests"]: + with self.subTest( + input=test_input["input"]["value"], + angle_class=test_input["input"]["class"], + precision=test["params"]["precision"], + truncate=test["params"]["truncate"], + ): + test_input_obj = test_input["input"]["class"]( + test_input["input"]["value"], unit=test_input["input"]["unit"] + ) + test_output = test_func(test_input_obj.deg, **test["params"]) + self.assertEqual(test_output, test["output"]) + + def test_ra_string_separators(self): + longitude = Longitude("23:16:09.951", unit="hourangle") + hms_string = deg2hms( + longitude.deg, hms_format=False, precision=2, truncate=False + ) + self.assertEqual(hms_string, "23:16:09.95") + hms_string = deg2hms( + longitude.deg, hms_format=True, precision=2, truncate=False + ) + self.assertEqual(hms_string, "23h16m09.95s") + + def test_dec_string_separators(self): + latitude = Latitude("-22:57:28.8905", unit="deg") + dms_string = deg2dms( + latitude.deg, dms_format=False, precision=2, truncate=False + ) + self.assertEqual(dms_string, "-22:57:28.89") + dms_string = deg2dms(latitude.deg, dms_format=True, precision=2, truncate=False) + self.assertEqual(dms_string, "-22d57m28.89s") diff --git a/vast_pipeline/tests/test_external_query.py b/vast_pipeline/tests/test_external_query.py new file mode 100644 index 000000000..843f3c072 --- /dev/null +++ b/vast_pipeline/tests/test_external_query.py @@ -0,0 +1,35 @@ +from unittest import skipIf + +from astropy.coordinates import SkyCoord, Angle +from django.conf import settings +from django.test import SimpleTestCase + +from vast_pipeline.utils import external_query + + +class ExternalQueryTest(SimpleTestCase): + def setUp(self): + # coordinates for SN 2018cow + self.coord = SkyCoord( + ra="16:16:00.220", dec="22:16:04.91", unit="hourangle,deg" + ) + self.radius = Angle("1arcmin") + + def test_simbad(self): + simbad_results = external_query.simbad(self.coord, self.radius) + self.assertGreaterEqual(len(simbad_results), 2) + self.assertEqual(simbad_results[0]["object_name"], "SN 2018cow") + + def test_ned(self): + ned_results = external_query.ned(self.coord, self.radius) + self.assertGreaterEqual(len(ned_results), 39) + self.assertEqual(ned_results[0]["object_name"], "SN 2018cow") + + @skipIf( + settings.TNS_API_KEY is None or settings.TNS_USER_AGENT is None, + "TNS_API_KEY or TNS_USER_AGENT not defined in settings.", + ) + def test_tns(self): + tns_results = external_query.tns(self.coord, self.radius) + self.assertGreaterEqual(len(tns_results), 1) + self.assertEqual(tns_results[0]["object_name"], "2018cow") diff --git a/vast_pipeline/tests/test_pipeline/data/skyc1_srcs_in.csv b/vast_pipeline/tests/test_pipeline/data/skyc1_srcs_in.csv index 868a1d272..09f25456f 100644 --- a/vast_pipeline/tests/test_pipeline/data/skyc1_srcs_in.csv +++ b/vast_pipeline/tests/test_pipeline/data/skyc1_srcs_in.csv @@ -1,3 +1,3 @@ -id,ra,dec,uncertainty_ew,uncertainty_ns,source,d2d,related,epoch -1,149.983325,-29.977778,0,2,1,0,,1 -2,150.020532,-30.016111,1,3,2,0,,1 +id,ra,dec,uncertainty_ew,uncertainty_ns,source,d2d,dr,related,epoch +1,149.983325,-29.977778,0,2,1,0,0,,1 +2,150.020532,-30.016111,1,3,2,0,0,,1 diff --git a/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_in.csv b/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_in.csv index ca0c16eaf..4eacc7baa 100644 --- a/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_in.csv +++ b/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_in.csv @@ -1,4 +1,4 @@ -id,ra,dec,uncertainty_ew,uncertainty_ns,source,d2d,related,epoch -1,149.983324,-29.977779,0.1,1.1,-1,0,,2 -2,149.983318,-30.016111,0.2,1.2,-1,0,,2 -3,150.020523,-30.016108,0.3,1.3,-1,0,,2 +id,ra,dec,uncertainty_ew,uncertainty_ns,source,d2d,dr,related,epoch +1,149.983324,-29.977779,0.1,1.1,-1,0,0,,2 +2,149.983318,-30.016111,0.2,1.2,-1,0,0,,2 +3,150.020523,-30.016108,0.3,1.3,-1,0,0,,2 diff --git a/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_no_new.csv b/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_no_new.csv index 34db3b1f3..82b76c5f3 100644 --- a/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_no_new.csv +++ b/vast_pipeline/tests/test_pipeline/data/skyc2_srcs_no_new.csv @@ -1,3 +1,3 @@ -id,ra,dec,source,d2d,related,epoch -1,149.983325,-29.977778,1,0,,2 -2,150.020532,-30.016111,2,0,,2 +id,ra,dec,source,d2d,dr,related,epoch +1,149.983325,-29.977778,1,0,0,,2 +2,150.020532,-30.016111,2,0,0,,2 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_drop.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_drop.csv index e9354b6d6..a6abcdfbb 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_drop.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_drop.csv @@ -1,6 +1,6 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,,1,1,1,1,1 -1,1,2,2,,1,1,1,1,2 -1,1,3,1,,3,3,1,1,3 -1,1,7,1,,1,3,1,1,3 -1,1,8,1,,2,2,1,1,3 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,,1,1,1,1,1 +1,1,2,,1,1,1,1,2 +1,1,3,,3,3,1,1,3 +1,1,7,,1,3,1,1,3 +1,1,8,,2,2,1,1,3 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_out.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_out.csv index 1bdf20f62..8c1b1bffb 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_out.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_advanced_out.csv @@ -1,9 +1,9 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,,1,1,1,1,1 -1,1,4,1,[2],1,1,1,2,1 -1,1,2,2,[4],1,1,1,1,2 -1,1,5,1,[3],3,1,1,2,1 -1,1,6,1,[3],3,2,1,3,1 -1,1,3,1,"[5, 6]",3,3,1,1,3 -1,1,7,1,,1,3,1,1,3 -1,1,8,1,,2,2,1,1,3 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,,1,1,1,1,1 +1,1,4,[2],1,1,1,2,1 +1,1,2,[4],1,1,1,1,2 +1,1,5,[3],3,1,1,2,1 +1,1,6,[3],3,2,1,3,1 +1,1,3,"[5, 6]",3,3,1,1,3 +1,1,7,,1,3,1,1,3 +1,1,8,,2,2,1,1,3 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_drop.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_drop.csv index 3b9700ad5..533f7e45d 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_drop.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_drop.csv @@ -1,7 +1,7 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,,1,1,1,1,1 -1,1,2,1,,1,1,1,2,1 -1,1,3,1,,3,1,1,2,1 -1,1,3,1,,3,2,1,3,1 -1,1,7,1,,1,3,1,1,3 -1,1,8,1,,2,2,1,1,3 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,,1,1,1,1,1 +1,1,2,,1,1,1,2,1 +1,1,3,,3,1,1,2,1 +1,1,3,,3,2,1,3,1 +1,1,7,,1,3,1,1,3 +1,1,8,,2,2,1,1,3 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_out.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_out.csv index 356af3d09..97ca39a70 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_out.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_deruiter_out.csv @@ -1,9 +1,9 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,,1,1,1,1,1 -1,1,2,1,[4],1,1,1,2,1 -1,1,4,2,[2],1,1,1,1,2 -1,1,3,1,"[5, 6]",3,1,1,2,1 -1,1,5,1,[3],3,2,1,3,1 -1,1,6,1,[3],3,3,1,1,3 -1,1,7,1,,1,3,1,1,3 -1,1,8,1,,2,2,1,1,3 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,,1,1,1,1,1 +1,1,2,[4],1,1,1,2,1 +1,1,4,[2],1,1,1,1,2 +1,1,3,"[5, 6]",3,1,1,2,1 +1,1,5,[3],3,2,1,3,1 +1,1,6,[3],3,3,1,1,3 +1,1,7,,1,3,1,1,3 +1,1,8,,2,2,1,1,3 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_dup.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_dup.csv index 06c545673..36f35109f 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_dup.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_dup.csv @@ -1,9 +1,9 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,,1,1,1,1,1 -1,1,2,1,,1,1,1,2,1 -1,1,2,2,,1,1,1,1,2 -1,1,3,1,,3,1,1,2,1 -1,1,3,1,,3,2,1,3,1 -1,1,3,1,,3,3,1,1,3 -1,1,7,1,,1,3,1,1,3 -1,1,8,1,,2,2,1,1,3 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,,1,1,1,1,1 +1,1,2,,1,1,1,2,1 +1,1,2,,1,1,1,1,2 +1,1,3,,3,1,1,2,1 +1,1,3,,3,2,1,3,1 +1,1,3,,3,3,1,1,3 +1,1,7,,1,3,1,1,3 +1,1,8,,2,2,1,1,3 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_ind_rel.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_ind_rel.csv index f1f35f3be..134081eda 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_ind_rel.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_ind_rel.csv @@ -1,9 +1,9 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,"[2, 7]",1,1,1,1,1 -1,1,2,1,"[1, 7]",1,1,1,2,1 -1,1,2,2,"[1, 7]",1,1,1,1,2 -1,1,3,1,[],3,1,1,2,1 -1,1,3,1,[],3,2,1,3,1 -1,1,3,1,[],3,3,1,1,3 -1,1,7,1,"[1, 2]",1,3,1,1,3 -1,1,8,1,,2,2,1,1,3 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,"[2, 7]",1,1,1,1,1 +1,1,2,"[1, 7]",1,1,1,2,1 +1,1,2,"[1, 7]",1,1,1,1,2 +1,1,3,[],3,1,1,2,1 +1,1,3,[],3,2,1,3,1 +1,1,3,[],3,3,1,1,3 +1,1,7,"[1, 2]",1,3,1,1,3 +1,1,8,,2,2,1,1,3 diff --git a/vast_pipeline/tests/test_pipeline/data/temp_srcs_nodup.csv b/vast_pipeline/tests/test_pipeline/data/temp_srcs_nodup.csv index d50bc5394..f84baa5a8 100644 --- a/vast_pipeline/tests/test_pipeline/data/temp_srcs_nodup.csv +++ b/vast_pipeline/tests/test_pipeline/data/temp_srcs_nodup.csv @@ -1,3 +1,3 @@ -index_old_skyc1,id_skyc1,source_skyc1,d2d_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d_skyc2,dr -1,1,1,1,,2,1,1,1,1 -1,1,2,1,,1,1,1,1,1 +index_old_skyc1,id_skyc1,source_skyc1,related_skyc1,index_old_skyc2,id_skyc2,source_skyc2,d2d,dr +1,1,1,,2,1,1,1,1 +1,1,2,,1,1,1,1,1 diff --git a/vast_pipeline/tests/test_pipeline/test_association.py b/vast_pipeline/tests/test_pipeline/test_association.py index c54ba30a7..f477e87c5 100644 --- a/vast_pipeline/tests/test_pipeline/test_association.py +++ b/vast_pipeline/tests/test_pipeline/test_association.py @@ -9,7 +9,7 @@ from django.test import SimpleTestCase from vast_pipeline.pipeline.association import ( - one_to_many_basic, + one_to_many_basic, one_to_many_advanced, many_to_many_advanced, many_to_one_advanced, @@ -33,7 +33,7 @@ def parse_or_nan(x): ---------- s : str String to parse. - + Returns ------- The parsed string, or NaN if it couldn't be parsed. @@ -46,7 +46,7 @@ def parse_or_nan(x): class OneToManyBasicTest(SimpleTestCase): ''' - Tests for one_to_many_basic in association.py + Tests for one_to_many_basic in association.py ''' @classmethod @@ -56,35 +56,35 @@ def setUpClass(self): ''' super().setUpClass() self.skyc2_srcs_nodup = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_nodup.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_nodup.csv'), header=0 ) self.skyc2_srcs_out = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_out.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_out.csv'), header=0, converters={'related': parse_or_nan} ) self.sources_df_in = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) self.sources_df_out = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_out.csv'), + os.path.join(DATA_PATH, 'sources_df_out.csv'), header=0 ) def test_duplicated_skyc2_empty(self): ''' - Test if one_to_many_basic will return the input dataframes unchanged + Test if one_to_many_basic will return the input dataframes unchanged when there are no duplicate sources in skyc2_srcs. Repeated values in - source are duplicates, ignoring -1 values. + source are duplicates, ignoring -1 values. ''' skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_nodup.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_nodup.csv'), header=0 ) sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) @@ -95,13 +95,13 @@ def test_duplicated_skyc2_empty(self): def test_duplicated_skyc2_nonempty(self): ''' - Test if one_to_many_basic correctly identifies duplicate sources and + Test if one_to_many_basic correctly identifies duplicate sources and relates them. - skyc2_srcs: all duplicate sources should have new unique source ids, + skyc2_srcs: all duplicate sources should have new unique source ids, the new ids are assigned in order of d2d - min d2d retains original id, - duplicate sources should have related sources listed using new source - ids, if >2 duplicates of 1 source then the sources with new ids will + duplicate sources should have related sources listed using new source + ids, if >2 duplicates of 1 source then the sources with new ids will be related to the old id whilst the old id will be related to all new ids, and other columns should remain unchanged. sources_df: must contain at least all source ids present in skyc2_src, @@ -113,7 +113,7 @@ def test_duplicated_skyc2_nonempty(self): header=0 ) sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) @@ -139,29 +139,29 @@ def setUpClass(self): header=0 ) self.temp_srcs_advanced_out = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_advanced_out.csv'), + os.path.join(DATA_PATH, 'temp_srcs_advanced_out.csv'), header=0, converters={'related_skyc1': parse_or_nan} ) self.temp_srcs_deruiter_out = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_deruiter_out.csv'), + os.path.join(DATA_PATH, 'temp_srcs_deruiter_out.csv'), header=0, converters={'related_skyc1': parse_or_nan} ) self.sources_df_in = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) self.sources_df_out = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_out.csv'), + os.path.join(DATA_PATH, 'sources_df_out.csv'), header=0 ) def test_duplicated_skyc1_empty(self): ''' Test if one_to_many_advanced will return the input dataframes unchanged - when there are no duplicate sources in temp_srcs. Repeated values in - source_skyc1 are duplicates. + when there are no duplicate sources in temp_srcs. Repeated values in + source_skyc1 are duplicates. ''' temp_srcs = pd.read_csv( @@ -169,13 +169,13 @@ def test_duplicated_skyc1_empty(self): header=0 ) sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) temp_srcs, sources_df = one_to_many_advanced( - temp_srcs, - sources_df, + temp_srcs, + sources_df, method='advanced' ) @@ -186,12 +186,12 @@ def test_duplicated_skyc1_empty(self): def test_method_advanced(self): ''' Test if one_to_many_advanced correctly identifies duplicate sources and - relates them for method=advanced. + relates them for method=advanced. - temp_srcs: all duplicate sources should have new unique source ids, + temp_srcs: all duplicate sources should have new unique source ids, the new ids are assigned in order of d2d_skyc2 - min d2d_skyc2 retains - original id, duplicate sources should have related sources listed using - new source ids, if >2 duplicates of 1 source then the sources with new + original id, duplicate sources should have related sources listed using + new source ids, if >2 duplicates of 1 source then the sources with new ids will be related to the old id whilst the old id will be related to all new ids, and other columns should remain unchanged. sources_df: must contain at least all source ids present in temp_src, @@ -199,17 +199,17 @@ def test_method_advanced(self): assigned to the new id. ''' temp_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), + os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), header=0 ) sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) temp_srcs, sources_df = one_to_many_advanced( - temp_srcs, - sources_df, + temp_srcs, + sources_df, method='advanced' ) @@ -219,30 +219,30 @@ def test_method_advanced(self): def test_method_deruiter(self): ''' Test if one_to_many_advanced correctly identifies duplicate sources and - relates them for method=deruiter. + relates them for method=deruiter. - temp_srcs: all duplicate sources should have new unique source ids, - the new ids are assigned in order of dr - min dr retains original id, - duplicate sources should have related sources listed using new source - ids, if >2 duplicates of 1 source then the sources with new ids will be - related to the old id whilst the old id will be related to all new ids, + temp_srcs: all duplicate sources should have new unique source ids, + the new ids are assigned in order of dr - min dr retains original id, + duplicate sources should have related sources listed using new source + ids, if >2 duplicates of 1 source then the sources with new ids will be + related to the old id whilst the old id will be related to all new ids, and other columns should remain unchanged. sources_df: must contain at least all source ids present in temp_src, rows with same id as duplicates in temp_srcs will be duplicated and assigned to the new id. ''' temp_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), + os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), header=0 ) sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0 ) temp_srcs, sources_df = one_to_many_advanced( - temp_srcs, - sources_df, + temp_srcs, + sources_df, method='deruiter' ) @@ -262,7 +262,7 @@ def setUpClass(self): ''' super().setUpClass() self.temp_srcs_nodup = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_nodup.csv'), + os.path.join(DATA_PATH, 'temp_srcs_nodup.csv'), header=0 ) self.temp_srcs_advanced_drop = pd.read_csv( @@ -270,10 +270,10 @@ def setUpClass(self): header=0 ) self.temp_srcs_deruiter_drop = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_deruiter_drop.csv'), + os.path.join(DATA_PATH, 'temp_srcs_deruiter_drop.csv'), header=0 ) - + def test_m_to_m_empty(self): ''' Test if many_to_many_advanced will return the input dataframe unchanged @@ -281,7 +281,7 @@ def test_m_to_m_empty(self): and souce_skyc1 need to be repeated for it to be a duplicate source. ''' temp_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'temp_srcs_nodup.csv'), + os.path.join(DATA_PATH, 'temp_srcs_nodup.csv'), header=0 ) @@ -292,16 +292,16 @@ def test_m_to_m_empty(self): def test_method_advanced(self): ''' Testing if many_to_many_advanced drops the correct rows for duplicate - sources when method=advanced. Duplicates are when both index_old_skyc2 - and souce_skyc1 are repeated. The duplicate rows with + sources when method=advanced. Duplicates are when both index_old_skyc2 + and souce_skyc1 are repeated. The duplicate rows with d2d_skyc2 > min(d2d_skyc2) will be dropped. - This test assumes that the index of the dataframe doesn't matter. + This test assumes that the index of the dataframe doesn't matter. ''' temp_srcs = pd.read_csv( os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), header=0 - ) + ) temp_srcs = many_to_many_advanced(temp_srcs, method='advanced') temp_srcs.reset_index(drop=True, inplace=True) @@ -311,8 +311,8 @@ def test_method_advanced(self): def test_method_deruiter(self): ''' Testing if many_to_many_advanced drops the correct rows for duplicate - sources when method=deruiter. Duplicates are when both index_old_skyc2 - and souce_skyc1 are repeated. The duplicate rows with dr > min(dr) will + sources when method=deruiter. Duplicates are when both index_old_skyc2 + and souce_skyc1 are repeated. The duplicate rows with dr > min(dr) will be dropped. This test assumes that the index of the dataframe doesn't matter. @@ -320,7 +320,7 @@ def test_method_deruiter(self): temp_srcs = pd.read_csv( os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), header=0 - ) + ) temp_srcs = many_to_many_advanced(temp_srcs, method='deruiter') temp_srcs.reset_index(drop=True, inplace=True) @@ -342,7 +342,7 @@ def setUpClass(self): self.temp_srcs_nodup = pd.read_csv( os.path.join(DATA_PATH, 'temp_srcs_nodup.csv'), header=0 - ) + ) self.temp_srcs_ind_rel = pd.read_csv( os.path.join(DATA_PATH, 'temp_srcs_ind_rel.csv'), header=0, @@ -358,7 +358,7 @@ def test_duplicated_skyc2_empty(self): temp_srcs = pd.read_csv( os.path.join(DATA_PATH, 'temp_srcs_nodup.csv'), header=0 - ) + ) temp_srcs = many_to_one_advanced(temp_srcs) @@ -366,16 +366,16 @@ def test_duplicated_skyc2_empty(self): def test_many_to_one_advanced(self): ''' - Testing if many_to_one_advanced relates the correct sources. Repeated - values in index_old_skyc2 are identified, these rows take the - source_skyc1 value without itself as related_skyc1 values. If there are - no source_skyc1 values except itself, then related_skyc1 is []. If - index_old_skyc2 is unique, then the previous related_skyc1 is retained. + Testing if many_to_one_advanced relates the correct sources. Repeated + values in index_old_skyc2 are identified, these rows take the + source_skyc1 value without itself as related_skyc1 values. If there are + no source_skyc1 values except itself, then related_skyc1 is []. If + index_old_skyc2 is unique, then the previous related_skyc1 is retained. ''' temp_srcs = pd.read_csv( os.path.join(DATA_PATH, 'temp_srcs_dup.csv'), header=0 - ) + ) temp_srcs_out = many_to_one_advanced(temp_srcs) @@ -384,15 +384,15 @@ def test_many_to_one_advanced(self): class TestHelpers(SimpleTestCase): ''' - Class which has some helper functions for testing. + Class which has some helper functions for testing. ''' def check_col(self, df1, df2, columns=['ra', 'dec', 'source', 'epoch']): ''' Function which checks that certain columns of two DataFrames are equal for BasicAssociationTest and AdvancedAssociationTest. - - Not testing related column, it is used in one_to_many_basic, which is + + Not testing related column, it is used in one_to_many_basic, which is already tested. Not testing d2d column, it is output from Astropy. Parameters @@ -425,15 +425,15 @@ def setUpClass(self): super().setUpClass() self.sources_df_basic_out = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_basic_out.csv') - ) + ) self.sources_df_no_new = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_no_new.csv') - ) + ) self.sources_df_all = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_all.csv') - ) + ) self.skyc1_srcs_in = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) self.skyc1_srcs_out = pd.read_csv( @@ -448,29 +448,29 @@ def setUpClass(self): def test_no_new_skyc2_srcs(self): ''' Test basic_association returns skyc1_srcs unchanged and sources_df with - new same sources under new epoch when given skyc2_srcs with no new - sources. + new same sources under new epoch when given skyc2_srcs with no new + sources. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_no_new.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_no_new.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) limit = Angle(10, unit='arcsec') @@ -485,28 +485,28 @@ def test_no_new_skyc2_srcs(self): def test_zero_limit(self): ''' Test basic_association returns all sources in skyc2_srcs as new sources - when the limit is zero. + when the limit is zero. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) limit = Angle(0, unit='arcsec') @@ -524,25 +524,25 @@ def test_basic_association(self): skyc1_srcs and sources_df. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) limit = Angle(10, unit='arcsec') @@ -568,15 +568,15 @@ def setUpClass(self): super().setUpClass() self.sources_df_advanced_out = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_advanced_out.csv') - ) + ) self.sources_df_no_new = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_no_new.csv') - ) + ) self.sources_df_all = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_all.csv') - ) + ) self.skyc1_srcs_in = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) self.skyc1_srcs_out = pd.read_csv( @@ -590,37 +590,37 @@ def setUpClass(self): def test_no_new_skyc2_srcs(self): ''' - Test advanced_association returns skyc1_srcs unchanged and sources_df - with new same sources under new epoch when given skyc2_srcs with no new - sources. + Test advanced_association returns skyc1_srcs unchanged and sources_df + with new same sources under new epoch when given skyc2_srcs with no new + sources. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_no_new.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_no_new.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) dr_limit = 5.68 bw_max = Angle(10, unit='arcsec') sources_df, skyc1_srcs = advanced_association( - 'advanced', sources_df, skyc1_srcs, skyc1, + 'advanced', sources_df, skyc1_srcs, skyc1, skyc2_srcs, skyc2, dr_limit, bw_max ) @@ -629,36 +629,36 @@ def test_no_new_skyc2_srcs(self): def test_zero_bw_max(self): ''' - Test advanced_association returns all sources in skyc2_srcs as new - sources when the limit is zero. + Test advanced_association returns all sources in skyc2_srcs as new + sources when the limit is zero. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) dr_limit = 5.68 bw_max = Angle(0, unit='arcsec') sources_df, skyc1_srcs = advanced_association( - 'advanced', sources_df, skyc1_srcs, skyc1, + 'advanced', sources_df, skyc1_srcs, skyc1, skyc2_srcs, skyc2, dr_limit, bw_max ) @@ -667,70 +667,70 @@ def test_zero_bw_max(self): def test_advanced(self): ''' - Test advanced_association correctly appends the sources in skyc2_srcs + Test advanced_association correctly appends the sources in skyc2_srcs into skyc1_srcs and sources_df for method=advanced. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) dr_limit = 5.68 bw_max = Angle(10, unit='arcsec') sources_df, skyc1_srcs = advanced_association( - 'advanced', sources_df, skyc1_srcs, skyc1, + 'advanced', sources_df, skyc1_srcs, skyc1, skyc2_srcs, skyc2, dr_limit, bw_max - ) + ) self.check_col(sources_df, self.sources_df_advanced_out) self.check_col(skyc1_srcs, self.skyc1_srcs_out) def test_deruiter(self): ''' - Test advanced_association correctly appends the sources in skyc2_srcs + Test advanced_association correctly appends the sources in skyc2_srcs into skyc1_srcs and sources_df for method=deruiter. - Note: this test is redundant, method is already tested in other + Note: this test is redundant, method is already tested in other functions. Better test would be if dr_limit gives different result. ''' sources_df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), + os.path.join(DATA_PATH, 'sources_df_ass_in.csv'), header=0 ) skyc1_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc1_srcs_in.csv'), header=0 ) skyc1 = SkyCoord( - skyc1_srcs['ra'].tolist(), - skyc1_srcs['dec'].tolist(), + skyc1_srcs['ra'].tolist(), + skyc1_srcs['dec'].tolist(), unit='deg' ) skyc2_srcs = pd.read_csv( - os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), + os.path.join(DATA_PATH, 'skyc2_srcs_in.csv'), header=0 ) skyc2 = SkyCoord( - skyc2_srcs['ra'].tolist(), - skyc2_srcs['dec'].tolist(), + skyc2_srcs['ra'].tolist(), + skyc2_srcs['dec'].tolist(), unit='deg' ) dr_limit = 5.68 @@ -759,20 +759,20 @@ def setUpClass(self): os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0, converters={'related': parse_or_nan} - ) + ) self.sources_df_out_2 = pd.read_csv( os.path.join(DATA_PATH, 'sources_df_out_2.csv'), header=0, converters={'related': parse_or_nan} - ) + ) def test_zero(self): ''' - Test _correct_parallel_source_ids doesn't change the input df when + Test _correct_parallel_source_ids doesn't change the input df when correction=0. ''' df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), + os.path.join(DATA_PATH, 'sources_df_in.csv'), header=0, converters={'related': parse_or_nan} ) @@ -787,8 +787,8 @@ def test_correct_parllel_source_ids(self): and relate column by the correction amount. ''' df = pd.read_csv( - os.path.join(DATA_PATH, 'sources_df_in.csv'), - header=0, + os.path.join(DATA_PATH, 'sources_df_in.csv'), + header=0, converters={'related': parse_or_nan} ) diff --git a/vast_pipeline/tests/test_pipelineconfig.py b/vast_pipeline/tests/test_pipelineconfig.py index e80fe913a..894caf6a6 100644 --- a/vast_pipeline/tests/test_pipelineconfig.py +++ b/vast_pipeline/tests/test_pipelineconfig.py @@ -140,6 +140,22 @@ def test_input_files_exist(self): pipeline_config = PipelineConfig(config_yaml) pipeline_config.validate() + def test_selavy_votable(self): + self.config_dict["inputs"]["selavy"][0] = ( + "vast_pipeline/tests/data/epoch01.selavy.components.xml" + ) + config_yaml = yaml.as_document(self.config_dict) + pipeline_config = PipelineConfig(config_yaml) + pipeline_config.validate() + + def test_selavy_csv(self): + self.config_dict["inputs"]["selavy"][0] = ( + "vast_pipeline/tests/data/epoch01.selavy.components.csv" + ) + config_yaml = yaml.as_document(self.config_dict) + pipeline_config = PipelineConfig(config_yaml) + pipeline_config.validate() + def test_input_glob(self): """Test simple glob expressions, one for each input""" config_yaml_original = yaml.as_document(self.config_dict) diff --git a/vast_pipeline/tests/test_regression/compare_runs.py b/vast_pipeline/tests/test_regression/compare_runs.py index f3510df94..7eb2a416b 100644 --- a/vast_pipeline/tests/test_regression/compare_runs.py +++ b/vast_pipeline/tests/test_regression/compare_runs.py @@ -4,9 +4,14 @@ from django.test import TestCase -def test_inc_assoc(testcase: TestCase, ass_add: pd.DataFrame, ass_backup: pd.DataFrame): +def test_inc_assoc( + testcase: TestCase, + ass_add: pd.DataFrame, + ass_backup: pd.DataFrame, + must_be_equal: bool = False +): ''' - Test that the number of associations increased or equal with added + Test that the number of associations increased or equal with added images. Parameters @@ -17,12 +22,16 @@ def test_inc_assoc(testcase: TestCase, ass_add: pd.DataFrame, ass_backup: pd.Dat Associations after images were added. ass_backup : pd.DataFrame Associations before images were added. + must_be_equal: bool + The associations being compared must be equal in length to assert True. ''' - - testcase.assertTrue(len(ass_add) >= len(ass_backup)) + if not must_be_equal: + testcase.assertTrue(len(ass_add) >= len(ass_backup)) + else: + testcase.assertEqual(len(ass_add), len(ass_backup)) def test_update_source( - testcase: TestCase, sources_backup: pd.DataFrame, sources_backup_db: pd.DataFrame, + testcase: TestCase, sources_backup: pd.DataFrame, sources_backup_db: pd.DataFrame, sources_add: pd.DataFrame, sources_add_db: pd.DataFrame ): ''' @@ -55,22 +64,22 @@ def test_update_source( def test_sources(sources_1: pd.DataFrame, sources_2: pd.DataFrame): ''' - Test that the sources are the same between two different runs. + Test that the sources are the same between two different runs. Parameters ---------- sources_1 : pd.DataFrame - The sources found in one run. + The sources found in one run. sources_2 : pd.DataFrame The sources found in a different run. ''' sources_1 = ( - sources_1 + sources_1 .sort_values(by=['wavg_ra', 'wavg_dec']) .reset_index(drop=True) ) sources_2 = ( - sources_2 + sources_2 .sort_values(by=['wavg_ra', 'wavg_dec']) .reset_index(drop=True) ) @@ -78,10 +87,10 @@ def test_sources(sources_1: pd.DataFrame, sources_2: pd.DataFrame): pd.testing.assert_frame_equal( sources_1, sources_2, - check_less_precise=4 + rtol=1e-4 ) -def test_relations(testcase: TestCase, relations_1: pd.DataFrame, +def test_relations(testcase: TestCase, relations_1: pd.DataFrame, relations_2: pd.DataFrame): ''' Test that the number relations are the same between two different runs. @@ -91,7 +100,7 @@ def test_relations(testcase: TestCase, relations_1: pd.DataFrame, testcase : class Test class. relations_1 : pd.DataFrame - The relations found in one run. + The relations found in one run. relations_2 : pd.DataFrame The relations found in a different run. ''' diff --git a/vast_pipeline/tests/test_regression/test_epoch.py b/vast_pipeline/tests/test_regression/test_epoch.py index 5d4064d1a..05d889274 100644 --- a/vast_pipeline/tests/test_regression/test_epoch.py +++ b/vast_pipeline/tests/test_regression/test_epoch.py @@ -1,5 +1,4 @@ import os -import types import pandas as pd import unittest import glob diff --git a/vast_pipeline/tests/test_regression/test_epoch_forced.py b/vast_pipeline/tests/test_regression/test_epoch_forced.py index bed69fde2..44ea7b31d 100644 --- a/vast_pipeline/tests/test_regression/test_epoch_forced.py +++ b/vast_pipeline/tests/test_regression/test_epoch_forced.py @@ -1,5 +1,4 @@ import os -import types import pandas as pd import unittest import glob diff --git a/vast_pipeline/tests/test_regression/test_epoch_parallel_add_image.py b/vast_pipeline/tests/test_regression/test_epoch_parallel_add_image.py index c7ae77631..793beb4fa 100644 --- a/vast_pipeline/tests/test_regression/test_epoch_parallel_add_image.py +++ b/vast_pipeline/tests/test_regression/test_epoch_parallel_add_image.py @@ -11,7 +11,6 @@ from django.test import TestCase, override_settings from django.core.management import call_command -from vast_pipeline.models import Source TEST_ROOT = os.path.join(s.BASE_DIR, 'vast_pipeline', 'tests') diff --git a/vast_pipeline/tests/test_regression/test_forced.py b/vast_pipeline/tests/test_regression/test_forced.py index 0e7e13e89..4cd570fe9 100644 --- a/vast_pipeline/tests/test_regression/test_forced.py +++ b/vast_pipeline/tests/test_regression/test_forced.py @@ -1,5 +1,4 @@ import os -import types import pandas as pd import unittest import glob diff --git a/vast_pipeline/tests/test_regression/test_normal.py b/vast_pipeline/tests/test_regression/test_normal.py index 0c84e6e2c..79e3be417 100644 --- a/vast_pipeline/tests/test_regression/test_normal.py +++ b/vast_pipeline/tests/test_regression/test_normal.py @@ -1,5 +1,4 @@ import os -import types import pandas as pd import unittest import glob diff --git a/vast_pipeline/tests/test_regression/test_parallel.py b/vast_pipeline/tests/test_regression/test_parallel.py index 38a4cd4d0..fa676a334 100644 --- a/vast_pipeline/tests/test_regression/test_parallel.py +++ b/vast_pipeline/tests/test_regression/test_parallel.py @@ -11,7 +11,6 @@ from django.test import TestCase, override_settings from django.core.management import call_command -from vast_pipeline.models import Source TEST_ROOT = os.path.join(s.BASE_DIR, 'vast_pipeline', 'tests') diff --git a/vast_pipeline/tests/test_regression/test_parallel_add_image.py b/vast_pipeline/tests/test_regression/test_parallel_add_image.py index 388f0308e..620cc885a 100644 --- a/vast_pipeline/tests/test_regression/test_parallel_add_image.py +++ b/vast_pipeline/tests/test_regression/test_parallel_add_image.py @@ -11,7 +11,6 @@ from django.test import TestCase, override_settings from django.core.management import call_command -from vast_pipeline.models import Source TEST_ROOT = os.path.join(s.BASE_DIR, 'vast_pipeline', 'tests') diff --git a/vast_pipeline/tests/test_regression/test_restore.py b/vast_pipeline/tests/test_regression/test_restore.py new file mode 100644 index 000000000..0cb45702a --- /dev/null +++ b/vast_pipeline/tests/test_regression/test_restore.py @@ -0,0 +1,136 @@ +import os +import pandas as pd +import unittest +import glob +import shutil + +from vast_pipeline.tests.test_regression import compare_runs, gen_config +from vast_pipeline.tests.test_regression.make_testdir import make_testdir + +from django.conf import settings as s +from django.test import TestCase, override_settings +from django.core.management import call_command + +from vast_pipeline.models import Source + + +TEST_ROOT = os.path.join(s.BASE_DIR, 'vast_pipeline', 'tests') + + +no_data = not glob.glob(os.path.join(TEST_ROOT, 'regression-data','EPOCH*')) +@unittest.skipIf( + no_data, + 'The regression test data is missing, skipping add image tests' +) +@override_settings( + PIPELINE_WORKING_DIR=os.path.join(TEST_ROOT, 'pipeline-runs'), +) +class BasicAddImageTest(TestCase): + ''' + Test pipeline runs when adding an image for basic association method. + ''' + + @classmethod + def setUpTestData(self): + ''' + Set up directories to test data, run the pipeline, and read the files. + ''' + base_path = 'normal-basic-forced' + compare_path = 'restore-basic-forced' + self.base_run = os.path.join( + s.PIPELINE_WORKING_DIR, base_path + ) + self.compare_run = os.path.join( + s.PIPELINE_WORKING_DIR, compare_path + ) + + # run with three epoch images + make_testdir(self.base_run) + gen_config.gen_config( + base_path, + s.PIPELINE_WORKING_DIR, + ['01', '03x', '02'] + ) + call_command('runpipeline', self.base_run) + self.sources_base = pd.read_parquet( + os.path.join(self.base_run, 'sources.parquet') + ) + self.relations_base = pd.read_parquet( + os.path.join(self.base_run, 'relations.parquet') + ) + self.ass_base = pd.read_parquet( + os.path.join(self.base_run, 'associations.parquet') + ) + index = self.sources_base.index + self.sources_base_db = pd.DataFrame( + [Source.objects.get(id=ind).n_meas for ind in index], + index=index, + columns = ['n_meas'] + ) + + # run with add image and then restore + make_testdir(self.compare_run) + gen_config.gen_config( + compare_path, + s.PIPELINE_WORKING_DIR, + ['01', '03x', '02'] + ) + call_command('runpipeline', self.compare_run) + + gen_config.gen_config( + compare_path, + s.PIPELINE_WORKING_DIR, + ['01', '03x', '02', '05x', '06x', '12'] + ) + call_command('runpipeline', self.compare_run) + call_command('restorepiperun', self.compare_run, no_confirm=True) + + self.ass_compare = pd.read_parquet( + os.path.join(self.compare_run, 'associations.parquet') + ) + self.sources_compare = pd.read_parquet( + os.path.join(self.compare_run, 'sources.parquet') + ) + index = self.sources_compare.index + self.sources_compare_db = pd.DataFrame( + [Source.objects.get(id=ind).n_meas for ind in index], + index=index, + columns = ['n_meas'] + ) + self.relations_compare = pd.read_parquet( + os.path.join(self.compare_run, 'relations.parquet') + ) + + # remove directories + shutil.rmtree(self.base_run) + shutil.rmtree(self.compare_run) + + def test_inc_assoc(self): + ''' + See documentation for test_inc_assoc in compare_runs. + ''' + compare_runs.test_inc_assoc( + self, self.ass_compare, self.ass_base, must_be_equal=True + ) + + def test_update_source(self): + ''' + See documentation for test_update_sources in compare_runs. + ''' + compare_runs.test_update_source( + self, + self.sources_base, self.sources_base_db, + self.sources_compare, self.sources_compare_db + ) + + def test_sources(self): + ''' + See documentation for test_sources in compare_runs. + ''' + compare_runs.test_sources(self.sources_base, self.sources_compare) + + def test_relations(self): + ''' + See documentation for test_relations in comapre_runs. + ''' + compare_runs.test_relations(self, self.relations_base, self.relations_compare) diff --git a/vast_pipeline/tests/test_runpipeline.py b/vast_pipeline/tests/test_runpipeline.py index 6e79ba518..30c26b9bb 100644 --- a/vast_pipeline/tests/test_runpipeline.py +++ b/vast_pipeline/tests/test_runpipeline.py @@ -1,29 +1,77 @@ +import glob import os +from typing import List, Optional from django.conf import settings as s from django.test import TestCase, override_settings -from django.core.management import call_command +from django.core.management import call_command, CommandError + +from vast_pipeline.models import Image TEST_ROOT = os.path.join(s.BASE_DIR, 'vast_pipeline', 'tests') +def _cleanup_test_run(run_dir: str, run_logs: Optional[List[str]] = None): + call_command('clearpiperun', run_dir) + prev_config_path = os.path.join(run_dir, "config_prev.yaml") + if os.path.exists(prev_config_path): + os.remove(prev_config_path) + if run_logs is None: + run_logs = glob.glob(os.path.join(run_dir, '*_log.txt')) + for run_log in run_logs: + os.remove(run_log) + + @override_settings( PIPELINE_WORKING_DIR=os.path.join(TEST_ROOT, 'pipeline-runs'), RAW_IMAGE_DIR=os.path.join(TEST_ROOT, 'data'), ) class RunPipelineTest(TestCase): - basic_assoc_run: str + run_dir: str + run_logs: Optional[List[str]] = None - @classmethod - def setUpTestData(cls): - cls.basic_assoc_run = os.path.join(s.PIPELINE_WORKING_DIR, 'basic-association') + def tearDown(self): + """Clean up the run directory after the test by removing the backup config and + all run logs. + """ + _cleanup_test_run(self.run_dir, run_logs=self.run_logs) - def setUp(self): + def test_check_run(self): + self.run_dir = os.path.join(s.PIPELINE_WORKING_DIR, 'basic-association') # TODO: replace with a load images function and call 'runpipeline' # from each tests (e.g. test_basic_assoc, text_advanced_assoc, etc.) - call_command('runpipeline', self.basic_assoc_run) + call_command('runpipeline', self.run_dir) + self.run_logs = glob.glob(os.path.join(self.run_dir, '*_log.txt')) + self.assertEqual(len(self.run_logs), 1) - def test_check_run(self): - run_log_path = os.path.join(self.basic_assoc_run, 'log.txt') - self.assertTrue(os.path.exists(run_log_path)) + def test_invalid_catalog(self): + self.run_dir = os.path.join(s.PIPELINE_WORKING_DIR, 'basic-association-invalid-catalog') + with self.assertRaises(CommandError) as context: + call_command('runpipeline', self.run_dir) + # check the original exception raised during runpipeline command is a KeyError + # (caused by attempting to read the invalid catalog) + original_exception = context.exception.__context__ + self.assertIsInstance(original_exception, KeyError) + # check that only the image for the invalid catalog was not saved + for epoch in range(1, 5): + image = Image.objects.get(name=f"epoch{epoch:02d}.fits") + self.assertIsInstance(image, Image) + with self.assertRaises(Image.DoesNotExist): + Image.objects.get(name="epoch05.fits") + # clean up config backup + os.remove(os.path.join(self.run_dir, "config_temp.yaml")) + + def test_pair_metrics_exist(self): + self.run_dir = os.path.join(s.PIPELINE_WORKING_DIR, 'basic-association') + call_command('runpipeline', self.run_dir) + # check that the measurement pairs parquet file was written + self.assertTrue(os.path.exists(os.path.join(self.run_dir, "measurement_pairs.parquet"))) + + def test_no_pair_metrics(self): + self.run_dir = os.path.join(s.PIPELINE_WORKING_DIR, 'basic-association-no-pairs') + call_command('runpipeline', self.run_dir) + # check that the measurement pairs parquet file was not written + self.assertFalse(os.path.exists(os.path.join(self.run_dir, "measurement_pairs.parquet"))) + # check that at least one of the other parquets were written + self.assertTrue(os.path.exists(os.path.join(self.run_dir, "sources.parquet"))) diff --git a/vast_pipeline/tests/test_webserver.py b/vast_pipeline/tests/test_webserver.py index 2312cd13b..423ebb7bb 100644 --- a/vast_pipeline/tests/test_webserver.py +++ b/vast_pipeline/tests/test_webserver.py @@ -1,7 +1,5 @@ -from django.conf import settings as s from django.test import Client from django.test import SimpleTestCase, override_settings -from django.urls import reverse @override_settings( diff --git a/vast_pipeline/urls.py b/vast_pipeline/urls.py index 0148d5cd2..b6e168990 100644 --- a/vast_pipeline/urls.py +++ b/vast_pipeline/urls.py @@ -22,6 +22,7 @@ router.register(r'sources', views.SourceViewSet, 'api_sources') router.register(r'rawimages', views.RawImageListSet, 'api_rawimages') router.register(r'runcfg', views.RunConfigSet, 'api_runcfg') +router.register(r'runlog', views.RunLogSet, 'api_runlog') router.register(r'sourcesfavs', views.SourceFavViewSet, 'api_sources_favs') router.register(r'utils', views.UtilitiesSet, 'api_utils') router.register(r'plots', views.SourcePlotsSet, 'api_source_plots') @@ -42,6 +43,8 @@ name='measurement_detail' ), path('sources/query/', views.SourceQuery, name='source_query'), + path('sources/query/plot/', views.SourceEtaVPlot, name='source_etav_plot'), + path('sources/query/plot/update//', views.SourceEtaVPlotUpdate, name='source_etav_plot_update'), path('sources//', views.SourceDetail, name='source_detail'), path('sources/favs/', views.UserSourceFavsList, name='source_favs'), path( @@ -50,8 +53,8 @@ kwargs={"tag_model": Source.tags.tag_model}, name="source_tags_autocomplete", ), - path('cutout//', views.ImageCutout.as_view(), name='cutout'), - path('cutout///', views.ImageCutout.as_view(), name='cutout'), + path('cutout//', views.ImageCutout.as_view(), name='cutout'), + path('cutout///', views.ImageCutout.as_view(), name='cutout'), path( 'measurements//,,/region/', views.MeasurementQuery.as_view(), diff --git a/vast_pipeline/utils/auth.py b/vast_pipeline/utils/auth.py index 72ceb96cd..74c3b8744 100644 --- a/vast_pipeline/utils/auth.py +++ b/vast_pipeline/utils/auth.py @@ -1,7 +1,7 @@ import logging import requests -from typing import Tuple, Dict +from typing import Dict from django.conf import settings from django.contrib.auth.models import User @@ -31,8 +31,8 @@ def create_admin_user(uid: int, response: Dict, details: Dict, user: User, Social auth user model object Returns: - return a dictionary with the Django User object in it or empty if - no action are taken + A dictionary with the Django User object in it or empty if + no action is taken. """ # assume github-org backend, add # if other backend are implemented @@ -88,8 +88,8 @@ def load_github_avatar(response: Dict, social: UserSocialAuth, *args, Arbitrary keyword arguments. Returns: - return a dictionary with the Social auth user object in it or empty if - no action are taken + A dictionary with the Social auth user object in it or empty if + no action is taken. """ # assume github-org backend, add # if other backend are implemented diff --git a/vast_pipeline/utils/external_query.py b/vast_pipeline/utils/external_query.py new file mode 100644 index 000000000..d292eaeff --- /dev/null +++ b/vast_pipeline/utils/external_query.py @@ -0,0 +1,253 @@ +import json +from typing import Any, Dict, List +from urllib.parse import urljoin + +from astropy.coordinates import SkyCoord, Angle, Longitude, Latitude +from astroquery.simbad import Simbad +from astroquery.ipac.ned import Ned +from django.conf import settings +import requests + + +def simbad(coord: SkyCoord, radius: Angle) -> List[Dict[str, Any]]: + """Perform a cone search for sources with SIMBAD. + + Args: + coord: The coordinate of the centre of the cone. + radius: The radius of the cone in angular units. + + Returns: + A list of dicts, where each dict is a query result row with the following keys: + + - object_name: the name of the astronomical object. + - database: the source of the result, i.e. SIMBAD. + - separation_arcsec: separation to the query coordinate in arcsec. + - otype: object type. + - otype_long: long form of the object type. + - ra_hms: RA coordinate string in hms format. + - dec_dms: Dec coordinate string in ±dms format. + """ + CustomSimbad = Simbad() + CustomSimbad.add_votable_fields( + "distance_result", + "otype(S)", + "otype(V)", + "otypes", + ) + try: + simbad_result_table = CustomSimbad.query_region(coord, radius=radius) + except requests.HTTPError: + # try the Harvard mirror + CustomSimbad.SIMBAD_URL = "https://simbad.harvard.edu/simbad/sim-script" + simbad_result_table = CustomSimbad.query_region(coord, radius=radius) + if simbad_result_table is None: + simbad_results_dict_list = [] + else: + simbad_results_df = simbad_result_table[ + ["MAIN_ID", "DISTANCE_RESULT", "OTYPE_S", "OTYPE_V", "RA", "DEC"] + ].to_pandas() + simbad_results_df = simbad_results_df.rename( + columns={ + "MAIN_ID": "object_name", + "DISTANCE_RESULT": "separation_arcsec", + "OTYPE_S": "otype", + "OTYPE_V": "otype_long", + "RA": "ra_hms", + "DEC": "dec_dms", + } + ) + simbad_results_df["database"] = "SIMBAD" + # convert coordinates to RA (hms) Dec (dms) strings + simbad_results_df["ra_hms"] = Longitude( + simbad_results_df["ra_hms"], unit="hourangle" + ).to_string(unit="hourangle") + simbad_results_df["dec_dms"] = Latitude( + simbad_results_df["dec_dms"], unit="deg" + ).to_string(unit="deg") + simbad_results_dict_list = simbad_results_df.to_dict(orient="records") + return simbad_results_dict_list + + +def ned(coord: SkyCoord, radius: Angle) -> List[Dict[str, Any]]: + """Perform a cone search for sources with NED. + + Args: + coord: The coordinate of the centre of the cone. + radius: The radius of the cone in angular units. + + Returns: + A list of dicts, where each dict is a query result row with the following keys: + + - object_name: the name of the astronomical object. + - database: the source of the result, i.e. NED. + - separation_arcsec: separation to the query coordinate in arcsec. + - otype: object type. + - otype_long: long form of the object type. + - ra_hms: RA coordinate string in hms format. + - dec_dms: Dec coordinate string in ±dms format. + """ + # NED API doesn't supply the long-form object types. + # Copied from https://ned.ipac.caltech.edu/Documents/Guides/Database + NED_OTYPES = { + "*": "Star or Point Source", + "**": "Double star", + "*Ass": "Stellar association", + "*Cl": "Star cluster", + "AbLS": "Absorption line system", + "Blue*": "Blue star", + "C*": "Carbon star", + "EmLS": "Emission line source", + "EmObj": "Emission object", + "exG*": "Extragalactic star (not a member of an identified galaxy)", + "Flare*": "Flare star", + "G": "Galaxy", + "GammaS": "Gamma ray source", + "GClstr": "Cluster of galaxies", + "GGroup": "Group of galaxies", + "GPair": "Galaxy pair", + "GTrpl": "Galaxy triple", + "G_Lens": "Lensed image of a galaxy", + "HII": "HII region", + "IrS": "Infrared source", + "MCld": "Molecular cloud", + "Neb": "Nebula", + "Nova": "Nova", + "Other": "Other classification (e.g. comet; plate defect)", + "PN": "Planetary nebula", + "PofG": "Part of galaxy", + "Psr": "Pulsar", + "QGroup": "Group of QSOs", + "QSO": "Quasi-stellar object", + "Q_Lens": "Lensed image of a QSO", + "RadioS": "Radio source", + "Red*": "Red star", + "RfN": "Reflection nebula", + "SN": "Supernova", + "SNR": "Supernova remnant", + "UvES": "Ultraviolet excess source", + "UvS": "Ultraviolet source", + "V*": "Variable star", + "VisS": "Visual source", + "WD*": "White dwarf", + "WR*": "Wolf-Rayet star", + "XrayS": "X-ray source", + "!*": "Galactic star", + "!**": "Galactic double star", + "!*Ass": "Galactic star association", + "!*Cl": "Galactic Star cluster", + "!Blue*": "Galactic blue star", + "!C*": "Galactic carbon star", + "!EmObj": "Galactic emission line object", + "!Flar*": "Galactic flare star", + "!HII": "Galactic HII region", + "!MCld": "Galactic molecular cloud", + "!Neb": "Galactic nebula", + "!Nova": "Galactic nova", + "!PN": "Galactic planetary nebula", + "!Psr": "Galactic pulsar", + "!RfN": "Galactic reflection nebula", + "!Red*": "Galactic red star", + "!SN": "Galactic supernova", + "!SNR": "Galactic supernova remnant", + "!V*": "Galactic variable star", + "!WD*": "Galactic white dwarf", + "!WR*": "Galactic Wolf-Rayet star", + } + ned_result_table = Ned.query_region(coord, radius=radius) + if ned_result_table is None or len(ned_result_table) == 0: + ned_results_dict_list = [] + else: + ned_results_df = ned_result_table[ + ["Object Name", "Separation", "Type", "RA", "DEC"] + ].to_pandas() + ned_results_df = ned_results_df.rename( + columns={ + "Object Name": "object_name", + "Separation": "separation_arcsec", + "Type": "otype", + "RA": "ra_hms", + "DEC": "dec_dms", + } + ) + ned_results_df["otype_long"] = ned_results_df.otype.replace(NED_OTYPES) + # convert NED result separation (arcmin) to arcsec + ned_results_df["separation_arcsec"] = ned_results_df["separation_arcsec"] * 60 + # convert coordinates to RA (hms) Dec (dms) strings + ned_results_df["ra_hms"] = Longitude( + ned_results_df["ra_hms"], unit="deg" + ).to_string(unit="hourangle") + ned_results_df["dec_dms"] = Latitude( + ned_results_df["dec_dms"], unit="deg" + ).to_string(unit="deg") + ned_results_df["database"] = "NED" + # convert dataframe to dict and replace float NaNs with None for JSON encoding + ned_results_dict_list = ned_results_df.sort_values("separation_arcsec").to_dict( + orient="records" + ) + return ned_results_dict_list + + +def tns(coord: SkyCoord, radius: Angle) -> List[Dict[str, Any]]: + """Perform a cone search for sources with the Transient Name Server (TNS). + + Args: + coord: The coordinate of the centre of the cone. + radius: The radius of the cone in angular units. + + Returns: + A list of dicts, where each dict is a query result row with the following keys: + + - object_name: the name of the transient. + - database: the source of the result, i.e. TNS. + - separation_arcsec: separation to the query coordinate in arcsec. + - otype: object type. + - otype_long: long form of the object type. Not given by TNS, will always be + an empty string. + - ra_hms: RA coordinate string in hms format. + - dec_dms: Dec coordinate string in ±dms format. + """ + TNS_API_URL = "https://www.wis-tns.org/api/" + headers = { + "user-agent": settings.TNS_USER_AGENT, + } + + search_dict = { + "ra": coord.ra.to_string(unit="hourangle", sep=":", pad=True), + "dec": coord.dec.to_string(unit="deg", sep=":", alwayssign=True, pad=True), + "radius": str(radius.value), + "units": radius.unit.name, + } + r = requests.post( + urljoin(TNS_API_URL, "get/search"), + data={"api_key": settings.TNS_API_KEY, "data": json.dumps(search_dict)}, + headers=headers, + ) + tns_results_dict_list: List[Dict[str, Any]] + if r.ok: + tns_results_dict_list = r.json()["data"]["reply"] + # Get details for each object result. TNS API doesn't support doing this in one + # request, so we iterate. + for result in tns_results_dict_list: + search_dict = { + "objname": result["objname"], + } + r = requests.post( + urljoin(TNS_API_URL, "get/object"), + data={"api_key": settings.TNS_API_KEY, "data": json.dumps(search_dict)}, + headers=headers, + ) + if r.ok: + object_dict = r.json()["data"]["reply"] + object_coord = SkyCoord( + ra=object_dict["radeg"], dec=object_dict["decdeg"], unit="deg" + ) + result["otype"] = object_dict["object_type"]["name"] + if result["otype"] is None: + result["otype"] = "" + result["otype_long"] = "" + result["separation_arcsec"] = coord.separation(object_coord).arcsec + result["ra_hms"] = object_coord.ra.to_string(unit="hourangle") + result["dec_dms"] = object_coord.dec.to_string(unit="deg") + result["database"] = "TNS" + result["object_name"] = object_dict["objname"] + return tns_results_dict_list diff --git a/vast_pipeline/utils/utils.py b/vast_pipeline/utils/utils.py index e09d421d3..f8a3b738e 100644 --- a/vast_pipeline/utils/utils.py +++ b/vast_pipeline/utils/utils.py @@ -10,7 +10,7 @@ from typing import Any, Dict, Tuple from astropy import units as u -from astropy.coordinates import SkyCoord +from astropy.coordinates import SkyCoord, Longitude, Latitude import numpy as np import pandas as pd @@ -18,7 +18,7 @@ logger = logging.getLogger(__name__) -class StopWatch(): +class StopWatch: """ A simple stopwatch to simplify timing code. """ @@ -60,7 +60,7 @@ def reset_init(self) -> float: return diff -def check_read_write_perm(path: str, perm: str='W') -> None: +def check_read_write_perm(path: str, perm: str = "W") -> None: """ Assess the file permission on a path. @@ -74,47 +74,25 @@ def check_read_write_perm(path: str, perm: str='W') -> None: Raises: IOError: The permission is not valid on the checked directory. """ - assert perm in ('R', 'W', 'X'), 'permission not supported' + assert perm in ("R", "W", "X"), "permission not supported" - perm_map = {'R': os.R_OK, 'W': os.W_OK, 'X': os.X_OK} + perm_map = {"R": os.R_OK, "W": os.W_OK, "X": os.X_OK} if not os.access(path, perm_map[perm]): - msg = f'permission not valid on folder: {path}' + msg = f"permission not valid on folder: {path}" logger.error(msg) raise IOError(msg) pass -def deg2sex(deg: float) -> Tuple[int, Tuple[float, float, float]]: - """ - Converts an angle in degrees to a tuple containing its sexagesimal components. - - Args: - deg: The angle to convert in degrees. - - Returns: - A nested tuple in the form (sign, (degrees, minutes, seconds)), where sign is - either -1 or 1. - - Example: - >>> deg2sex(12.582438888888889) - (12, 34, 56.78000000000182) - >>> deg2sex(-12.582438888888889) - (-12, 34, 56.78000000000182) - """ - - sign = -1 if deg < 0 else 1 - adeg = abs(deg) - degf = m.floor(adeg) - mins = (adeg - degf) * 60. - minsf = int(m.floor(mins)) - secs = (mins - minsf) * 60. - - return (sign, (degf, minsf, secs)) - - -def deg2dms(deg: float, dms_format: bool = False, precision: int = 2) -> str: - """Convert angle in degrees into a DMS formatted string. e.g. +def deg2dms( + deg: float, + dms_format: bool = False, + precision: int = 2, + truncate: bool = False, + latitude: bool = True, +) -> str: + """Convert angle in degrees into a DMS formatted string. Args: deg: The angle to convert in degrees. @@ -123,6 +101,11 @@ def deg2dms(deg: float, dms_format: bool = False, precision: int = 2) -> str: precision (optional): Floating point precision of the arcseconds component. Can be 0 or a positive integer. Negative values will be interpreted as 0. Defaults to 2. + truncate (optional): Truncate values after the decimal point instead of rounding. + Defaults to False (rounding). + latitude (optional): The input `deg` value should be intrepreted as a latitude. + Otherwise, it will be interpreted as a longitude. + Defaults to True (latitude). Returns: `deg` formatted as a DMS string. @@ -134,21 +117,53 @@ def deg2dms(deg: float, dms_format: bool = False, precision: int = 2) -> str: '+02d34m56.78s' >>> deg2dms(-12.582438888888889, precision=1) '-12:34:56.8' + >>> deg2dms(-12.582438888888889, precision=1, truncate=True) + '-12:34:56.7' """ - - sign, sex = deg2sex(deg) - signchar = "+" if sign == 1 else "-" + AngleClass = Latitude if latitude else Longitude + angle = AngleClass(deg, unit="deg") precision = precision if precision >= 0 else 0 - sec_width = 3 + precision if precision > 0 else 2 - - if dms_format: - return f'{signchar}{sex[0]:02d}d{sex[1]:02d}m{sex[2]:0{sec_width}.{precision}f}s' - - return f'{signchar}{sex[0]:02d}:{sex[1]:02d}:{sex[2]:0{sec_width}.{precision}f}' - -def deg2hms(deg: float, hms_format: bool = False, precision: int = 2) -> str: - """Convert angle in degrees into a HMS formatted string. e.g. + output_str: str = angle.to_string( + unit="deg", + sep="fromunit" if dms_format else ":", + precision=precision if not truncate else None, + alwayssign=True, + pad=True, + ) + if truncate: + # find the decimal point char position and the number of decimal places in the + # rendered input coordinate (in DMS format, not decimal deg) + dp_pos = output_str.find(".") + n_dp = len(output_str[dp_pos + 1:]) if dp_pos >= 0 else 0 + + # if the input coordinate precision is less than the requsted output precision, + # pad the end with zeroes + if n_dp < precision: + seconds_str = "" + # account for rendered input coord having precision = 0 + if dp_pos < 0: + seconds_str += "." + seconds_str += "0" * (precision - n_dp) + output_str += seconds_str + # otherwise, cut off the excess decimal places + elif n_dp > precision: + if precision > 0: + # account for the decimal point char + precision += 1 + output_str = output_str[: dp_pos + precision] + # in the n_dp == precision case, do nothing + return output_str + + +def deg2hms( + deg: float, + hms_format: bool = False, + precision: int = 2, + truncate: bool = False, + longitude: bool = True, +) -> str: + """Convert angle in degrees into a HMS formatted string. Args: deg: The angle to convert in degrees. @@ -157,6 +172,11 @@ def deg2hms(deg: float, hms_format: bool = False, precision: int = 2) -> str: precision (optional): Floating point precision of the seconds component. Can be 0 or a positive integer. Negative values will be interpreted as 0. Defaults to 2. + truncate (optional): Truncate values after the decimal point instead of rounding. + Defaults to False (rounding). + longitude (optional): The input `deg` value should be intrepreted as a longitude. + Otherwise, it will be interpreted as a latitude. + Defaults to True (longitude). Returns: `deg` formatted as an HMS string. @@ -168,15 +188,18 @@ def deg2hms(deg: float, hms_format: bool = False, precision: int = 2) -> str: '12h34m56.78s' >>> deg2hms(188.73658333333333, precision=1) '12:34:56.8' + >>> deg2hms(188.73658333333333, precision=1, truncate=True) + '12:34:56.7' """ - sign, sex = deg2sex(deg / 15.) - precision = precision if precision >= 0 else 0 - sec_width = 3 + precision if precision > 0 else 2 - - if hms_format: - return f'{sex[0]:02d}h{sex[1]:02d}m{sex[2]:0{sec_width}.{precision}f}s' - - return f'{sex[0]:02d}:{sex[1]:02d}:{sex[2]:0{sec_width}.{precision}f}' + # use the deg2dms formatter, replace d with h, and cut off the leading ± + # sign + return deg2dms( + deg / 15.0, + dms_format=hms_format, + precision=precision, + truncate=truncate, + latitude=not longitude, + ).replace("d", "h")[1:] def eq_to_cart(ra: float, dec: float) -> Tuple[float, float, float]: @@ -189,14 +212,16 @@ def eq_to_cart(ra: float, dec: float) -> Tuple[float, float, float]: dec: The declination coordinate, in degrees, to convert. Returns: - The cartesian coordinates. + The cartesian x coordinate. + The cartesian y coordinate. + The cartesian z coordinate. """ # TODO: This part of the code can probably be removed along with the # storage of these coodinates on the image. return ( - m.cos(m.radians(dec)) * m.cos(m.radians(ra)),# Cartesian x - m.cos(m.radians(dec)) * m.sin(m.radians(ra)),# Cartesian y - m.sin(m.radians(dec))# Cartesian z + m.cos(m.radians(dec)) * m.cos(m.radians(ra)), # Cartesian x + m.cos(m.radians(dec)) * m.sin(m.radians(ra)), # Cartesian y + m.sin(m.radians(dec)), # Cartesian z ) @@ -209,9 +234,16 @@ def equ2gal(ra: float, dec: float) -> Tuple[float, float]: dec (float): Declination in units of degrees. Returns: - Tuple (float, float): Galactic longitude and latitude in degrees. + Galactic longitude in degrees. + Galactic latitude in degrees. """ - c = SkyCoord(np.float(ra), np.float(dec), unit=(u.deg, u.deg), frame='icrs') + c = SkyCoord( + np.float(ra), + np.float(dec), + unit=( + u.deg, + u.deg), + frame="icrs") l = c.galactic.l.deg b = c.galactic.b.deg @@ -227,9 +259,13 @@ def gal2equ(l: float, b: float) -> Tuple[float, float]: b (float): Galactic latitude in degrees. Returns: - Tuple (float, float): Right ascension and declination in units of degrees. + Right ascension in degrees. + Declination in degrees. """ - c = SkyCoord(l=np.float(l) * u.deg, b=np.float(b) * u.deg, frame='galactic') + c = SkyCoord( + l=np.float(l) * u.deg, + b=np.float(b) * u.deg, + frame="galactic") ra = c.icrs.ra.deg dec = c.icrs.dec.deg @@ -251,11 +287,12 @@ def parse_coord(coord_string: str, coord_frame: str = "icrs") -> SkyCoord: coord_frame (str, optional): The frame of `coord_string`. Defaults to "icrs". Returns: - SkyCoord + The SkyCoord object. """ # if both coord components are decimals, assume they're in degrees, otherwise assume # hourangles and degrees. Note that the unit parameter is ignored if the units are - # not ambiguous i.e. if coord_string contains the units (e.g. 18.4d, 5h35m, etc) + # not ambiguous i.e. if coord_string contains the units (e.g. 18.4d, + # 5h35m, etc) try: _ = [float(x) for x in coord_string.split()] unit = "deg" @@ -282,11 +319,10 @@ def optimize_floats(df: pd.DataFrame) -> pd.DataFrame: input dataframe, no specific columns. Returns: - The input dataframe with the `float64` type - columns downcasted. + The input dataframe with the `float64` type columns downcasted. """ - floats = df.select_dtypes(include=['float64']).columns.tolist() - df[floats] = df[floats].apply(pd.to_numeric, downcast='float') + floats = df.select_dtypes(include=["float64"]).columns.tolist() + df[floats] = df[floats].apply(pd.to_numeric, downcast="float") return df @@ -303,16 +339,17 @@ def optimize_ints(df: pd.DataFrame) -> pd.DataFrame: Input dataframe, no specific columns. Returns: - The input dataframe with the `int64` type - columns downcasted. + The input dataframe with the `int64` type columns downcasted. """ - ints = df.select_dtypes(include=['int64']).columns.tolist() - df[ints] = df[ints].apply(pd.to_numeric, downcast='integer') + ints = df.select_dtypes(include=["int64"]).columns.tolist() + df[ints] = df[ints].apply(pd.to_numeric, downcast="integer") return df -def dict_merge(dct: Dict[Any, Any], merge_dct: Dict[Any, Any], add_keys=True) -> Dict[Any, Any]: +def dict_merge( + dct: Dict[Any, Any], merge_dct: Dict[Any, Any], add_keys=True +) -> Dict[Any, Any]: """Recursive dict merge. Inspired by dict.update(), instead of updating only top-level keys, dict_merge recurses down into dicts nested to an arbitrary depth, updating keys. The `merge_dct` is merged into @@ -331,20 +368,51 @@ def dict_merge(dct: Dict[Any, Any], merge_dct: Dict[Any, Any], add_keys=True) -> add_keys (bool): whether to add new keys Returns: - dict: updated dict + Updated dict. """ dct = dct.copy() if not add_keys: - merge_dct = {k: merge_dct[k] for k in set(dct).intersection(set(merge_dct))} + merge_dct = {k: merge_dct[k] + for k in set(dct).intersection(set(merge_dct))} for k, v in merge_dct.items(): if ( k in dct and isinstance(dct[k], dict) - and isinstance(merge_dct[k], collections.Mapping) + and isinstance(merge_dct[k], collections.abc.Mapping) ): dct[k] = dict_merge(dct[k], merge_dct[k], add_keys=add_keys) else: dct[k] = merge_dct[k] return dct + + +def timeStamped(fname, fmt="%Y-%m-%d-%H-%M-%S_{fname}"): + return datetime.now().strftime(fmt).format(fname=fname) + + +def calculate_n_partitions(df, n_cpu, partition_size_mb=100): + """ + This function will calculate how many partitions a dataframe should be + split into. + + Args: + df: The pandas dataframe to be partitionined. + n_cpu: The number of available CPUs. + partition_size: The optimal partition size in MB. + Returns: + The optimal number of partitions. + """ + mem_usage_mb = df.memory_usage(deep=True).sum() / 1e6 + n_partitions = int(np.ceil(mem_usage_mb / partition_size_mb)) + + # n_partitions should be >= n_cpu for optimal parallel processing + if n_partitions < n_cpu: + n_partitions = n_cpu + + partition_size_mb = int(np.ceil(mem_usage_mb / n_partitions)) + + logger.debug(f"Using {n_partitions} partions of {partition_size_mb}MB") + + return n_partitions diff --git a/vast_pipeline/utils/view.py b/vast_pipeline/utils/view.py index df6c6044f..ec25a26ed 100644 --- a/vast_pipeline/utils/view.py +++ b/vast_pipeline/utils/view.py @@ -1,6 +1,5 @@ """Functions and variables used in pipeline/views.py.""" -import logging from vast_pipeline.models import SkyRegion from typing import List, Dict, Optional, Any @@ -202,7 +201,7 @@ def generate_colsfields( be not searchable in the final table. Returns: - colsfields (list): List containing JSON object. + List containing the JSON object. """ colsfields = [] @@ -277,8 +276,7 @@ def get_skyregions_collection(run_id: Optional[int]=None) -> Dict[str, Any]: run_id (int, optional): Run ID to filter on if not None. Returns: - skyregions_collection (dict): Dictionary representing a JSON obejct - containing the sky regions. + Dictionary representing a JSON object containing the sky regions. """ skyregions = SkyRegion.objects.all() if run_id is not None: diff --git a/vast_pipeline/views.py b/vast_pipeline/views.py index 7c0da2c66..6245ed7dd 100644 --- a/vast_pipeline/views.py +++ b/vast_pipeline/views.py @@ -2,22 +2,21 @@ import os import json import logging +import matplotlib.pyplot as plt import traceback -import dask.dataframe as dd import dask.bag as db import pandas as pd -from typing import Dict, Any, Tuple, List +from typing import Dict, Any, Tuple, List, Optional from glob import glob from itertools import tee +from pathlib import Path from astropy.io import fits -from astropy.coordinates import SkyCoord, Angle, Longitude, Latitude +from astropy.coordinates import SkyCoord, Angle from astropy.nddata import Cutout2D from astropy.wcs import WCS from astropy.wcs.utils import proj_plane_pixel_scales -from astroquery.simbad import Simbad -from astroquery.ned import Ned from bokeh.embed import json_item @@ -26,13 +25,17 @@ from django.contrib.contenttypes.models import ContentType from django.db import transaction from django.db.models import F, Count, QuerySet -from django.http import FileResponse, Http404, HttpResponseRedirect +from django.http import ( + FileResponse, Http404, HttpResponseRedirect, JsonResponse, HttpResponse +) from django.shortcuts import render, redirect, get_object_or_404 +from django.template.loader import render_to_string from django.urls import reverse from django.utils.safestring import mark_safe from django_q.tasks import async_task +import requests from rest_framework import status import rest_framework.decorators from rest_framework.request import Request @@ -47,7 +50,7 @@ from django.contrib.postgres.aggregates.general import ArrayAgg from django.contrib.auth.decorators import login_required -from vast_pipeline.plots import plot_lightcurve +from vast_pipeline.plots import plot_lightcurve, plot_eta_v_bokeh from vast_pipeline.models import ( Comment, CommentableModel, Image, Measurement, Run, Source, SourceFav, ) @@ -57,32 +60,33 @@ SourceFavSerializer, SesameResultSerializer, CoordinateValidatorSerializer, ExternalSearchSerializer ) +from vast_pipeline.utils import external_query from vast_pipeline.utils.utils import deg2dms, deg2hms, parse_coord, equ2gal from vast_pipeline.utils.view import generate_colsfields, get_skyregions_collection from vast_pipeline.management.commands.initpiperun import initialise_run from vast_pipeline.forms import PipelineRunForm, CommentForm, TagWithCommentsForm from vast_pipeline.pipeline.config import PipelineConfig -from vast_pipeline.pipeline.main import Pipeline -from vast_pipeline.pipeline.utils import get_create_p_run +from vast_pipeline.image.utils import open_fits logger = logging.getLogger(__name__) def _process_comment_form_get_comments( - request, instance: CommentableModel + request: Request, instance: CommentableModel ) -> Tuple[CommentForm, "QuerySet[Comment]"]: - """Process the comment form and return the form and comment objects. If the `request` - method was POST, create a `Comment` object attached to `instance`. + """Process the comment form and return the form and comment objects. + If the `request` method was POST, create a `Comment` object attached to + `instance`. Args: request: Django HTTP request object. - instance (CommentableModel): Django object that is a subclass of `CommentableModel`. + instance: Django object that is a subclass of `CommentableModel`. This is the object the comment will be attached to. Returns: - Tuple[CommentForm, QuerySet[Comment]]: a new, unbound `CommentForm` instance; and - the `QuerySet` of `Comment` objects attached to `instance`. + A new, unbound `CommentForm` instance. + The `QuerySet` of `Comment` objects attached to `instance`. """ if request.method == "POST": comment_form = CommentForm(request.POST) @@ -112,15 +116,7 @@ def Login(request): @login_required def Home(request): - totals = {} - totals['nr_pruns'] = Run.objects.count() - totals['nr_imgs'] = Image.objects.count() - totals['nr_srcs'] = Source.objects.count() - totals['nr_meas'] = Measurement.objects.count() - context = { - 'totals': totals, - 'd3_celestial_skyregions': get_skyregions_collection(), 'static_url': settings.STATIC_URL } return render(request, 'index.html', context) @@ -248,22 +244,24 @@ def measurements(self, request, pk=None): return Response(serializer.data) @rest_framework.decorators.action(detail=True, methods=['post']) - def run(self, request, pk=None): + def run( + self, request: Request, pk: Optional[int] = None + ) -> HttpResponseRedirect: """ Launches a pipeline run using a Django Q cluster. Includes a check on ownership or admin stataus of the user to make sure processing is allowed. Args: - request (Request): Django REST Framework request object. - pk (int, optional): Run object primary key. Defaults to None. + request: Django REST Framework request object. + pk: Run object primary key. Defaults to None. Raises: Http404: if a Source with the given `pk` cannot be found. Returns: Response: Returns to the orignal request page (the pipeline run - detail). + detail). """ if not pk: messages.error( @@ -280,17 +278,9 @@ def run(self, request, pk=None): # make sure that only the run creator or an admin can request the run # to be processed. - if ( - p_run.user != request.user.get_username() - and not request.user.is_staff - ): - msg = ( - 'You do not have permission to process this pipeline run!' - ) - messages.error( - request, - msg - ) + if p_run.user != request.user and not request.user.is_staff: + msg = 'You do not have permission to process this pipeline run!' + messages.error(request, msg) return HttpResponseRedirect(request.META.get('HTTP_REFERER')) # check that it's not already running or queued @@ -352,6 +342,262 @@ def run(self, request, pk=None): reverse('vast_pipeline:run_detail', args=[p_run.id]) ) + @rest_framework.decorators.action(detail=True, methods=['post']) + def restore( + self, request: Request, pk: Optional[int] = None + ) -> HttpResponseRedirect: + """ + Launches a restore pipeline run using a Django Q cluster. Includes a + check on ownership or admin status of the user to make sure + processing is allowed. + + Args: + request: Django REST Framework request object. + pk: Run object primary key. Defaults to None. + + Raises: + Http404: if a Source with the given `pk` cannot be found. + + Returns: + Response: Returns to the orignal request page (the pipeline run + detail). + """ + if not pk: + messages.error( + request, + 'Error in config write: Run pk parameter null or not passed' + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + try: + p_run = get_object_or_404(self.queryset, pk=pk) + except Exception as e: + messages.error(request, f'Error in run fetch: {e}') + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + # make sure that only the run creator or an admin can request the run + # to be processed. + if p_run.user != request.user and not request.user.is_staff: + msg = 'You do not have permission to process this pipeline run!' + messages.error(request, msg) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + # check that it's not already running or queued + if p_run.status in ["RUN", "QUE", "RES", "INI"]: + msg = ( + f'{p_run.name} is already running, queued, restoring or is ' + 'only initialised. It cannot be restored at this time.' + ) + messages.error( + request, + msg + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + if Run.objects.check_max_runs(settings.MAX_PIPELINE_RUNS): + msg = ( + 'The maximum number of simultaneous pipeline runs has been' + f' reached ({settings.MAX_PIPELINE_RUNS})! Please try again' + ' when other jobs have finished.' + ) + messages.error( + request, + msg + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + prev_status = p_run.status + try: + debug_flag = 3 if request.POST.get('restoreDebug', None) else 1 + + async_task( + 'django.core.management.call_command', + 'restorepiperun', + p_run.path, + no_confirm=True, + verbosity=debug_flag + ) + + msg = mark_safe( + f'Restore {p_run.name} successfully sent to the queue!

    Refresh the' + ' page to check the status.' + ) + messages.success( + request, + msg + ) + except Exception as e: + with transaction.atomic(): + p_run.status = 'ERR' + p_run.save() + messages.error(request, f'Error in restoring run: {e}') + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + return HttpResponseRedirect( + reverse('vast_pipeline:run_detail', args=[p_run.id]) + ) + + @rest_framework.decorators.action(detail=True, methods=['post']) + def delete( + self, request: Request, pk: Optional[int] = None + ) -> HttpResponseRedirect: + """ + Launches the remove pipeline run using a Django Q cluster. Includes a + check on ownership or admin status of the user to make sure + deletion is allowed. + + Args: + request (Request): Django REST Framework request object. + pk (int, optional): Run object primary key. Defaults to None. + + Raises: + Http404: if a Source with the given `pk` cannot be found. + + Returns: + Response: Returns to the run index page (list of pipeline runs). + """ + if not pk: + messages.error( + request, + 'Error in config write: Run pk parameter null or not passed' + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + try: + p_run = get_object_or_404(self.queryset, pk=pk) + except Exception as e: + messages.error(request, f'Error in run fetch: {e}') + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + # make sure that only the run creator or an admin can request the run + # to be processed. + if p_run.user != request.user and not request.user.is_staff: + msg = 'You do not have permission to process this pipeline run!' + messages.error(request, msg) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + # check that it's not already running or queued + if p_run.status in ["RUN", "QUE", "RES"]: + msg = ( + f'{p_run.name} is already running, queued or restoring. ' + 'It cannot be deleted at this time.' + ) + messages.error( + request, + msg + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + try: + async_task( + 'django.core.management.call_command', + 'clearpiperun', + p_run.path, + remove_all=True, + ) + + msg = mark_safe( + f'Delete {p_run.name} successfully requested!

    ' + ' Refresh the Pipeline Runs page for the deletion to take effect.' + ) + messages.success( + request, + msg + ) + except Exception as e: + with transaction.atomic(): + p_run.status = 'ERR' + p_run.save() + messages.error(request, f'Error in deleting run: {e}') + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + return HttpResponseRedirect( + reverse('vast_pipeline:run_index') + ) + + @rest_framework.decorators.action(detail=True, methods=['post']) + def genarrow( + self, request: Request, pk: Optional[int] = None + ) ->HttpResponseRedirect: + """ + Launches the create arrow files process for a pipeline run using + a Django Q cluster. Includes a check on ownership or admin status of + the user to make sure the creation is allowed. + + Args: + request: Django REST Framework request object. + pk: Run object primary key. Defaults to None. + + Raises: + Http404: if a Source with the given `pk` cannot be found. + + Returns: + Response: Returns to the orignal request page (the pipeline run + detail). + """ + if not pk: + messages.error( + request, + 'Error in config write: Run pk parameter null or not passed' + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + try: + p_run = get_object_or_404(self.queryset, pk=pk) + except Exception as e: + messages.error(request, f'Error in run fetch: {e}') + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + # make sure that only the run creator or an admin can request the run + # to be processed. + if p_run.user != request.user and not request.user.is_staff: + msg = 'You do not have permission to process this pipeline run!' + messages.error(request, msg) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + # check that it's not already running or queued + if p_run.status != "END": + msg = ( + f'{p_run.name} has not completed successfully.' + ' The arrow files can only be generated after the run is' + ' successful.' + ) + messages.error( + request, + msg + ) + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + try: + overwrite_flag = True if request.POST.get('arrowOverwrite', None) else False + + async_task( + 'django.core.management.call_command', + 'createmeasarrow', + p_run.path, + overwrite=overwrite_flag, + verbosity=3 + ) + + msg = mark_safe( + f'Generate the arrow files for {p_run.name} successfully requested!

    ' + ' Refresh the page and check the generate arrow log output for the status of the process.' + ) + messages.success( + request, + msg + ) + except Exception as e: + with transaction.atomic(): + p_run.status = 'ERR' + p_run.save() + messages.error(request, f'Error in deleting run: {e}') + return HttpResponseRedirect(request.META.get('HTTP_REFERER')) + + return HttpResponseRedirect( + reverse('vast_pipeline:run_detail', args=[p_run.id]) + ) + # Run detail @login_required @@ -366,17 +612,11 @@ def RunDetail(request, id): p_run['nr_meas'] = p_run['n_selavy_measurements'] p_run['nr_frcd'] = p_run['n_forced_measurements'] p_run['nr_srcs'] = p_run['n_sources'] + p_run['new_srcs'] = p_run['n_new_sources'] else: p_run['nr_meas'] = 'N/A' p_run['nr_frcd'] = 'N/A' p_run['nr_srcs'] = 'N/A' - - if p_run_model.status == 'Completed': - p_run['new_srcs'] = Source.objects.filter( - run__id=p_run['id'], - new=True, - ).count() - else: p_run['new_srcs'] = 'N/A' # read run config @@ -385,11 +625,29 @@ def RunDetail(request, id): with open(f_path) as fp: p_run['config_txt'] = fp.read() - # read run log file - f_path = os.path.join(p_run['path'], 'log.txt') + # read prev run config + f_path = os.path.join(p_run['path'], 'config.yaml.bak') if os.path.exists(f_path): with open(f_path) as fp: - p_run['log_txt'] = fp.read() + p_run['prev_config_txt'] = fp.read() + + log_files = sorted(glob(os.path.join(p_run['path'], '*[0-9]_log.txt'))) + log_files = [os.path.basename(i) for i in log_files[::-1]] + + restore_log_files = sorted( + glob(os.path.join(p_run['path'], '*[0-9]_restore_log.txt')) + ) + restore_log_files = [os.path.basename(i) for i in restore_log_files[::-1]] + + genarrow_log_files = sorted( + glob(os.path.join(p_run['path'], '*[0-9]_gen_arrow_log.txt')) + ) + genarrow_log_files = [os.path.basename(i) for i in genarrow_log_files[::-1]] + + # Detect whether arrow files are present + p_run['arrow_files'] = os.path.isfile( + os.path.join(p_run['path'], 'measurements.arrow') + ) image_fields = [ 'name', @@ -412,7 +670,7 @@ def RunDetail(request, id): ) image_datatable = { - 'table_id': 'imageTable', + 'table_id': 'dataTable', 'api': ( reverse('vast_pipeline:api_pipe_runs-images', args=[p_run['id']]) + '?format=datatables' @@ -435,68 +693,14 @@ def RunDetail(request, id): 'order': [1, 'asc'] } - meas_fields = [ - 'name', - 'ra', - 'ra_err', - 'uncertainty_ew', - 'dec', - 'dec_err', - 'uncertainty_ns', - 'flux_peak', - 'flux_peak_err', - 'flux_peak_isl_ratio', - 'flux_int', - 'flux_int_err', - 'flux_int_isl_ratio', - 'frequency', - 'compactness', - 'snr', - 'has_siblings', - 'forced' - ] - - meas_colsfields = generate_colsfields( - meas_fields, - {'name': reverse('vast_pipeline:measurement_detail', args=[1])[:-2]}, - not_searchable_col=['frequency'] - ) - - meas_datatable = { - 'table_id': 'measTable', - 'api': ( - reverse('vast_pipeline:api_pipe_runs-measurements', args=[p_run['id']]) + - '?format=datatables' - ), - 'colsFields': meas_colsfields, - 'colsNames': [ - 'Name', - 'RA (deg)', - 'RA Error (arcsec)', - 'Uncertainty EW (arcsec)', - 'Dec (deg)', - 'Dec Error (arcsec)', - 'Uncertainty NS (arcsec)', - 'Peak Flux (mJy/beam)', - 'Peak Flux Error (mJy/beam)', - 'Peak Flux Isl. Ratio', - 'Int. Flux (mJy)', - 'Int. Flux Error (mJy)', - 'Int. Flux Isl. Ratio', - 'Frequency (MHz)', - 'Compactness', - 'SNR', - 'Has siblings', - 'Forced Extraction' - ], - 'search': True, - } - context = { "p_run": p_run, - "datatables": [image_datatable, meas_datatable], + "datatables": [image_datatable], "d3_celestial_skyregions": get_skyregions_collection(run_id=id), "static_url": settings.STATIC_URL, + "log_files": log_files, + "restore_log_files": restore_log_files, + "genarrow_log_files": genarrow_log_files } context["comment_form"], context["comments"] = _process_comment_form_get_comments( @@ -1287,11 +1491,139 @@ def SourceQuery(request): 'New High Sigma' ], 'search': False, + 'deferLoading': 0, # don't fetch results until a query is made } } ) +# Source query eta V plot +@login_required +def SourceEtaVPlot(request: Request) -> Response: + """The view for the main eta-V plot page. + + Args: + request (Request): Django REST Framework request object. + + Returns: + The response for the main eta-V page. + """ + min_sources = 50 + + source_query_result_id_list = request.session.get("source_query_result_ids", []) + + sources_query_len = len(source_query_result_id_list) + + if sources_query_len < min_sources: + messages.error( + request, + ( + f'The query has returned only {sources_query_len} sources.' + f' A minimum of {min_sources} sources must be used to produce' + ' the plot.' + ) + ) + + plot_ok = 0 + + else: + sources = Source.objects.filter( + id__in=source_query_result_id_list, + n_meas__gt=1, + eta_peak__gt=0, + eta_int__gt=0, + v_peak__gt=0, + v_int__gt=0 + ) + + new_sources_ids_list = list(sources.values_list("id", flat=True)) + + new_sources_query_len = len(new_sources_ids_list) + + diff = sources_query_len - new_sources_query_len + + if diff > 0: + messages.warning( + request, + ( + f'Removed {diff} sources that either had' + ' only one datapoint, or, an \u03B7 or V value of 0.' + ' Change the query options to avoid these sources.' + ) + ) + + request.session["source_query_result_ids"] = new_sources_ids_list + + if new_sources_query_len < min_sources: + messages.error( + request, + ( + 'After filtering, the query has returned only' + f' {sources_query_len} sources. A minimum of {min_sources}' + ' sources must be used to produce the plot.' + ) + ) + + plot_ok = 0 + + else: + if new_sources_query_len > settings.ETA_V_DATASHADER_THRESHOLD: + messages.info( + request, + ( + "Sources outside of the selected sigma area" + " are displayed as a non-interactive averaged" + " distribution." + ) + ) + + plot_ok = 1 + + context = { + 'plot_ok': plot_ok, + } + + return render(request, 'sources_etav_plot.html', context) + + +@login_required +def SourceEtaVPlotUpdate(request: Request, pk: int) -> Response: + """The view to perform the update on the eta-V plot page. + + Args: + request (Request): Django REST Framework request object. + pk (int, optional): Source object primary key. Defaults to None. + + Raises: + Http404: if a Source with the given `pk` cannot be found. + + Returns: + The response for the update page. + """ + try: + source = Source.objects.values().get(pk=pk) + except Source.DoesNotExist: + raise Http404 + + source['wavg_ra_hms'] = deg2hms(source['wavg_ra'], hms_format=True) + source['wavg_dec_dms'] = deg2dms(source['wavg_dec'], dms_format=True) + source['wavg_l'], source['wavg_b'] = equ2gal(source['wavg_ra'], source['wavg_dec']) + + context = { + 'source': source, + 'sourcefav': ( + SourceFav.objects.filter( + user__id=request.user.id, + source__id=source['id'] + ) + .exists() + ), + 'datatables': [] + } + + return render(request, 'sources_etav_plot_update.html', context) + + # Source detail @login_required def SourceDetail(request, pk): @@ -1327,15 +1659,25 @@ def SourceDetail(request, pk): 'forced', 'image_id' ] - measurements = list( - Measurement.objects.filter(source__id=pk).annotate( - datetime=F('image__datetime'), - image_name=F('image__name'), - frequency=F('image__band__frequency'), - ).order_by('datetime').values(*tuple(cols)) + measurements_qs = ( + Measurement.objects.filter(source__id=pk) + .annotate(datetime=F("image__datetime"), image_name=F("image__name")) + .order_by("datetime") ) - first_det_meas_index = [i['forced'] for i in measurements].index(False) + measurements = list( + measurements_qs.annotate( + frequency=F("image__band__frequency"), + ).values(*tuple(cols)) + ) + # subset of measurements used for the cutouts + measurements_cutouts = list( + measurements_qs[: settings.MAX_CUTOUT_IMAGES].values( + "id", "ra", "dec", "image_id", "image_name", + ) + ) + # get the measurement for the first detection - used for the first detection cutout + first_det_meas = measurements[[i['forced'] for i in measurements].index(False)] for one_m in measurements: one_m['datetime'] = one_m['datetime'].isoformat() @@ -1448,8 +1790,9 @@ def SourceDetail(request, pk): 'source': source, 'source_next_id': source_next_id, 'source_previous_id': source_previous_id, - 'first_det_meas_index': first_det_meas_index, + 'first_det_meas': first_det_meas, 'datatables': [measurements, related_datatables], + 'cutout_measurements': measurements_cutouts, # flag to deactivate starring and render yellow star 'sourcefav': ( SourceFav.objects.filter( @@ -1516,9 +1859,16 @@ class ImageCutout(APIView): authentication_classes = [SessionAuthentication, BasicAuthentication] permission_classes = [IsAuthenticated] - def get(self, request, measurement_name, size="normal"): - measurement = Measurement.objects.get(name=measurement_name) - image_hdu: fits.PrimaryHDU = fits.open(measurement.image.path)[0] + def get(self, request, measurement_id: int, size: str = "normal"): + img_type = request.query_params.get('img_type', 'fits') + if img_type not in ('fits', 'png'): + raise Http404( + "GET query param img_type must be either 'fits' or 'png'." + ) + + measurement = Measurement.objects.get(id=measurement_id) + + image_hdu: fits.PrimaryHDU = open_fits(measurement.image.path)[0] coord = SkyCoord(ra=measurement.ra, dec=measurement.dec, unit="deg") sizes = { "xlarge": "40arcmin", @@ -1527,9 +1877,9 @@ def get(self, request, measurement_name, size="normal"): } filenames = { - "xlarge": f"{measurement.name}_cutout_xlarge.fits", - "large": f"{measurement.name}_cutout_large.fits", - "normal": f"{measurement.name}_cutout.fits", + "xlarge": f"{measurement.name}_cutout_xlarge.{img_type}", + "large": f"{measurement.name}_cutout_large.fits.{img_type}", + "normal": f"{measurement.name}_cutout.fits.{img_type}", } try: @@ -1558,7 +1908,11 @@ def get(self, request, measurement_name, size="normal"): cutout_hdu = fits.PrimaryHDU(data=cutout.data, header=cutout_header) cutout_file = io.BytesIO() - cutout_hdu.writeto(cutout_file) + + if img_type == "fits": + cutout_hdu.writeto(cutout_file) + else: + plt.imsave(cutout_file, cutout.data, dpi=600) cutout_file.seek(0) response = FileResponse( cutout_file, @@ -1574,7 +1928,7 @@ class MeasurementQuery(APIView): def get( self, - request, + request: Request, ra_deg: float, dec_deg: float, image_id: int, @@ -1717,8 +2071,15 @@ def list(self, request): )) # add home directory user data for user and jupyter-user (user = github name) req_user = request.user.username - for user in [f'~{req_user}', f'~jupyter-{req_user}']: - user_home_data = os.path.join(os.path.expanduser(user), settings.HOME_DATA_DIR) + for user in [f'{req_user}', f'jupyter-{req_user}']: + if settings.HOME_DATA_ROOT is not None: + user_home_data = os.path.join( + settings.HOME_DATA_ROOT, user, settings.HOME_DATA_DIR + ) + else: + user_home_data = os.path.join( + os.path.expanduser(f'~{user}'), settings.HOME_DATA_DIR + ) if settings.HOME_DATA_DIR and os.path.exists(user_home_data): img_regex_list.append(os.path.join(user_home_data, '**' + os.sep + '*.fits')) selavy_regex_list.append(os.path.join(user_home_data, '**' + os.sep + '*.txt')) @@ -1856,6 +2217,84 @@ def write(self, request, pk=None): ) +class RunLogSet(ViewSet): + authentication_classes = [SessionAuthentication, BasicAuthentication] + permission_classes = [IsAuthenticated] + queryset = Run.objects.all() + + @rest_framework.decorators.action(detail=True, methods=['get']) + def fetch(self, request, pk=None): + if not pk: + return Response( + { + 'message': { + 'severity': 'danger', + 'text': [ + 'Error in run log fetch request:', + 'Run pk parameter null or not passed' + ] + } + }, + status=status.HTTP_400_BAD_REQUEST + ) + + logname = self.request.query_params.get('logname', None) + + if not logname: + return Response( + { + 'message': { + 'severity': 'danger', + 'text': [ + 'Error in run log fetch request:', + 'logname url parameter null or not passed' + ] + } + }, + status=status.HTTP_400_BAD_REQUEST + ) + p_run = get_object_or_404(self.queryset, pk=pk) + logpath = Path(p_run.path) / logname + + if not logpath.exists(): + return Response( + { + 'message': { + 'severity': 'danger', + 'text': [ + 'Error in run log fetch request:', + f'Path {logpath} does not exist' + ] + } + }, + status=status.HTTP_400_BAD_REQUEST + ) + + try: + pipeline_log = logpath.read_text() + except Exception as e: + trace = traceback.format_exc().splitlines() + trace = '\n'.join(trace[-4:]) + msg = { + 'message': { + 'severity': 'danger', + 'text': ( + 'Error in run log fetch request\n' + f'{e}\n\n' + 'Debug trace:\n' + f'{trace}' + ).split('\n'), + } + } + return Response(msg, status=status.HTTP_400_BAD_REQUEST) + + logfile = { + 'log_html_content': ""+ pipeline_log + "" + } + + return JsonResponse(logfile, status=status.HTTP_200_OK) + + class SourceFavViewSet(ModelViewSet): authentication_classes = [SessionAuthentication, BasicAuthentication] permission_classes = [IsAuthenticated] @@ -1878,8 +2317,12 @@ def create(self, request): # return Response(serializer.data, status=status.HTTP_201_CREATED) # return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) data = request.data.dict() + if 'next' in data.keys(): + data.pop('next') data.pop('csrfmiddlewaretoken') data['user_id'] = request.user.id + + return_data = {} try: check = ( SourceFav.objects.filter( @@ -1890,17 +2333,30 @@ def create(self, request): ) if check: messages.error(request, 'Source already added to favourites!') + success = False else: fav = SourceFav(**data) fav.save() messages.success(request, 'Added to favourites successfully') + success = True except Exception as e: messages.error( request, f'Errors in adding source to favourites: \n{e}' ) + success = False + + return_data['success'] = success + return_data['messages'] = render_to_string( + 'messages.html', + {}, + request + ) - return HttpResponseRedirect(reverse('vast_pipeline:source_detail', args=[data['source_id']])) + return HttpResponse( + json.dumps(return_data, ensure_ascii=False), + content_type="application/json" + ) def destroy(self, request, pk=None): try: @@ -1962,6 +2418,21 @@ class UtilitiesSet(ViewSet): authentication_classes = [SessionAuthentication, BasicAuthentication] permission_classes = [IsAuthenticated] + @staticmethod + def _external_search_error_handler( + external_query_func, + coord: SkyCoord, + radius: Angle, + service_name: str, + request: Request, + ) -> List[Dict[str, Any]]: + try: + results = external_query_func(coord, radius) + except requests.HTTPError: + messages.error(request, f"Unable to get {service_name} query results.") + results = [] + return results + @rest_framework.decorators.action(methods=['get'], detail=False) def sesame_search(self, request: Request) -> Response: """Query the Sesame name resolver service and return a coordinate. @@ -1973,7 +2444,7 @@ def sesame_search(self, request: Request) -> Response: Defaults to "all". Returns: - Response: a Django REST framework Response. Will return JSON with status code: + A Django REST framework Response. Will return JSON with status code: - 400 if the query params fail validation (i.e. if an invalid Sesame service or no object name is provided) or if the name resolution fails. Error messages are returned as an array of strings under the relevant query @@ -2000,7 +2471,7 @@ def coordinate_validator(self, request: Request) -> Response: - frame (str): the frame for the given coordinate string e.g. icrs, galactic. Returns: - Response: a Django REST framework Response. Will return JSON with status code: + A Django REST framework Response. Will return JSON with status code: - 400 if the query params fail validation, i.e. if a frame unknown to Astropy is given, or the coordinate string fails to parse. Error messages are returned as an array of strings under the relevant query parameter key. @@ -2015,8 +2486,9 @@ def coordinate_validator(self, request: Request) -> Response: return Response() @rest_framework.decorators.action(methods=["get"], detail=False) - def simbad_ned_search(self, request: Request) -> Response: - """Perform a cone search with SIMBAD and NED and return the combined results. + def external_search(self, request: Request) -> Response: + """Perform a cone search with external providers (e.g. SIMBAD, NED, TNS) and + return the combined results. Args: request (Request): Django REST Framework Request object get GET parameters: @@ -2031,7 +2503,7 @@ def simbad_ned_search(self, request: Request) -> Response: `astropy.coordinates.Angle`, respectively. Returns: - Response: a Django REST framework Response containing result records as a list + A Django REST framework Response containing result records as a list under the data object key. Each record contains the properties: - object_name: the name of the astronomical object. - database: the source of the result, e.g. SIMBAD or NED. @@ -2055,75 +2527,18 @@ def simbad_ned_search(self, request: Request) -> Response: except ValueError as e: raise serializers.ValidationError({"radius": str(e.args[0])}) - # SIMBAD cone search - CustomSimbad = Simbad() - CustomSimbad.add_votable_fields( - "distance_result", "otype(S)", "otype(V)", "otypes", + simbad_results = self._external_search_error_handler( + external_query.simbad, coord, radius, "SIMBAD", request ) - simbad_result_table = CustomSimbad.query_region(coord, radius=radius) - if simbad_result_table is None: - simbad_results_dict_list = [] - else: - simbad_results_df = simbad_result_table[ - ["MAIN_ID", "DISTANCE_RESULT", "OTYPE_S", "OTYPE_V", "RA", "DEC"] - ].to_pandas() - simbad_results_df = simbad_results_df.rename( - columns={ - "MAIN_ID": "object_name", - "DISTANCE_RESULT": "separation_arcsec", - "OTYPE_S": "otype", - "OTYPE_V": "otype_long", - "RA": "ra_hms", - "DEC": "dec_dms", - } - ) - simbad_results_df["database"] = "SIMBAD" - # convert coordinates to RA (hms) Dec (dms) strings - simbad_results_df["ra_hms"] = Longitude( - simbad_results_df["ra_hms"], unit="hourangle" - ).to_string(unit="hourangle") - simbad_results_df["dec_dms"] = Latitude( - simbad_results_df["dec_dms"], unit="deg" - ).to_string(unit="deg") - simbad_results_dict_list = simbad_results_df.to_dict(orient="records") - - # NED cone search - ned_result_table = Ned.query_region(coord, radius=radius) - if ned_result_table is None or len(ned_result_table) == 0: - ned_results_dict_list = [] - else: - ned_results_df = ned_result_table[ - ["Object Name", "Separation", "Type", "RA", "DEC"] - ].to_pandas() - ned_results_df = ned_results_df.rename( - columns={ - "Object Name": "object_name", - "Separation": "separation_arcsec", - "Type": "otype", - "RA": "ra_hms", - "DEC": "dec_dms", - } - ) - ned_results_df["otype_long"] = "" # NED does not supply verbose object types - # convert NED result separation (arcmin) to arcsec - ned_results_df["separation_arcsec"] = ( - ned_results_df["separation_arcsec"] * 60 - ) - # convert coordinates to RA (hms) Dec (dms) strings - ned_results_df["ra_hms"] = Longitude( - ned_results_df["ra_hms"], unit="deg" - ).to_string(unit="hourangle") - ned_results_df["dec_dms"] = Latitude( - ned_results_df["dec_dms"], unit="deg" - ).to_string(unit="deg") - ned_results_df["database"] = "NED" - # convert dataframe to dict and replace float NaNs with None for JSON encoding - ned_results_dict_list = ned_results_df.sort_values( - "separation_arcsec" - ).to_dict(orient="records") - - results_dict_list = simbad_results_dict_list + ned_results_dict_list - serializer = ExternalSearchSerializer(data=results_dict_list, many=True) + ned_results = self._external_search_error_handler( + external_query.ned, coord, radius, "NED", request + ) + tns_results = self._external_search_error_handler( + external_query.tns, coord, radius, "TNS", request + ) + + results = simbad_results + ned_results + tns_results + serializer = ExternalSearchSerializer(data=results, many=True) serializer.is_valid(raise_exception=True) return Response(serializer.data) @@ -2155,3 +2570,32 @@ def lightcurve(self, request: Request, pk: int = None) -> Response: use_peak_flux = request.query_params.get("peak_flux", "true").lower() == "true" plot_document = plot_lightcurve(source, use_peak_flux=use_peak_flux) return Response(json_item(plot_document)) + + @rest_framework.decorators.action(methods=['get'], detail=False) + def etavplot(self, request: Request) -> Response: + """Create the eta-V plot. + + Args: + request (Request): Django REST Framework request object. + + Raises: + Http404: if no sources are found. + + Returns: + Response: Django REST Framework response object containing the Bokeh plot in + JSON format to be embedded in the HTML template. + """ + source_query_result_id_list = request.session.get("source_query_result_ids", []) + try: + source = Source.objects.filter(pk__in=source_query_result_id_list) + except Source.DoesNotExist: + raise Http404 + # TODO raster plots version for Slack posts + use_peak_flux = request.query_params.get("peak_flux", "true").lower() == "true" + + eta_sigma = float(request.query_params.get("eta_sigma", 3.0)) + v_sigma = float(request.query_params.get("v_sigma", 3.0)) + + plot_document = plot_eta_v_bokeh( + source, eta_sigma=eta_sigma, v_sigma=v_sigma, use_peak_flux=use_peak_flux) + return Response(json_item(plot_document)) diff --git a/webinterface/.env.template b/webinterface/.env.template index 8b874fa33..139181633 100644 --- a/webinterface/.env.template +++ b/webinterface/.env.template @@ -18,12 +18,24 @@ SOCIAL_AUTH_GITHUB_SECRET=fillMeUp SOCIAL_AUTH_GITHUB_ORG_NAME=fillMeUp SOCIAL_AUTH_GITHUB_ADMIN_TEAM=fillMeUp +# External APIs +# TNS_API_KEY= uncomment and fill to use +# TNS_USER_AGENT= uncomment and fill to use + # Pipeline PIPELINE_WORKING_DIR=pipeline-runs FLUX_DEFAULT_MIN_ERROR=0.001 POS_DEFAULT_MIN_ERROR=0.01 RAW_IMAGE_DIR=raw-images HOME_DATA_DIR=vast-pipeline-extra-data +# HOME_DATA_ROOT=Uncomment to set a custom path to user data dirs # PIPELINE_MAINTAINANCE_MESSAGE=Uncomment and fill to show MAX_PIPELINE_RUNS=3 MAX_PIPERUN_IMAGES=200 +MAX_CUTOUT_IMAGES=30 + +# Q_CLUSTER_TIMEOUT=86400 +# Q_CLUSTER_RETRY=86402 +# Q_CLUSTER_MAX_ATTEMPTS=1 + +ETA_V_DATASHADER_THRESHOLD=20000 diff --git a/webinterface/settings.py b/webinterface/settings.py index e095b9d7c..5e57e49a1 100644 --- a/webinterface/settings.py +++ b/webinterface/settings.py @@ -143,6 +143,9 @@ CRISPY_TEMPLATE_PACK = "bootstrap4" +TNS_API_KEY = env('TNS_API_KEY', default=None) +TNS_USER_AGENT = env('TNS_USER_AGENT', default=None) + # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases @@ -162,7 +165,7 @@ Q_CLUSTER = { 'name': 'VastPipeline', 'workers': 3, - 'timeout': 86400, + 'timeout': env('Q_CLUSTER_TIMEOUT', cast=int, default=86400), 'queue_limit': 6, 'ack_failures': True, 'bulk': 10, @@ -170,7 +173,8 @@ 'label': 'Django Q tasks', 'daemonize_workers': False, 'recycle': 100, - 'retry': 86402 + 'retry': env('Q_CLUSTER_RETRY', cast=int, default=86402), + 'max_attempts': env('Q_CLUSTER_MAX_ATTEMPTS', cast=int, default=1), } # REST framework settings @@ -283,7 +287,10 @@ if '/' not in RAW_IMAGE_DIR: RAW_IMAGE_DIR = os.path.join(BASE_DIR, RAW_IMAGE_DIR) -# extra user-supplied data folder, relative to the user's home directory on the deployment machine +# extra user-supplied data folder +# HOME_DATA_DIR is relative to HOME_DATA_ROOT if HOME_DATA_ROOT is not None +# otherwise, HOME_DATA_DIR is relative to the user's home directory on the deployment machine +HOME_DATA_ROOT = env('HOME_DATA_ROOT', default=None) HOME_DATA_DIR = env('HOME_DATA_DIR', cast=str, default='vast-pipeline-extra-data') # allowed source finders @@ -327,6 +334,7 @@ 'selavy_local_rms_zero_fill_value': 0.2, 'create_measurements_arrow_files': False, 'suppress_astropy_warnings': True, + 'pair_metrics': True, 'source_aggregate_pair_metrics_min_abs_vs': 4.3, } @@ -336,5 +344,11 @@ # maximum number of images for non-admin runs MAX_PIPERUN_IMAGES = env('MAX_PIPERUN_IMAGES', cast=int, default=200) +# maximum number of cutout images to render on the source detail page +MAX_CUTOUT_IMAGES = env('MAX_CUTOUT_IMAGES', cast=int, default=30) + # pipeline maintainance message/banner PIPELINE_MAINTAINANCE_MESSAGE = env('PIPELINE_MAINTAINANCE_MESSAGE', cast=str, default=None) + +# web server eta v plot datashader threshold +ETA_V_DATASHADER_THRESHOLD = env('ETA_V_DATASHADER_THRESHOLD', cast=int, default=20000)